3 Secrets to Becoming a Predictive Enterprise

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Three Secrets to Creating a Successful
Predictive Enterprise

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© 2019 Decision Management Solutions 8
Speakers
James Taylor
CEO of Decision Management Solutions
Leading expert in how to use advanced analytics,
business rules and digital decisioning to improve
business results
A faculty member of the International Institute for
Analytics and the author of multiple books
 Clifton Phua
Senior Director / Customer-Facing Data Scientist for
APAC at DataRobot
More than a decade leading analytics efforts at
Singtel/NCS, SAS, and A*STAR
Domain expertise in banking, insurance, government,
cyber security, fraud detection, and public safety

9
AGENDA
Three secrets to Creating a Successful Predictive
Enterprise
1. Focus on front-line operations
2. Industrialize predictive analytics
3. Turn machine learning into business learning
Questions?

A Predictive Enterprise
Broad and deep use of predictive analytics
Manage Risk Customer Value
Detect Fraud Reduce Costs
A Predictive Enterprise applies predictive analytics and machine learning, to manage risk, detect
fraud, maximize customer value and reduce operational costs throughout the enterprise.

Three Secrets to Success As a Predictive Enterprise
Focus on Front-line
Operational Decisions
Industrialize Predictive
Analytics
Turn Machine Learning into
Business Learning

12© 2019 Decision Management Solutions
Focus on Front-line Operational Decisions

From presenting
data for analysis …
Many organizations focus
on presenting data to
people so they can do
analysis or monitor
progress.

…
to making decisions with
predictive analyticsA Predictive Enterprise puts the
focus on decision-making,
explicitly identifying the decisions
they want to improve so they can
identify the predictive analytics
that will help.

“Breakaway companies are almost twice as likely to have identified and prioritized the top ten to 15 decision-making processes in which to embed analytics.”
“Most companies start their analytics journey with data; they determine what they have and figure out where it can be applied. Almost by definition, that approach will
limit analytics’ impact. To achieve analytics at scale, companies should work in the opposite direction. They should start by identifying the decision-making processes they
could improve to generate additional value in the context of the company’s business strategy and then work backward to determine what type of data insights are required
to influence these decisions and how the company can supply them.”
“Breakaway companies … have
identified and prioritized the …
decision-making processes in
which to embed analytics.”
“Most companies start their analytics journey with data. Almost
by definition, that approach will limit analytics’ impact.”
“To achieve analytics at scale, companies should … start by
identifying the decision-making … they could improve to
generate additional value”
Breaking away: The secrets to scaling analytics, May 2018 By Peter Bisson, Bryce Hall, Brian McCarthