Market Trends Report

MARket tReNdS RePORt
big bang coming soon
FEBRUARY 2016

5
sommaire
CORE TECHNO TRENDS, BUSINESS MODEL AND SOCIAL CHANGES
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P. 7
P. 19
P. 25
P. 41
DISRUPTED INDUSTRIES, CHANGING DAILY LIFE AND BUSINESS
FINTECH DISRUPT (AND PARTNER WITH), BANKING & INSURANCE
B2B TRADE: ALSO RIPE FOR DISRUPTIVE INNOVATIONS

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The largest industry in the world, by far, is on the verge of disruption.
Financial Institutions were convinced that they were too big, too complex
and too regulated to be disrupted. But this was in the world of before.
Any industry is in the end dependent on one single thing: customers.
After the 2008 financial crisis, and re-enforced by the outbreak of on-going financial
scandals, the relationship between financial institutions and clients has been broken,
and there is no way back. Customers discovered that they could not blindly rely on
financial institutions anymore and that they needed alternatives and back-ups.
First, banks ignored us, thinking that some pirates coming from Silicon Valley
had no clue about finance and were going to fail. Then, when some clients
started switching to Fintech companies offering transparency, fair prices
and great user experience, they laughed at us. Then, when we started
dramatically eroding their revenue base and profits, they started fighting us
and tried to damage our credibility, arguing that we were small, unreliable and,
in some cases, risky to be in business with. An interesting argument coming from
financial institutions that were massively bailed out with public money during the
financial crisis. Then, Fintech won.
When I say Fintech won, it does not mean that we now have a leading market share
or that clients have already entirely adopted us. I say we won for three main reasons.
The first one is that we have proven that financial services outside of banks are a true
alternative for consumers and businesses of any size.
The second one is that most financial institutions have now understood, at least
among top management, that they will have to dramatically evolve in the coming
years, go digital, close branches, be customer-friendly and develop a culture of on-
going customer-centric innovation.
Thethirdreason,whichisaconsequenceofthefirsttwo,isthataco-petitiveeco-system
is now being shaped. On one hand, financial institutions have understood that they
can get a lot of value by collaborating, partnering with, investing in or acquiring
Fintech companies. This is a way for them to develop this disruptive culture based
on technology, innovation, out-of-the-box thinking and customer experience. On
the other side, Fintech companies have accepted that the financial industry is very
complex and that they can not rebuild everything from scratch in the short or mid
term. They need banks to get access to financial markets and infrastructures.
The opportunities coming from the emergence of Fintech companies and the
development of a co-petitive eco-system with financial institutions are limitless. We
will see disruptive innovation spreading out for many years to come. As always, those
keen to take risks, break with tradition and move fast will win.
foreword by philippe gelis
ceo kantox

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The purpose of this first edition of the Market Trends Report is to shed light on the way digital technologies reshape trade finance, a sector
which often does not get as much publicity as B2C financial services.
Given that disruption often comes from adjacent sectors or from the application of an existing technology to a new field, we found it essential
to begin with a broad analysis of the latest trends before zooming in progressively on financial services and on trade finance specifically.
The report is structured around four chapters, starting from the general core techno trends, and converging towards the changes impacting the
trade finance ecosystem:
1- Core techno trends, business model and social changes
2- Disrupted industries, changes in the way we live and work
3- FinTech disrupt (and partner with) banking and insurance
4- Conclusion: Trade Finance is also ripe for disruptive innovations
We really hope that you will like this Market Trends Report and that you will find it useful. When you read it, please keep in mind that it is still
being refined. We welcome your feedbacks, insights and suggestions.
Looking forward to hearing back from you,
Louis & Christophe
Euler Hermes Digital Agency

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CORE TECHNO TRENDS, BUSINESS MODEL
AND SOCIAL CHANGES
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Core techno trends, business
model and social changes
1.1.1 Cloud
The main benefit of cloud computing technologies is that they allow on-demand
access to virtually infinite computing power. Cloud computing operates through a
network of remote servers hosted on the Internet to store, manage and process data,
rather than local servers. Behind the success of cloud computing lie a number of key
underlying technologies such as Docker, a container technology allowing programs
to run on any platform.
The leader in cloud computing is Amazon with its AWS offering (Amazon Web
Services) which has become a substantial source of revenue aside the original
e-commerce business with more than $2.4 billion in turnover for Q4 2015. Other large
providers include Google, Citrix, IBM, Microsoft or Salesforce.
Why is it called the Cloud? Wikipedia brings the following answer:
Cloud computing metaphor: For a user, the network elements representing the
provider-rendered services are invisible, as if obscured by a cloud.
As resources are shared and only usage needs to be remunerated, as opposed to
ownership, the cost of using cloud computing is often lower than the cost of owning
a local solution. Cloud computing makes it possible for fast growth startups to scale
their infrastructure at high speed and low cost.
From a user perspective, Cloud computing is also an effective way to “variabilize” the
cost base (e.g. by removing the need to invest in servers from Day 1) while gaining
flexibility (e.g. easy to scale down if one doesn’t need as much capacity as initially
planned) and decreasing overall spend. It is also a way to increase the accessibility of
services and to curb maintenance costs. To take a concrete example, if you decide to
use Google For Work to manage your company’s email, you no longer have to worry
about local software updates.
The obvious drawback of cloud computing is the perceived lack of control and
ownership by users, especially when sensitive data is at stake. Even though cloud-
based services are on average more reliable and secure than in-house applications,
many corporates and regulated industries face reputational and operational risks
when resorting to the cloud. To mitigate those risks, large companies often set up
their own “private” clouds, or even hybrid clouds bundling two or more clouds (private,
public and/or community) from different service providers.
The “Cloud” model can be applied to an increasing number of domains:
• Infrastructure (Infrastructure as a Service as known as IaaS): for computing
power, storage space, network
• Development environment (Platforms as a Service also known as PaaS): for
operating system, programming language, database, webserver
• Software (Software as a Service as known as SaaS): for email (e.g. Gmail), CRM,
accounting, virtual desktop
1
1.1 technologies

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1.1.2 apis
API stands for Application Programming Interface. It is a standardized way to make
two programs communicate with each other. APIs have been widely adopted in
recent years and have tremendously helped the rapid emergence of innovations: in a
way, APIs make programs similar to Lego bricks, allowing them to be easily combined
and assembled to build bigger things.
Nowadays, in the ecosystem economy, corporate offerings usually require to
exchange digital information with partners and customers in an efficient and secure
way. This requires APIs. If a company builds poor APIs, it deteriorates the value of its
offering.
In terms of business model, many disruptors have an API at the core of their
proposition. For some of them, the API is even the sole offering. For instance, take
PayPal: the core of their proposition to online merchants is an API allowing them to
build their payment interface. Similarly, startups like Uber did not have to build their
entire platform: instead, they used a number of standalone services (Google Maps,
emailing service, payment gateway…) that they assembled by connecting their APIs.
For more about APIs, and how their are reshaping business, read here:
http://www.slideshare.net/faberNovel/6-reasons-why-apis-are-reshaping-your-
business
1.1.3 high speed connectivity everywhere
The ubiquity of high speed connectivity at an affordable cost is instrumental to
today’s technology revolution, and comes along with the notion of application stores
and mobile ecosystems. High speed connectivity is a key enabler to support a fluid
user experience on data-heavy services like Facebook, YouTube, Spotify or recently
Periscope. This in turn catalyzed significant revenues from mobile advertising, which
has fueled the race for even more compelling mobile apps.
Large bandwidth is achieved both on mobile and fixed connections. Today’s
smartphones embark an already impressive computing power – but nothing in
comparison with what is available by leveraging high speed connectivity to tap into
the cloud where the most sophisticated, power hungry applications can be hosted.
Moreover, the combination of high speed connectivity with high processing power
and high storage capacity equips every user with the ability and autonomy to develop
new applications and implement ideas in a way which was not even thinkable twenty
years ago. In many ways, new technologies are devolving tremendous power to the
people – the “Internet Democracy” – as well as generating new constraints on data
privacy and information overload.
Did you know? Internet.org is bringing high-speed connectivity in developing
countries.
At the same time, Facebook is also developing its own network of drones to
bring internet connectivity to remote parts of the planet.
APIs are way more than a technical topic.
They are also a core business capability.

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1.1.4 iot
IoT stands for Internet of Things, and refers to the fact that many objects are now
connected to the Internet, operating as a wide “network of things”: light bulbs (e.g.
adjusting luminosity to occupational sensors), trucks (Vnomics), thermostats
(Nest), components of a car (speedometer, accelerometer, entertainment system...),
wearables, industrial sensors used for machine maintenance, medical sensors used
for pathology detection, the list goes on.
By connecting those objects together, existing services can be fully reinvented with
a different business model leveraging the data that becomes available. For instance,
take motor insurance, onboard telematics have made it possible to develop PAYD
(pay-as-you-drive) policies which create powerful incentives to drive safely while
feeding the insurer with a whole raft of new data.
Behind the IoT wave is a recently created industry allowing to design and manufacture
connected objects very cheaply. A device with a Wifi connection, a thermometer and
a handful of other sensors can be manufactured for less than 1 USD. This unleashes
the creativity of designers around the world and we’re just at the beginning of a cycle
which will see many new connected products being crafted and commercialized. A
recently created startup (21inc) even came up with the concept of a bitcoin mining
chip for IoT objects, allowing things to allocate part of the energy they consume to
generate revenue by supporting the general transaction recording process taking
place in the bitcoin network.
In 2003, the number of connected devices stood at 0.5BN. At the end of 2015,
it is estimated to be close to 5BN – a large portion of which consists of mobile
phones and tablets. It is estimated that connected “things” will be in excess of
40BN by 2020, generating a real data explosion which can only be dealt with
using Big Data methodologies.

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1.1.5 big data and artificial intelligence
The conjunction of the aforementioned trends has also opened up new horizons for
data analytics and insights production. In particular, cloud computing has pushed
to a whole new level (actually exponentially, if you remember Moore’s law) our
ability to process data, both in terms of raw computing power and new algorithmic
approaches, whilst IoT and social media generate day after day an ever increasing
amount of available data to analyze. As a yardstick of this data explosion, the amount
of data recorded in two days in 2010 was equivalent to the entire data produced by
mankind since the beginning of times. Another way of looking at it is to observe that
more than 90% of the today's data was produced over the past two years.
Parallel to the exponential increase in available data, there has been significant
progress in certain domains within Artificial Intelligence (AI), such as Deep Learning,
which allows one to make the most of the data at hand. Deep Learning is a way to
teach a program to automatically recognize certain patterns: for example, finding
if a picture contains someone’s head. This requires huge data sets (for example
of pictures) which can be stored in some clouds, and large computing power to
build the neural networks capable of undertaking the computing-heavy task of
crawling these big data sets to actually “learn” what a head looks like. Deep learning
approaches tend to be more effective in the long run than traditional statistics
whereby a phenomenon is modelled by fitting a certain distribution onto observed
occurrences. From an epistemological standpoint, the acquisition of new insights is
moving from a top-down, hypothesis-driven approach, to a bottom-up, brute force
approach.
At the same time as our ability to process new data improves, the amount of
data to which this ability can be applied is rapidly increasing. On the one hand,
user generated content is being generated every second on social medial (think of
Facebook, LinkedIn, Twitter, Instagram and the likes). One the other hand, as the IoT
kicks in, most of what we do becomes recorded somewhere, ready to be analyzed.
The nature of the data being processed is evolving too. In the old days, most of the data
that could be analyzed by computers was “structured” i.e. organized along the fields
of a database. With the increase in processing power and the rise of AI, insights can
be derived automatically from unstructured data sets (free text, pictures, videos...). As
a consequence, all the data coming from social networks, CCTV recordings, machine
sensors can be deciphered on a large scale.
Global social graphs are a typical application of the large volumes of data which
are emerging from the digital economy. It is well known that services like Facebook
monetize their data by allowing advertisers to address messages to users of the
platform. This requires an efficient profiling: in the case of Facebook and the likes,
it relies on the notion of a social graph. The social graph links each user with other
users, and each user with interests. It also maps every content created on the
platform, turning anything into a graph node and allowing gigantic algorithms to
intelligently crawl all this data to better “understand” users’ interests. With several
services reaching hundreds of millions active users (Facebook, WhatsApp, WeChat…),
the big social graphs are now truly global.
A recent illustration of AI progress occurred in January 2016 when Google’s
DeepMind defeated a top human player at the game of go, which was
regarded as widely impossible for another decade.

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1.1.6 distributed computing / bitcoin
/ smart contracts
Bitcoin made the news as a new highly volatile currency which several regulators
attempted to control although it is by design distributed and therefore beyond
the current system of regulators, central banks and four-party schemes. It is still
considered to be in its infancy, with a technology that works but few people to really
understand it and its potential.
bitcoin price
Bitcoin is referred to as a crypto-currency because it uses cryptography to calculate
“proof of work” and to process transactions. It is both a payment system and a
currency. It leverages a “distributed ledger” architecture, whereby every node in the
network can actively contribute to transaction processing and recording: hence the
affiliation to distributed computing.
Because all transactions are recorded by the nodes, Bitcoin does not need a central
authority to be trusted or to operate the system, which solves trust issues in
transactions where one party does not know the other. The cost of transactions is
also relatively low cost compared with other payment systems as central overheads
are removed from the process.
However, distributed computing is much broader than Bitcoin, with numerous
promising opportunities outside currencies. The technology which underpins the
Bitcoin is often referred to as blockchain since it relies on a distributed database that
maintains a continuously growing list of data records in the various nodes. It should
be noted that blockchains can be public, semi-public or even private, by creating a
system where access permissions are tightly controlled, while still maintaining the
guarantees of authenticity and decentralization that blockchains typically provide.
Private blockchains will facilitate the adoption of the technology by incumbent banks
and most of them are indeed launching POCs in this area. It must be said, however,
that distributed computing is an old concept in computer science, but regained
significant attention recently as the combination with cryptographic concepts
unlocks numerous use cases, especially around the utilization of smart contracts.
A smart contract is an algorithmic script running on a blockchain, and can be used
in order, for example, to enforce a contract between two parties, by automating
the execution of the terms of an agreement when pre-determined events occur.
For those interested in how bitcoin works and in understanding the related
concepts (blockchain, public ledger, mining...), the page below will provide
a useful introduction:
https://bitcoin.org/en/how-it-works

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Smart contracts have potential applications in securities, payments, trade finance,
notarial and legal services. In any case, the common denominator behind smart
contracts is the removal of the need for a trusted third party – just like Bitcoin.
An example of such use cases is what Hyper Ledger offers to financial institutions:
http://www.digitalasset.com/#ecosystem
A meatier intro to blockchain and its applications to trade finance can be found here:
https://www.tradefinance.training/blog/articles/blockchain-implications-for-trade-
finance/
More about smart contracts here:
https://docs.erisindustries.com/explainers/smart_contracts/
Finally, as The Economist put it in a recent article:
The spread of blockchains is bad for anyone in the “trust business”
http://www.economist.com/news/leaders/21677198-technology-behind-bitcoin-
could-transform-how-economy-works-trust-machine
1.1.7 Conclusion: leveraging external
assets
Oneunderlyingthemebehindtheabovetechtrendsisaformofdecentralization(cloud,
distributed ledger, social media, IoT). By crowdsourcing ideas, data, infrastructure,
computing power, companies as well as individuals are able to find tremendous
leverage externally to accelerate their growth. To an extent, it could even be argued
that the ability to assemble external assets in an innovative way has become more
differentiating than the possession of a rare resource.
In the 21st century, corporations and other human groups will need to come to terms
with some disturbing paradigm shifts e.g. usage is more important than propriety,
collective intelligence matters more than expertise and collaboration (“co-opetition”)
yields more than outright competition. Ownership, expertise and competition only

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allow for incremental growth (a few %pts p.a.), whereas exponential development
comes from leveraging the power of a larger external community. Think of the
growth trajectories of some recent startups (Facebook, LinkedIn, Twitter, Waze)
which reached 100 million users in less than 10 years when it took 75 years for the
telephone to reach the same number of people – just to quote an example from the
“old” economy.
Understanding those paradigm shifts will be vital for incumbent players to reinvent
themselvesandmaintaintheirleadership.However,itislikelythatonlyafewwillmakeit
becausesuccessinadigitaleconomyrequiresasetofvalues(openness,engagement,
delegation, empathy) which often clashes with the realities of today’s corporate life.
1.2 disruption catalysts
1.2.1 excess of funding and silicon valley
effect
The excess of capital available globally through the venture capital (VC) industry is
supporting an extremely fast pace of innovation through startups creation. It has
never been as easy to raise significant amounts of money. This in turns has attracted
more wantrepreneurs than ever before, hoping to create the next unicorn. Given their
number, very few ideas / opportunities are left unexplored: there is always a VC ready
to bet on a good idea or a good team, hoping to sell it back a few months or years later
to an incumbent, or to IPO it.
The Silicon Valley illustrates these ecosystem dynamics very well, having pushed it
to an unprecedented level. Some say the success of the Valley is due to the unfair
advantage of unique talent concentration between San Francisco and San Jose, who
mix and share ideas very openly, in an unregulated environment. The talent itself is
more expensive than anywhere else in the world, and it adds speed to the Darwinian
nature of the ecosystem: succeed or fail fast.
Venture capitalists facilitate the rise and fall of startups, by having very open
conversations even among competitors: at the fringe of regulations, they help
each other to make their startups grow and to present the best possible bride to an
incumbent, unless of course the startup is so good it can live on its own and the VC
can exit with an IPO.
A few VCs stand out from the crowd, with an outstanding record of nurturing
successful startups. Some wantrepreneurs start caring more about what accelerator
programs (YCombinator, …) and what VCs (Khosla, GV, Sequoia …) other cofounders
have on their CV than their university diploma.
A good example of how existing leaders can build on the power of the
community was provided by Allstate, a leading insurance company in the
US. They took the gamble of opening their claims prediction algorithm
(highly sensitive asset, developed and fine-tuned over the years by their
dozens of actuaries) to the public to see if anyone could “beat” it. As you
may have guessed, their algorithm was matched and outperformed by the
public in a matter of weeks. And it was not even an actuary who found the
winning solution :-)

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1.2.2 cracked startup methodology
The Lean Startup is the ultimate user guide to achieving success when creating
a startup (or any project in highly uncertainty conditions, even in well established
companies). It was designed by Eric Ries and Steve Blank around 2010. Its principles
are now well understood by everyone in the startup
world, applied systematically, and still actively
studied and measured to make sure any possible
improvements are instantly shared with the entire
startup community.
The Lean Startup relies on (just) 5 principles:
• Entrepreneurs are everywhere
• Entrepreneurship is management
• Validated learning
• Innovation accounting
• Build-measure-learn
The last principle, Build-measure-learn, is probably
the best-known, for its association with the buzzword
“Pivot”. Pivoting rapidly is indeed a fundamental
aspect of driving towards success in conditions of
extreme uncertainty.
Beyond the Lean Startup, a significant number of methods, advices and lessons
learned from successful entrepreneurs, frameworks, are very openly available and
taught to help wantrepreneurs achieve success. For instance, user experience design
(“UX”) has seen tremendous progress of the past years and considerably improved
clients’ interaction with digital services and products. Agile development methods
have also been widely adopted by the digital newcomers.
This comes in addition to the numerous accelerators, incubators, and other
structures aiming at applying the best possible methods when creating a startup,
as well as opening up powerful networks of contacts, access to funding, and the
discipline required to focus on the right priorities at the right time.
1.2.3 better business models
Platforms are one of those “better” business models, highly scalable, protected by
their market dominance once scaled up, and creating value by building a market that
did not exist before. For years, platform business models were a dream achieved by
very few companies, given the difficulty of growing both the supply and the demand
sides. This is not really the case anymore: a number of players have cracked the
method to grow their platform ecosystem: in turn this has trained a generation of
growth hackers specialized in initiating and scaling specific ecosystems.
For instance, P2P (peer to peer) belongs to platform business models, and is a
powerful trend reshaping many industries: hospitality (Airbnb), transportation
(Blablacar), auctioning (eBay)…
Another particular type of platform business model is crowdsourcing: it allows to
solicit members of the platform for their help. Examples can be found in virtually
any domain, e.g. lending (Lending Club, KissKissBankBank, Zopa), capital raising
(Indigogo), advance ordering (Kickstarter), know-how (Quora, Datascience.net,
Kaggle), lifestyle (Trip Advisor), entertainment (Netflix, Spotify, Meetic, Tinder).
To learn more watch this excellent video from Gary Vaynerchuk, a well-
known investor and public figure in the Silicon Valley:
https://www.facebook.com/gary/videos/10153423320098350/

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1.2.4 speedy scalability through copycats
A number of players have taken the particular role of identifying the ideas that work
well in a particular market, and to copy them as rapidly as possible in other markets.
This usually paves the way for international expansion of successful (American)
startups, as they can acquire those local copycats whenever they enter a new market.
This contributed to accelerating the speed at which innovations are rolled out on a
global basis. And as result, a disruption happening successfully in any country around
the globe can become a global phenomenon in less than a year.
Some entrepreneurs and companies even specialize in establishing and scaling
up copycats. For instance, Rocket Internet started out as an innovation studio and
managed to industrialize the whole process of detecting and transferring proven
digital business models from one market to another. They now claim holdings in
more than 100 countries adding up to more than 30,000 employees.
1.3 Society and future of mankind
This section was kept intentionally short as it could generate endless debates taking
us pretty far from trade finance. This being said, the reason why it is critical to pause
and reflect on societal changes is that such changes affect customer values, beliefs
and behaviors. We will try to focus on those changes which are most certain and
require consideration when designing a customer journey, a product or our own
working environment.
1.3.1 Social changes
Recent studies have shown that digital natives (as known as the millennials) have a
different way of interacting with their environment. Some anecdotal changes include
better finger motricity (coming from constant typing and texting, observable on
electroencephalograms) as well as an increased ability to distinguish moving shapes
(probably linked to intense video gaming).
More fundamental changes are also taking place in the way digital natives relate to
them:
Memory outsourcing
As most knowledge is published online (Wikipedia) or can be found easily (Google),
the need to memorize vanishes accordingly. The whole trajectory of mankind
can actually be viewed as a gradual externalization of knowledge, from learning
everything by heart à la Socrates to hand-writing, from book printing to floppy discs,
from CD-ROMs to the cloud.
Trust in the community
Through the multiplication of communication channels (email, SMS, FB, LinkedIn,
Twitter, WhatsApp, WeChat, FaceTime, Slack...), and ubiquitous connectivity,
customers remain in touch with the world and their peers 24/7. When it comes

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to organizing external data or finding something, digitals refer to the community
more than on established experts. A good parallel is the way Google overtook other
search engines, meaning that the opinion of other users (bottom-up organization of
knowledge) has definitively overtaken the old pyramidal table of contents model.
In other words, in a context of constant overflow of information, digital natives place
much more trust in the opinion of their peers. Think how long it took for Wikipedia
to overtake Encyclopedia Britannica. Corporates have also been able to piggy-back
on this trend by articulating their business models around third-party assessments
(Trip Advisor) or by offering Next Best Actions (NBAs) derived from users’ behavior
such as Amazon’s “other users have also purchased the following items”.
Data privacy
As increasing amounts of data are published online and more trust is placed in the
community, digital natives are willing to share more data about themselves than
previous generations. In a network economy, this is a pretty rational behavior as
increased transparency creates a win-win situation for all participants. By doing
so, millennials have learned to manage the distinction between private life and
public information. This does not mean that the public/private delineation has been
abolished, quite the contrary in fact, but that millennials are very self-conscious
about what type of content is disclosed to the community. The concept of belonging
to a community has also been reinvented; community and privacy have shifted from
the family and immediate neighborhood to a group of (often online) peers sharing
the same interests, values and projects.
Short attention span
As a society where everything is only one click away, it has become unbearable
to follow a convoluted sales process. Also, in a society of constant information
overflow and pervasive advertising, the ability to focus has largely decreased,
leading to a form of “mediocrity”. Why think by oneself when the content is readily
accessible online? Why pay for quality content (newspaper, music, film) when you
can get access to apparently similar content for free? How often will you verify that
the information provided on a blog is valid?
As a consequence of shorter attention spans, an interesting development for UX
is the rise of message commerce i.e. customer interaction that takes place on
a chat platform such as Facebook Messenger, WhatsApp or We Chat, instead
of phone or email. Indeed, email interaction proves too cumbersome, slow and
choppy for customers compared with the fluidity of a chat. It also fits well with the
communication habits of millennials who chat more than they phone or email, and
value the ability to remain on their preferred communication tools.
Narcissism
As well as a preference for instant gratification, digital natives have arguably become
more self-centered. Facebook and LinkedIn are not only about socializing, they’re
also about reconstructing and projecting a dreamed image of the self. The number
of followers or the number of likes are the dominant KPI to assess one’s relevance in
an unforgiving uber-democracy. Ironically, the fallacy of web 2.0 empowerment leads
to a form of narcissism and consumerism which represents the culmination of an
evolution anticipated by Tocqueville, Aron or Orwell.
1.3.2 Organization of work
Digital technologies and artificial intelligence are leading to another wave of
automation similar to what took place during the first two industrial revolutions. The
difference is that it’s not only manual, repetitive, less qualified work which is being
displaced this time. Even white collar jobs are at stake.
For instance, traders in investment banks are very likely to (finish) disappear(ing) over
the coming years as algorithms monitor and dynamically adjust portfolio volatility. A
large “creative destruction” will be taking place over the coming years whereby entire
professions will disappear while new jobs will emerge – e.g. the risk managers in
charge of developing the trading algorithm which replaced the traders.
Another important trend is the move from top-down work organization to less
hierarchical systems. In fact, the more data we exchange, the more entropy increases

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in the system. Large, rigid, siloed organizations are very effective to deal with codified,
repetitive tasks, but they cannot cope with entropy and struggle to innovate at a fast
pace. If we extend Weber’s categorization of human groupings, we’re moving from
bureaucracy to “holacracy” where small, agile networks outpace large corporates.
Looking back on the past century and the gradual rise of information economy, an
interesting analogy can be found in the way decentralized systems have outlived
centralized ones (e.g. capitalism over planned economy).
Several implications of this trend can be witnessed in the corporate world:
• Collaboration among employees, transversal teams and background diversity are
the basic requirements of a learning organization
• Employee empowerment, delegation and subsidiarity are necessary so that
decisions are made on the ground, where the quality of information is optimal
• Speed and adaptability are more worthy assets than planning. In an ever
changing environment, business plans and strategies tend to fall apart in a few
months. That’s why the ability to take onboard market feedback and change
course rapidly is often more conducive to success than the quality of the initial
plan
1.3.3 perspectives of mankind
In the same way as the private sector had to transform itself, new forms of political
governance might be needed in the public sphere. There is no denying that the
Internet deeply transforms socio-political practices. Messages no longer flow solely
from the few to the many. Nowadays, messages also flow from the many to the
many, with increased quantity, frequency and interactivity. It is not sure that current
political governance – still highly centralized at national level – is still adapted in the
face of recent technological, economic and social changes.
Networked social movements have been particularly active since 2010, notably
in the Arab revolutions against dictatorships. Online and particularly wireless
communication has helped social movements pose more of a challenge to State
power. By disintermediating government and corporate control of communication,
horizontal social media have created a new pace of information which in turn has led
to a change in the practice of governmental power.
Other perspectives relate to the unsustainable impact of humanity on earth. Even
though predictions remain extremely difficult in this area, the combination of
genetics and digital technologies has already started to give rise to various forms
of “bio-hacking”, whereby entire organs can be replaced with new or improved limbs.
In this context, it is also pretty clear that life expectancy will make another jump –
some scientists even reckon that immortality could become accessible within a few
decades…
Another intriguing development is the rise of AI, which revives the science fiction
fear of a society dominated by robots. Is it a coincidence if people like Bill Gates,
Elon Musk or Nobel-prize winning scientists view AI as one of the most dangerous
technologies for the future of mankind? Indeed, war robots or deep data mining from
security agencies all rely on the same AI principles as other life enhancing inventions.

As Jim Marous, a banking and FinTech strategist, puts it:
Because of embedded sensors and AI, we are the last 'pure humans' ever.

20
DISRUPTED INDUSTRIES,
CHANGING DAILY LIFE AND BUSINESS
2

21
disrupted industries,
changes in the way we live and work
Digital is transforming commerce in several ways.
The first transformation comes from the general development of digital commerce:
it started with e-commerce, then marketplaces, then mobile commerce, and now
social commerce (within Facebook, WeChat, ...). Digital commerce now accounts
for a substantial proportion of overall commerce, in mature economies as well as in
developing ones.
For the last 15 years, online commerce was essentially a separate channel from
offline commerce (the high street shops, etc.), built and run separately, sometimes
even outsourced to an e-commerce specialist. Consumer behavior evolved, to reach
the point where it is today: brands integrating online and offline commerce to offer a
seamless experience perform better than those which have not adapted. This fuels
a deep transformation in the retail and commerce industry, where everyone is racing
after the most engaging multi-channel approaches.
The third transformative factor is the gigantic competition created by Amazon and
similar platforms. Amazon operates at such a large scale, with low real estate cost,
and with unprecedented operational efficiency across the entire value chain, that
it can afford extremely small margins, if not negative sometimes, compensated by
extreme margins on other lines of business of Amazon (like its cloud services).
2.2 Smart City
What is happening behind the stage is fairly simple: tech titans approach big cities
with the promise to manage them better than what public authorities have managed
so far. Some major fields of intelligent city activation are:
• Innovation economy
• Urban infrastructure
• Governance
Sidewalk Labs is an example such an initiative. Google created Sidewalks Labs
beginning June 2015, with former New York City Deputy Mayor Daniel Doctoroff at its
helm, reporting directly to Larry Page. The ambition is very bold:
“Making transportation more efficient and lowering the cost of living, reducing
energy usage and helping government operate more efficiently... Sidewalk Labs will
develop new products, platforms and partnerships to make progress in these areas.”
(https://plus.google.com/+LarryPage/posts/M1twDYHaui3 for more details)
2
2.1 Connected commerce / retail
Wikipedia defines a Smart City as one that uses digital technologies or
Information and Communication Technologies (ICT) to enhance quality
and performance of urban services, to reduce costs and resource
consumption, and to engage more effectively and actively with its citizens.
(https://en.wikipedia.org/wiki/Smart_city)

22
Google (or rather Alphabet, following their reorganization) is not the first one in this
field: Sprint, Cisco, Veniam, GE and many others already made big investments in the
concept of smart city.
On a more individual level, new technologies are also disrupting the role,
responsibilities, and needs of citizens within the city. An example is the relationship
to electricity production and storage, for households as well as businesses. Tesla
Powerwall (http://www.teslamotors.com/powerwall) has taken a strong position in
this emerging race. What struck the global innovator community was the clear vision
Elon Musk had when he introduced Powerwall, and the way he took the world by
surprise, although a project of this size can hardly be kept secret: https://youtu.be/
NvCIhn7_FXI. It shows that some of tomorrow’s industries may be built without most
of us even noticing – leaving incumbents running far behind.
2.3 connected house
Behind the Connected House is the dream of the Smart Home and the Intelligent
Living. And it is not science fiction anymore, although few players are well positioned
to capture this new industry.
Some very visible ambassadors of this Connected House industry include:
• Nest (https://nest.com/) acquired by Google
• Samsung with SmartThings (http://www.smartthings.com/)
• From a slightly different perspective, Amazon, with the Dash (https://fresh.
amazon.com/dash/) and the Echo (http://www.amazon.com/Amazon-SK705DI-
Echo/dp/B00X4WHP5E)
Some unexpected players are also appearing, sometimes betting their future on this
opportunity. This is the case, for example, of Technicolor, historically known for its
role in the film industry, and now building its future around the Connected Home
(http://www.technicolor.com/en/solutions-services/connected-home)
2.4 smart mobility
2.4.1 ride sharing
Platformization and big data have opened new perspectives to the transportation
industry, in the form of Ride Sharing services disrupting old industries and changing
habits.
Ride sharing has been an emblematic example of the 2.0 economy – to the point
where the word Uberization was coined to describe a certain form of disruption.
Uber (https://www.uber.com/) is indeed a good example of Smart mobility, creating a
platform where drivers meet riders, and as a whole leading to a better transportation

23
experience. Uber is now a company valued at more than USD50BN, making it one
of the biggest unicorns, ahead of Xiaomi and AirBnB. Uber is also often used as an
example of how the regular taxi industry failed to deliver an experience in line with
what new technologies enable.
Another aspect of Smart Mobility is illustrated by the well-known startup BlaBlaCar
(https://www.blablacar.com/), which focuses on long-distance ride-sharing. It is now
part of the unicorn club (and one of the only 3 French unicorns). At first, there seemed
to be no market: after all, BlaBlaCar was replacing the good old hitch hiking. The
company managed to make a market by creating a platform that connects a demand
and an offer that could only meet by chance before technology enabled to formalize
it.
Ride-sharing is not necessary limited to the well-known crowd-sourcing model.
For example, BMW is currently testing DriveNow with Sixt in Munich, and next in
Barcelona (https://youtu.be/r8BOh82Pwvo)
2.4.2 self-driving car
Deep learning explains the second big trend seen in Smart Mobility: the emergence of
self-driving cars. A fully self-driving car is still an RD concept for now, as technology
still faces a few obstacles before cars are really able to differentiate between a
normal person and a police officer, for example. Also the current driving regulation is
not meant for self-driving cars, and even the ethical questions raised by self-driving
vehicles are far from having obvious answers.
http://www.technologyreview.com/view/542626/why-self-driving-cars-must-be-
programmed-to-kill/
Yet some cars can self-drive in certain conditions. A few example include:
• George in his garage: http://www.bloomberg.com/features/2015-george-hotz-
self-driving-car/
• Google Self-driving Car: https://www.google.com/selfdrivingcar/
• Daimler Self-driving truck: http://www.slashgear.com/autonomy-on-the-
autobahn-daimler-tests-its-self-driving-truck-04407699/
• Tesla Autopilot: http://www.teslamotors.com/blog/your-autopilot-has-arrived.
Tesla Autopilot is the closest thing to a self-driving car on open roads today.
It does this using a combination of cameras, radar, ultrasonic sensors and
previously acquired data that has been uploaded from other Tesla vehicles.
A few other players are listed here : http://www.driverless-future.com/?page_id=155

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2.4.3 electric car
Theelectriccarindustryisnotnew,butstillinrelativeinfancy:forexamplemostelectric
vehicles in France are owned by the French post office (http://www.breezcar.com/
actualites/article/groupe-la-poste-plus-grande-flotte-voitures-electriques-0715). Yet
as most traditional car makers are moving relatively slowly in this field, this opens
opportunities for disruptors to change the face of the industry. For example:
• Tesla (http://www.teslamotors.com/) : premium cars, but with clear ambitions in
also building an electric car for the masses (Tesla Model 3)
• Toyota (http://www.toyota.com/esq/vehicles/electric-vehicle.html)
• BMW (http://www.bmw.com/com/en/insights/corporation/bmwi/360_electric.
html)
Electric cars are not just disrupting car manufacturers: they are fundamentally
changing the entire value chain as electric cars require lower maintenance.
The ecosystem for refueling also needs a complete rethink, with implications far
beyond the need of recharging electrical cars. Indeed, few solutions exist for a decent
experience when recharging an electrical vehicle, especially for car owners looking for
something as quick as refueling a car at a petrol station. Japan took the approach to
standardize electrical car batteries so that it became possible to have battery change
stations working for all car makers. It is unlikely that such a solution will be adopted
in the rest of the world. So instead of a discreet network of fuel stations like we have
today, an option is to embed electrical supply at most places where electrical cars
can be parked, which also requires a payment system to pay for the recharge. The
remaining step before intelligent car parks is very small, and it makes the Electric car
a very important milestone in the new paradigm, with Smart Cities and connected
objects.
Last but not least, business models where cars are owned neither by a person nor by
a company can be imagined, linking technologies derived from the blockchain and
the economics of electrical cars. If interested, read more here: http://www.economist.
com/news/briefing/21677228-technology-behind-bitcoin-lets-people-who-do-not-
know-or-trust-each-other-build-dependable.
2.5 ehealth
Bring new technologies and startup approaches to the health sector: this is the
essence of eHealth. The health industry has always been a big consumer and
partner of new technologies, but not the kind of those that are disrupting it right now.
Indeed, regulation and big profits have let the health industry live long with significant
inefficiencies at its core.
The diversity of players is immense. A good way to discover this field (although quite
US-centric) is the following Venture Scanner landscape:
eHealth also has connections with wellness disruptors such as Withings, FitBit and
so on: increasingly, these players get a seat in the discussions of countries’ healthcare
strategy. As a matter of fact, they bring the potential to make people more physically
active, at the scale of a country. It has the potential to reduce some healthcare

25
costs in the long term, if the population gets healthier. As eHealth is propelled at the
forefront of discussions of such scale and impact, this further attracts investments
into this emerging industry, further accelerating the disruption.
Last but not least, and linked to the new causes that motivate today’s leading
innovators, some entrepreneurs adopt the startup way to solve issues that were
previously the domain of big players only. Examples include Xavier Duportet (http://
eligo-bioscience.com/, intelligent antibiotics) and Romain Lacombe (https://www.
plumelabs.com/, artificial intelligence to solve cities’ pollution issues).

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FINTECH DISRUPT (AND PARTNER WITH),
BANKING INSURANCE
3

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fintech disrupt (and partner
with) banking and insurance
FinTech stands for Financial Technology and represents the category of startups and
innovators that leverage technology to bring innovations to the finance sector.
Traditional players have often grown following the “universal bank” model, offering
a full range of services in all possible domains (payments, lending, savings, …) and
targeting wide segments of customers. Unlike traditional players, FinTech players
typically focus on improving very specific niches within the value chain by leveraging
cutting-edge technology. Venture Scanner provides a good overview and examples
of FinTech players within each domain of finance: https://www.venturescanner.com/
sector_maps/financial-technology.pdf
FinTech leaves no banking or insurance domain untouched. Each startup alone is too
small to directly compete with the banking or insurance incumbent giants. However,
the already big number of FinTech startups, and the massive VC amounts ready to
be invested in this space, make it very similar to sand blasting to the incumbents: one
grain of sand does not have much impact, but many of them targeting a specific point
at high velocity cut right through any material.
In addition to pure FinTech players, other actors of the digital economy are starting to
make inroads into financial services. For instance, Google, Amazon and Alibaba are
all working on specific offerings which could very well become the biggest source of
disruption for incumbent players due to their scale and large installed customer bases.
To illustrate this, Uber became the biggest creator of bank accounts in the US, as its
drivers need one in order to receive their payment.
3

28
Most finance executives are well aware of the disruption, but the answer to the
underlying strategic questions are far from obvious:
• What are the “must win” battles and where do we need to invest?
• How can FinTech approaches be leveraged to win those battles?
• What changes to the operating model are needed to make it work?
• How do we access the right resources to operate the change?
3.1 retail banking (and private banking)
3.1.1 retail banking
Typically, most digital attackers have developed a value proposition which revolves
around three pillars:
• Simplicity: where traditional retail banks offer complex products ridden with
opaque fees and small font clauses, new comers focus on clean, emotional
propositions
• Convenience: unlike banks’ often lousy interface, new players enjoy the benefits
of a brand new IT platform. More importantly, they tend to put UX (user
experience) at the core of every process, product or interaction that they design
• Price: as they don’t face the same cost structures, new players often undercut
incumbents on price and deliver better value for money
Many FinTech gurus tend to contend that banks will be reduced to empty shells
once completely disrupted by the new startup players – they will still be needed for
regulatory reasons but all the value-add will be provided by new comers. Indeed, banks
are increasingly seeking to partner with FinTech players in order to reengineer the
most critical steps of their value chains. While it’s difficult to tell what the end game
will be and if banking profits will be fully captured by FinTech players, it is already
possible to see how the latter have displaced banks in the hearts (and wallets) of end
clients.
An interesting illustration of this trend is the rise of players such as Bankin or Linxo
which enable clients to aggregate their personal accounts from various banks into
one single consolidated/mobile/real-time interface. This trend is hugely annoying
banks as they lose control over the ultimate customer. Moreover, it also means that
Bankin has a more comprehensive of a customer’s financial situation than each of
the client’s banks, and therefore is better placed to offer suitable advice for future
cross-sell.
Data might be the door for banks to prove this dark vision wrong. Indeed, large
incumbents can take advantage of their large data sets to create value in a way that
prevents the new startup players from taking over. Today’s banks are indeed slow-
moving mammoths but most of them are learning fast how to make a better use of
the data at hand to really understand the customer, which opens up a raft of value
added services, that has been so much spoken about.

29
An example of such value-added services is customer-centric: better serving
customers by monitoring every interaction in real-time, and answering to customer’s
needs in real-time. It means tracking every single interaction via any single channel,
and being able to qualify it. Digital channels may lack the sound of the voice of the
customer to achieve this - but the pace at which customer is typing on his/her mobile
keyboard may just give as much information as the voice tone would have in order
to assess if the customer is happy or not with his interaction with the bank. The end
game is about using all the information available to deliver an intuitive experience
predicated on a deep understanding of clients’ goals and preferences.
Another example is in the way this data becomes meaningful on an aggregated level:
when you want to buy a house in a given neighborhood, your bank will be able to tell
you what lifestyle the people in this neighborhood have, and if you will get along well
with your neighbors.
This means that the banks currently face a risk of becoming less relevant if they
do not acquire the new skills of data science and deep customer insights, and their
margins on current offerings are at risk given the pressure exerted by new startups.
This also means that banks will have to overcome the inefficiencies of their legacy
systems and break down the siloes to make the most of the data stored in their
various systems.
There is however, a bigger risk, coming from China, and difficult to comprehend.
The Alibaba, TenCent, and Weibo are all digital giants that have already changed the
way Chinese people are banking. The expansion of these giants outside of China,
first in the rest of Asia, and then in the Western world, is visible: Alibaba recently
opened several offices in Europe, JD (Alibaba’s biggest competitor) just opened one
in Silicon Valley. For now, they work abroad on their historical core services, but there
is no reason they will not export their banking services either. And when they do so,
banking may experience the same kind of tsunami as retail is currently going through
with Amazon.
Illustrations:
• Simple
• Moven
• Hello Bank
• Silicon Valley Bank
• Atom Bank (first bank that will be mobile online, launching in 2016)
Other topics for ulterior development:
• Online onboarding experience (paperless account opening in 3 minutes,
leveraging facial recognition and social security number)
• Ecosystem play (network of partnerships, seamlessly integrating customers’
banking activities with the rest of their day-to-day lives)
• Social (leveraging the power of a customer’s social network to create greater
intimacy)
• Local community (using peer pressure to foster better behaviors e.g. retirement
preparation through regular savings)
• Fraud management (using AI and big data to weed out fraudulent customers and
transactions)
3.1.2 Lending
Lending tends to be a business line on its own in most traditional banks. FinTech
players tend to combine it with capital raising.
Several trends can be observed:
Read more about how the Chinese digital titans are about to disrupt
financial services here: http://innotribe.com/wp-content/uploads/2015/10/
Innotribe-The-platform-for-disruption-How-Chinas-FinTech-will-change-
how-the-world-thinks-about-banking.pdf

30
Big data for underwriting:
This is about using a wider spectrum of data for scoring and underwriting.
For instance, social medial (LinkedIn, Facebook, Twitter...) contain large amounts
of data that can be leveraged to assess the solvency of individuals and corporates,
especially when those are not well covered by the traditional sources of credit
info. Moreover, traditional information sources (e.g. financial information) may not
accurately reflect the speed at which people and corporates make money or go out
of business.
Artificial Intelligence (AI) algorithms to better predict behaviors:
One key principle is to systematically identify signals that deviate from the norm.
One B2B online lender mentioned they were obviously verifying how old the email
address used for registration was, but found it even more insightful to track behavior
of applicants around the email they receive from the platform upon application:
how many times do they open it? How much time do they take to click? How many
times do they click? Whenever there is a deviation from the norm, it calls for further
sanity checks, and the information is integrated back into the AI logic to improve the
model.
AI can even be used for collections. A good example of this is True Accord (https://
www.trueaccord.com/).
If we do a reality check of where AI helps FinTech today, here is what we find out:
• Artificial Intelligence is currently very valuable to detect fraud
• It is useful for credit scoring but needs to be combined with human intelligence
Which opens up a perspective for collaboration between banks and FinTech:
• Banks with clients’ historical data and cash balance have very useful information
which alternative lenders cannot get this easily
• Banks often find ways to open this data to alternative lenders in a win-win deal
(e.g. Commonwealth Bank of Australia’s partnership with OnDeck)
• Regulators might at some point force banks to open their APIs to third parties
Crowdfunding:
Crowdfunding is a form of alternative finance, which has emerged outside of the
traditional financial system. It is often referred to as Peer-to-Peer funding and it is
based on three types of actors: the project initiator who proposes the idea and/
or project to be funded; individuals or groups who fund the project and for whom
the entrance cost to investing is lower than ever; and a moderating organization
(the “platform”) that brings the parties together. Moderating organizations often

31
offer a legal entity to represent investors (e.g. at shareholders’ meetings) and often
provide complementary services such as legal and marketing consultancy to project
initiators.
Crowdfunding is one of the major disruptions faced by incumbent institutions.
Banks face high costs structures, tightening regulation and struggle to provide
timely quality service especially in the SME segment. The inefficiency of the banking
system has created the conditions for the crowdfunding market to grow rapidly
($5bn raised in 2013) on the back of an increasing number of platforms such as
Lending Club, Zopa, Funding Circle or Prêt d’Union.
Within P2P funding, it is important to distinguish between crowdlending and
equity crowdfunding (which is addressed specifically in a separate paragraph).
Crowdlending brings liquidity to the industry by offering higher returns to investors
in the current period of low interest rates, and giving access to credit to customers
left behind by the traditional lenders. The model exists in B2C and in B2B, for most
categories of lending. The “crowd” providing the funding may consist of individuals
as well as corporates which have excess liquidity (such as insurance companies)
and which to invest in SME loans to get extra yield. For instance, Dutch insurer
Aegon recently contributed €100 million via German platform AuxMoney. http://
www.lendacademy.com/insurer-aegon-invests-e150-million-on-auxmoney/
Startups disrupting this space include:
• Lending Club
• Funding Circle
• Kabbage
• Zopa
• AuxMoney
• Lendix
• Prêt d’Union
• Prosper
• Affirm
3.1.3 retail investments
Many FinTech players have identified a gap in the current provision of advice to
mass and mass affluent customers, specifically where customers are seeking advice
(for investing, for retirement preparation...) but don’t want to incur the cost and
inconvenience of a full-fledge private bank or IFA.
Therefore, many innovations in this space are related to the provision of light or
automated advice to investors:
• Robo-advisor leading to the disintermediation of traditional financial advisors
and providing a comparable level of service for a fraction of the cost (read http://
www.economist.com/news/finance-and-economics/21677245-growth-firms-
selling-computer-generated-financial-advice-slowing-does-not)
• Open architecture investment instead of in-house product pushing
• Automated portfolio development based on investor’s profile and risk appetite
• Promotion of passive investment and automatic rebalancing over expensive
alpha search
• Improved experience (web chat, concierge service, skype with an online personal
advisor...)
• Peer investing e.g. “invest with the stars” (eToro)
• Startups disrupting this space include:
• Betterment
• FutureAdvisor
• Kapitall
• SigFig
• Yomoni
• eToro

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3.2 payments
3.2.1 retail payments
Payments are a sector of finance that was meant to be disrupted by FinTech:
incumbents left the ecosystem full of inefficiencies, regulators opened up the door to
newcomers by creating a new lighter status, and technology shifts bring new means
of addressing the core customer needs.
Two level of disruptions are occurring:
• Shifts in parts of the value chain without replacing the core
• Radically new approaches that reinvent the full value chain
A way to illustrate the various trends is to break the payment innovations by
technology:
• Mobile has been an emblematic disruptive trend in the last years, following the
lead of Square. Square combined technologies in a different way compared to
traditional POS payment systems, thereby allowing merchants to accept card
payments simply by using their smartphone and a cheap card reader plugged
into the jack port of their phone, for a straightforward 2.75% fee. By doing so,
Square brought payment acceptance to 2 million merchants in the US, among
the 8 million merchants in this market. The core of the card payment value chain
remains unchanged with Square: the networks (Visa and MasterCard), the issuing
bank of the card, and even the banking acquirer. Indeed Square operates more
like a digital distributor for the incumbent bank acquirer, allowing him to address
markets it did not even consider before.
• Square has been followed by numerous me-too startups in most markets
(iZettle, Payleven, Sumup, LevelUp, Shopkeep, Revel), with less visible success.
Some providers offer more customized services for businesses, like inventory
monitoring, staff management and accounting.
On the consumer side, many players have tried to disrupt mobile payments (Google,
LevelUp…), and most mobile payment volumes came from mobile commerce (PayPal,
iTunes, …) and SMS payments (mPesa ...). Overall, this seems to represent a limited
amount of disruptions, and one could say that incumbent players are innovative
enough to avoid full disruption – or that payments are too regulated, too complex, not
profitable enough when not at scale, and most users are not inclined to adopt new
methods to pay unless they really trust them and see a benefit.
Yet a massive wave of disruptions is coming to payments, and it can already be
observed in certain geographies: it seems as if the good recipe was discovered when
mobile social networks proposed users to make payments to each other in one click.
Examples include also WeChat Payments and QQ wallet in China. The core payment
remains within the existing card networks, therefore it’s “just” a shift in the value chain.
Yet many other forms of payments (cash, cheques, Roman Denarius…) are disrupted
by this trend: a good number of employers in China now pay their employees using
QQ, many households pay their bills using WeChat, ...

33
Meanwhile, core card payments are slowly adapting to the digital world, moving away
step by step from the notion of account number (the number on the face of a credit
card) to leverage the benefits of tokenization. Tokenization dates back to the late
1980s but the technology is getting serious traction now. Technically, it consists in
replacing a sensitive piece of information by a non-sensitive identifier (the “token”)
which has no intrinsic value. Players like Apple (Apple Pay) and Google (Android Pay,
HCE payments) have improved and promoted tokenization, and a new generation of
startups is aiming to disrupt payments leveraging tokens (token.io, ...).
Another very visible piece of disruption in payments is obviously bitcoin and the
blockchain-related innovations in payments, some of which were discussed in a
previous section. Examples of players in this space include:
• bitpay: allows merchants to accept bitcoin payments
• bitreserve (recently renamed uphold): links with banking accounts
• ripple: builds alternative blockchains and semi-public distributed ledgers
• coinbase: bitcoin wallet
• purse.io: trading e-commerce discounts (for example -25% on Amazon) to
increase liquidity of bitcoin in specific markets
Bitcoin may not scale up and become a main-stream payment system like card
payments are. However, it opened up a radically new way of doing payments.
An immediate disruptive element of bitcoin is the fact that it allows teenagers all
over the world to access payments, thanks to its relative anonymity. For example,
many teenagers use it to buy digital goods when gaming online. By the time they start
working, this generation will have used electronic payments confidently for several
years, unlike the older generations.
It is to be noted that the applications of distributed ledger far exceed the sole perimeter
of payments. This is one of the reasons explaining the attraction of remarkable talent
from the financial services industry for this domain. Examples include Blythe Masters
(CEO of Digital Asset Holdings, a former senior executive at JP Morgan, Managing
Director at 28 and inventor of credit default swaps), Eric van der Kleij (ex-founder of
Level39)...
3.2.2 remittances
Remittances have been one of those industries where incumbents have used their
market dominance to impose unfair prices and keep antiquated technologies,
protected by their brand strength and sometimes unfair regulations. With only a
handful of sizeable players, fees used to be predatory.
This USD 580 BN market is dominated by Western Union and MoneyGram.
We can see today the first signs that millennials are truly reinventing
payments: payment conferences used to be for industry veterans, now they
start to be filled with teenagers creating payment startups even before they
graduate from college.

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Innovators in this space have forged an interesting position by facilitating a payment
process that is overall more effective, while being operated at a much better price
point. The innovations happen essentially in three areas:
• Digital delivery of money: no more cash pickup needed
• Instant money transfer
• Lower currency exchange rates
There still remains a question of regulation, anti money laundering and fight against
terrorism financing.
3.2.3 corporate payments
The universe of corporate payments and trade finance instruments has shown a slow
evolution – after all B2B services are often the last digitized industries, and not all
end users (importers, exporters, ...) are ready to trust alternatives to paper. However,
incumbents are aware of the benefits of moving corporate payments into a state
reflecting the current technical possibilities, and have set up initiatives to digitize the
corporate payments industry, step by step.
Such initiatives include digital trade documents (Bolero, ESSDocs, ...), digital interfaces
to guarantees issuance (GlobalTrade, ...) and digital replacement of the Letters of
Credit (BPO, Bank Payment Obligation). No initiative managed to gain significant
traction, except occasional trade corridors in specific industries. Generally speaking,
the corporate payments industry is ripe for disruption.
Some disruptors are designing innovative processes to issue trade finance
instruments, while others (Skuchain, Chromaway, Epiphyte, Filimint, ...) are leveraging
the blockchain for new corporate payment concepts, keeping traditional letters of
credit as a fallback option for litigation cases.
Clear disruption is also taking place in FX services, an area where incumbents seem
to provide limited value add but charging high commissions. Players include Kantox
(the leading foreign exchange platform for corporates) or Ebury.
3.2.4 bank-to-bank infrastructure
The blockchain is (again) a technology that can potentially facilitate the emergence
of disruptors of the bank-to-bank infrastructure. Players like Ripple and Hyper Ledger
(now part of Digital Asset Holdings) work with banks to explore these opportunities.

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3.3 corporate and investment banking
The industry is so complex, regulated, and servicing clients requires such big balance
sheets, that it does not allow for easy entry of FinTech disruptors tackling the entire
value proposition of incumbent giants: the technology is not enough, the ability to
meet compliance requirements, industrial strength execution and high-touch client
relationships are also paramount. Therefore, most FinTech in the Corporate and
Investment Banking space aim at solving very specific needs and usually go to market
by integrating with incumbents.
An exception to this approach is the factoring and corporate lending industry, where
most FinTech address their target market with a direct approach, independently of
any relationship with banks, and usually starting with small end clients.
3.3.1 financing
Financing is a space where disruption is very visible (especially on the SME segment),
fostered by a conjunction of factors:
• Balance sheet restrictions imposed by new regulatory regimes (B2.5, B3) which
have put banks on the back foot since the global financial crisis
• The poor service delivered with high cost structures by large banks to SME
clients. Existing products (factoring, leasing...) are still delivered through old
processes on antiquated systems which do not reflect clients’ expectations in
terms of speed and convenience
• The low interest rate environment which pushes many buy-side investors (hedge
funds, pension funds, insurance companies) to seek higher returns. When a 10-
year govie from the German government yields 0.3%, lending at 6% to a corporate
starts sounding like a boon! Therefore, an increasing number of financiers are
now willing to provide funding to SMEs when the banks cannot.
New comers have entered the financing space (corporate loans, factoring, dynamic
discounting...) by leveraging specific advantages:
Big Data and AI
Proprietary access to information traditionally placed banks at a competitive
advantage. However, the ability to assess credit risk with minimum resources (a few
data scientists vs. the scores of financial analysts that banks have) has enabled
new comers to overcome the roadblocks linked to the lack of information
Easiness to build from scratch
Modern technology makes it possible to launch “new banks” (Fidor, WeBank) in a
matter of months, with clean IT, lean processes and no overheads
Financing need
Small suppliers receive pressure in the form of extended payment terms, increased
working capital imposed on them by large buyers and persisting bank conservatism.
The general trend toward open account from letters of credit has further contributed
to the problem. In reaction, supply chain solutions are developing very rapidly
(double digit expected in the next 5 years) with several digital solutions offered by
non-bank providers.
Ecosystem effect
Some of the most serious contenders are likely to be those that are embedded in
other ecosystems that provide triggers to identify the need for financing.
As Andreessen (Netscape founder) said it, “software is eating the world”. One of
the immediate ecosystems from which to derive triggers is the finance and ERP
software which are directly plugged into company data. An example in this area is
the partnership between FundBox, which is a kind of online invoice discounter, and
Intuit, which provides finance and tax software to small business (including the
famous “QuickBooks”).
Another ecosystem from which new competition is already emerging consists of the
various e-commerce platforms. Alibaba and Amazon have both launched financing
offerings which have the potential to become major competitors in the coming
years due to the financial clout of the parent companies and the large installed

36
customer bases
Eventually, social networks (Facebook, LinkedIn...) or internet companies (Google,
Tencent / WeBank) could also be tempted to enter this area (as well as retail lending
obviously) as they have access to large amounts of data which can be leveraged for
underwriting as well as for loan origination
Some disruptors in this area include:
OnDeck (lending)
FundingCircle (marketplace for business loans)
BlueVine (invoice factoring)
Taulia (dynamic discounting)
Intuit QuickBooks + FundBox (finance software / online discounting with plugplay
into the corporate ledger) http://investors.intuit.com/press-releases/press-release-
details/2015/Intuit-and-Fundbox-Partner-to-Tackle-1-Pain-Point-Faced-by-Small-
Businesses-Cash-Flow/default.aspx
3.3.2 wholesale transaction banking
Wholesale Transaction Banking is often defined as covering the following areas:
• Trade finance
• Cash management
• And sometimes also includes wholesale payment solutions
It represents a large and stable source of fees for banks (stickiness, low ALM risk)
estimated by BCG at $330 billion in 2014. It is still expanding at a CAGR of 7-8% on
the back of globalization and emerging markets growth.
Wholesale Transaction Banking has proved less prone to disruption than other lines
of business:
• Lots of historical processes (such as L/C) which have been going on for decades
• B2B nature, not attracting a lot of public attention
• Already pretty automated (e.g. payments)
• And most importantly subject to important network effects
• The Digital Trade Ecosystem (source: BCG)
Read more about BCG’s analysis here:
https://corporates.swift.com/sites/sdccor/files/bcg_embracing_digital_in_trade_
finance_oct2015.pdf
The number of players involved and the dynamics between these players make it
difficult to find alignment and change the system. Indeed, many incumbents have no
incentive to change and thus won’t change unless a tipping point is reached in favor
of a new solution. Therefore, many worthy innovations have failed to deploy in recent
years e.g. electronic bills of lading, MT 798 (high upfront integration costs), Bank
Payment Obligation (cannibalization of banks’ L/C business, upfront investment for
corporates, suboptimal value proposition compared with L/C).
The digital trade ecosystem

37
This being said, many legacy processes are still highly manual, paper-based and
inefficient. Therefore, a number of innovations are now beginning to hit wholesale
transaction banking:
OCR (Optical Character Recognition)
OCR drives a lot of dematerialization and automation potential within the existing
framework
Compliance 2.0
New filtering technologies are revolutionizing compliance processes which are
central to trade finance (KYC, AML, sanctions). In particular, fraud detection
algorithms relying on AI are gradually replacing manual checks
Supply chain reengineering
e-invoicing is set to become a market standard of the next decade. Initial take off
was pretty slow as it required heavy investments to develop EDI (Electronic Data
Interchange) systems to exchange transactional data between trading partners until
the early 2000s. With the advent of web-services, open networks of e-invoices have
emerged and will inevitably become the market standard.
For instance, it is estimated that more than 95% of invoices are electronic in Finland.
Furthermore, many public sector organizations in Europe – including Sweden,
Norway, Spain and Denmark – have announced compulsory eInvoicing programs.
From the perspective of client companies, electronic invoicing services are a way to
automate their accounts payable departments as well as a strategic tool to manage
a supplier chain and optimize procurement.
Commerce
The formation of supplier-buyer networks trading electronically will not only
decrease the risk associated to B2B trade, it will also give rise to piles of data ready
to be analyzed. Such data is already being used by eInvoicing providers to move into
Financing Services (e.g. Basware and Taulia offering forms of supply chain finance)
Change in the mix of payment instruments in a world where risk protection is less
value due to increased transparency and data
The lack of availability, burdensome complexity, delays and cost of L/Cs has led
to global trade relying increasingly on Open Account commerce (payment terms)
despite its disadvantages for the seller.
More importantly, new blockchain-based solutions are appearing that bridge the gap
between unprotected (Open Account) transactions and fully collateralized trades
(or payment in advance). Smart contracts recorded on distributed ledgers have
the potential to address the root causes of the limitations in current trade finance
offerings. It moves seller payment and timing requirements into a fast, automated,
electronic blockchain-based system that relieves those fiduciary responsibilities in
those vast majority of cases when transactions proceed normally without disputes.
Several large wholesale banks have recently united to back R3CEV, a startup
dedicated to building the next generation of trade finance services.
Disruptors include:
• Taulia
• Tungsten
• Ariba
• Tradeshift
• SkuChain
• R3CEV
• ARGO
3.3.3 institutional investments
Key trends include:
• Improved portfolio analysis and investment strategies due to Big Data (sentiment
analysis, web crawl, data viz). Buy side players now leverage big data for their

38
analytical trading tools, for instance by using greater access to historical data
and statistical analysis to make market predictions, or by using machine-learning
algorithms to discover clearer market entry and exit signals. Visualization tools
and VR technology also offer new ways to glean meaningful insight from data.
• Algorithmic trading (including high frequency trading) which relies on computer
programs to make trading decisions based on information that is received
electronically, before human traders are capable of processing the information
they observe. Algo trading is now well embedded in the practices of institutional
investors. In 2009, it was estimated that it represented about two thirds of all US
equity trading volume.
• Further automation and easier outsourcing of non-core tasks (fund management
and administration, custody, settlement, reporting)
• As a consequence, the market-making universe has expanded to include high-
frequency trading firms, hedge funds, and even (depending on the asset class)
asset managers. Primary markets are thus being increasingly penetrated by large
asset managers, boutique investment banks, regional banks, and private-equity
firms, while i-banks’ traditional information advantage is eroding at a fast pace.
On the back of data explosion, a number of information-services providers are
looking to digitally enter the market from an initial base of data and analytics
delivery.
Disruptors include:
• Addepar
• StockTwits
• SumZero
• Epiphyte
• Symphony
3.3.4 equity financing
Equity crowdfunding is another trend attesting digital’s ability to pool individual
energies into a collective effort. Indeed, it enables individuals (and corporates) to
support ventures initiated by other people (or organizations) through the provision of
finance in the form of equity. It is an emerging but fast growing market.
Equity crowdfunding platforms usually act as entities bundling many small investors
into one legal entity which allows them to join equity trading market. Investors are no
longer limited by expertise or investment size limits. Therefore, equity crowdfunding
brings additional cash to the table while enabling small companies to issue shares
over the internet and receive the investments of registered users in return. Some
platforms also offer additional services to filter and evaluate the projects submitted
to the public for funding.
Existing financial services players can also leverage equity crowdfunding for investing
as well as for brand building. For instance, Allianz France launched a crowd equity
investment in partnership with SmartAngels and Idinvest Partners. Through this
fund, Allianz co-invests alongside its clients by matching clients’ monies up to €50k
when the project is deemed valid by Idinvest. In addition, Allianz clients benefit from
a 5-year put option in case of death or disability.
By combining blockchain technology with crowdfunding, several players are now
trying to set up digital stock offerings. One of the main appeals of digital-based
equity financing is that it is easier to create a liquid market and to market the offering
globally. Funderbeam, for example, offers a blockchain-based infrastructure to trade
equity beyond actual stock market. Blockchain here acts as a trustworthy book
keeper of who owns which stocks.
Disruptors include:
• CircleUp
• SeedInvest
• ExitRound
• Seedrs
• CrowdCube
• AngelList
• etc...

39
In early stage investments, some reward-based platforms enable crowdsourced
investments into startups and sometimes even established corporates. These
platforms do not allow investing in equity. Funders go to these platforms to benefit
from a reward which often comes in the form of a product or another incentive
received from the financed company.
Financed companies often resort to this way of financing for market testing purposes.
Indeed, reward-based platforms provide publicity and audience for a prototype, which
allows to check market appetite for an idea and “pre-sell”. Market testing is often more
important for startups than funding itself. Platforms in this space include:
• Kickstarter
• IndieGogo
Sometimes products and projects receive substantial funding without any
commensurate reward which begs a question as to whether they shouldn’t allow
equity to be offered as well. Especially in cases of the most successful projects like
virtual reality headset Oculus Rift, which attracted a broad controversy. The company
received funding of $2.4 million from small donors. In return donors got Oculus Rift
t-shirts and some first prototypes of headset. 18 months later Oculus Rift sold to
Facebook for $2 billion in cash and stocks bringing founders 143x RoI but leaving
Kickstarter backers with a feeling of being tricked not to invest in equity but into a
reward.
3.4 Insurance
In this section, we will examine the various trends affecting the insurance industry
before looking at the changes affecting the individual lines of business.
3.4.1 disintermediation
and commoditization
For many years, insurance was viewed as a high-end product distributed through a
specialist workforce of agents and brokers. However, as competition intensifies, the

40
whole industry is gradually shifting to cheaper forms of distribution, starting with
those lines of business where the product can be most easily standardized and the
need for advice is the lowest.
The transition is now almost complete in personal lines on the non-life side – at least,
in all advanced markets. Over the past 20 years, direct distribution (first phone, then
online) has wiped out intermediated distribution in the UK. While the revolution took
a bit longer in other markets like Germany and France, traditional brokerage and
agency networks inexorably lost market share to new entrants benefitting from a
lower cost structure. Similarly, in many markets, bancassurance has proven to be a
cost-effective channel to reach out to customers as the insurance sale has become
facilitated by a pre-existing transaction or relationship. In Spain, France and Italy, 70%
of all life insurance is now distributed through banks.
The core of the issue is that insurance is not a “pleasure” buy and customers are
usually highly sensitive to price. In other words, insurance can very easily become a
commodity and insurers will always struggle to differentiate from each other. Indeed,
there’s nothing easier to replicate than an insurance product!
There are two ways for an insurance company to stand out. First, a good reputation
built through many years of high quality servicing (esp. in case of a claim, which is the
real moment of truth for any policyholder) is the main asset an insurer can build. For
the rest, as Paul Geddes, CEO of Direct Line Group, puts it, it’s all about the price (for
which solid underwriting is a pre-requisite) and the convenience.
One of the most recent developments is the rise of price comparison websites, also
known as “aggregators” which inject further transparency on the market (Compare
the Market, Confused.com, Money Supermarket...) and make price competition even
fiercer. In reaction, many insurers have developed radically low-cost direct offerings
such as Hastings and eSure in the UK or Direct Assurance and Amaguiz in France.
3.4.2 more granular pricing and underwriting
In most retail lines, underwriting has always been largely statistically driven thanks
to the large volumes of contracts. For any insurance company, it is vital to avoid
falling behind competitors in terms of pricing and risk assessment because this
immediately triggers anti-selection and deterioration in the loss ratio. For example,
if an insurer finds out before everyone that drivers of red cars have more accidents
(all else being equal), this insurer will increase the premium charged to this category
of drivers. As a result, all the other insurers will see an influx of drivers of red cars the
following year causing an increase in claims frequency.
Nowadays, insurers are increasingly able to identify additional predictive factors and
to improve their risk segmentation into thousands of risk pricing cells. This is driven by
two main changes: on the one hand, big data makes more data available for analysis
and on the other hand, machine learning enables insurers to develop more powerful
algorithms. For instance, insurers have started collecting data on our browsing and
Emerging Developing Mature Type 1 Mature Type 2
Distribution life cycle stage
Others
Tied
Agents
Others
IFA / Brokers
Bancassurance
Tied
Agents
IFA / Brokers
Bancassurance
Tied Agents Tied Agents
Bancassurance
IFA / Brokers
Aggregators
Digital
Digital
Low
High
Markets along the distribution llife cycle
CHINA RUS INDONESIA S’PORE FRA BEL US UK AUSPOL
Degreeofcontrolformanufacturer
Commoditization and disintermediation are long term tendencies which are
only going to be reinforced by digital technologies.
Global distribution life cycle pattern

41
clicking behaviors as those can be good predictors of risk appetite.
Besides, with the advent of price comparison websites, pricing has become
increasingly sophisticated and takes into account not only the risk component (as
known as “risk pricing”) but also the client’s elasticity and the competitive positioning
vs. other insurers (as known as “market pricing”). In the long run, this trend towards
micro risk discrimination and segmentation poses some challenges because it
leads to a kind of “de-mutualization” of risk which goes against the very principle of
insurance (i.e. risk pooling).
In parallel, an opposite trend towards re-mutualization (but still a form of
disintermediation) is taking place through P2P insurance, which enables users to
form groups - a bit like on LinkedIn - looking for insurance cover. Typically, claims
up to a certain threshold are shared among the group while high severity claims are
ceded to an insurer. Obviously, this trend reduces the role of insurance companies to
a form of reinsurance and deprives them from end customer ownership. Examples in
this area include: FriendSurance, Bought by Many and Guevara.
3.4.3 change in underlying risk
The retail insurance industry is undergoing a revolution forcing its reinvention, as the
underlying industries (house, car, health) are themselves undergoing deep and lasting
changes (see above), which changing the nature, frequency and severity of claim
events.
For example, in a city where all cars would be driverless, there would probably be
very few car accidents and insurance premiums would be reduced proportionately.
Consequently, our mother company Allianz estimates that Motor insurance premiums
will reduce by 50 to 80% over the next ten years! This begs the question of the role of
the insurer, which will have to shift very rapidly from risk transfer to servicing. In the
case of the motor insurance industry, the largest threat for insurers does not come
from other insurance companies but rather from car manufacturers and providers of
onboard telematics.
3.4.4 Implications by line of business
Non-Life and Health insurance
The race to commoditization is open, it’s all about convenience and price so only
insurers with low cost structures will survive. Especially in retail lines of business,
most intermediaries will vanish as no millennial would ever consider purchasing
insurance other than online - and mainly through a price comparison website. In
addition, the nature of the underlying risk will evolve due to the internet of things
(connected car, smart home, wearables for eHealth), causing not only a decrease of
the pure insurance market but also competition from
Life insurance
Traditional protection products (term, serious illness, accident) should remain the
turf of insurance companies. However, these product lines represent less than a
third of life insurance premiums. Other products (whole of life, unit linked etc.) which
are in fact investment products distributed through agents, financial advisors and
partner banks, will face fierce competition from new entrants. In particular, low cost
(but good quality) offerings supported by robo advisors will lead to fee compression
and further disintermediation.
Business and Corporate insurance
Except at the low end of the market (business insurance for SMEs), most policies
are tailor made and thus likely to keep being sold in person – often through brokers.
This should not change in the near future. However, all insurers have started to
harness digital technologies e.g. by using big data for risk underwriting and sensors
for data collection and risk prevention. Even on the life side for collective policies,
insurers develop portals and applications for employees to connect and manage
their benefits.

42
B2B TRADE: ALSO RIPE
FOR DISRUPTIVE INNOVATIONS
4

43
conclusion: trade finance is also
ripe for disruptive innovations4
The objective of this last section is to analyze the impact on trade finance of
technology changes that have been described in the previous sections. In particular,
we focus on the way two new trends - platformization and big data - will reshape B2B
trade in the coming years.
The two trends below have already started to unfold and depending on the speed
at which they keep spreading, they will lead to very different scenarios for the Trade
Finance industry. It should also be noted that these trends are not mutually exclusive:
their impact adds up and compounds if they occur simultaneously.
4.1 platformization of b2b trade
In this scenario, mega commerce platforms such as Alibaba, Amazon, Tencent and
eBay keep growing and end up capturing a dominant share of B2B trade. Direct B2B
trade between well-known and trusted counterparts is in decline. Online search
engines have taken a share of the “fresh” trade and a growing proportion of B2B trade
is now obliged to use paid advertising to sustain volumes. In fact, platform thinking
gradually displaces the traditional product approach. Companies can no longer
develop products with a linear “manufacture to distribute” process, they need to
engage customers on platforms that allow users to create information and exchange
content with each other.
Platformization as a trend goes beyond e-commerce as most goods and services
become available through such platforms, from software (email, CRM, accounting,
HR) to lifestyle (AirBnB, BlaBlaCar) and entertainment (Spotify, Netflix).
The impact on the FS industry is very visible. Several lines of business have been
uberized by small platforms. In the same way as they access their key software
through cloud-based platforms, SMEs buy finance-related services (FX, insurance,
lending, factoring, employee benefits, …) online from FinTech companies as these
provide better service at a lower price than large incumbents. In fact, most of the
traditionally “intermediated” services have moved to such platforms.
Some implications of platformization for the trade finance sector include:
• More and more transactions move online and can be settled using non traditional
payment channels (PayPal, blockchain based systems, salaries and money
transfer taking place on WeChat)
• The way companies buy, sell, look for customers changes radically. For instance,
Facebook-like app ecosystems emerge (e.g. Intuit app store) in which companies
need to be inserted to reach new prospects
• Due to the number of interactions and transactions taking place on large
platforms, these have acquired superior data on users which gives them an
unfair advantage over banks and insurers
• Some platforms enter a “monetization” game to cross-sell other services on
their user base. In emerging markets, Alibaba, Baidu and Tencent are credible
providers of financial solutions

44
• Perceived risk of trading goes down as platforms create a safer environment by
allowing users to evaluate their counterparts
4.2 big data in b2b trade
This scenario is only a continuation of an existing trend whereby more and more
data is generated every day. As a result, more data becomes available on corporates
and individuals. Some leaders such as Google, Facebook, LinkedIn, Amazon naturally
accumulate large amounts of useful data but this is also true for less obvious
leaders. For instance, cloud-based ERPs, payment services providers or eInvoicing
networks have access to meaningful data on B2B trade, ranging from payment
behavior, supplier-buyer relationships to the full ledger of transactions. In addition, a
lot of data becomes accessible to everyone through the Open Data movement (Open
Corporates, credit black list...) or API providers on multiple cloud-based accounting
systems (CriskCo) which reveals data that was previously difficult to assemble.
In addition to the availability of data, the ability to process, analyze and visualize
information has increased significantly. Artificial Intelligence (AI) has become
commonplace and has started to reshape entire industries. Many clerical tasks
are executed automatically e.g. financial advice is increasingly provided by robo-
advisors, simple auditing tasks are performed by algorithms, the bulk of accounting
takes place in cloud-based systems, customer service is automatically delivered by
the next generations of Siri and OK Google. Machine learning frameworks make it
relatively easy to start training a machine on any business question where something
can be predicted. In some ecosystems, AI-driven objects are becoming the norm e.g.
by 2020, a significant proportion of new cars will ember have self-driving capabilities
and will be connected together.
Besides, a whole industry has emerged around Big Data. Some players specialize in
repackaging data, reaching out to new audiences (for example CreditHQ with credit
bureau data tailored to SME needs, CriskCo with accounting data), while others make
AI tools available to a larger public (Anaplan, Glassdoor, Dataminr, Dataiku).
Some implications of Big Data for the trade finance sector include:
• Banks and insurance companies have lost some of their data advantage over
other players. In particular, several other ecosystems (large platforms, ERPs…)
have access to highly relevant data for propensity modelling and risk assessment
• Banks and insurance companies are outsmarted by AI specialists when it comes
to processing large amounts of information. Firms like Palantir and IBM Watson
have started to take over entire risk management departments, even at large
corporates
• Businesses become increasingly data-driven. Even though human beings
themselves are not more rationale than before, decision-making (marketing,
pricing, risk management) is increasingly supported by new data and analysis
• All firms compete for (scarce) data science resources, but only a few manage to
attract and retain top-notch talent while others need to rely on a combination of
external solutions and temporary resources

45
• In general, the proliferation of data and the increasing quality of predictions
decrease the perception of risk among trading parties. Self-insurance becomes a
viable option for many a corporate
4.3 parting thoughts: the future
of trade finance
In addition to the external technology changes that materialize through the two
aforementioned mega trends, Trade Finance is subject to some internal shifts that
will affect the future dynamics of the industry.
The general environment of safer transactions favored by large platforms and data
proliferation is further supported by the structuring of efficient supply chains and
the emergence of eInvoicing networks. At the same time as transactions become
safer, financing and risk mitigation are often embedded in supply chain finance or
dynamic discounting solutions. Such financing solutions are often integrated into the
invoicing platform or the accounting system, and thus have become very familiar to
clients who consider themselves “covered” also for credit risk. As Kamel Alzarka, CEO
of Falcon Group, summarizes it, clients always prefer “cash now” to “indemnification
later” – and many financing solutions exist that provide the comfort of early liquidity
without the hassle of credit insurance, letters of credit or BPO.
In addition to straightforward financing solutions which were the initial focus of
newcomers, other services are becoming available through platforms: information,
collections, FX and credit risk insurance to name a few. Some fintech startups
(Taulia, Market Invoice, BlueVine, Tungsten) are emerging as independent cloud
supermarkets for finance solutions, disintermediating banks and insurers as they
provide more convenient, faster services, especially to the millions of SMEs which did
not have access to efficient factoring and reverse factoring solutions through their
main-street banks.
A complete unknown in the equation is the way distributed ledger technology
is potentially going to redefine the way trust is enforced in all transactions – and
thereby have an impact on Trade Finance. Whilst it is not yet clear as of 2016 what
the consequences of the blockchain will be, there are already various attempts
made at disrupting existing products such as letters of credit and guarantees – and
replacing them with more cost effective, timely alternatives. Those attempts come
from FinTech players (Skuchain, Epiphyte, ChromaWay, …) as well as from incumbent
players partnering with startups (R3CEV, Reddit), which increases the chance of
widespread adoption.
All those evolutions contribute to sketching a new normal where data will become
more pervasive and where corporates will feel less exposed to credit risk. At the same
time, the frontier between the historical lines of business (L/Cs, credit insurance,
factoring, leasing) will become blurred as substitute offerings are developed on clean
technology by putting client needs at the forefront. This will undoubtedly contribute
to the rise of a new paradigm for Trade Finance. In the long term, there should
be both an increase in volumes as new customers become addressable (namely
the whole SME segment) and an increase in service quality. It remains to be seen
who will most benefit from the new paradigm – incumbent players partnering with
startups and preserving their grip over the main profit pools, large software and
e-commerce platforms diversifying into financial services or the more agile FinTech
players uberizing today’s behemoths.

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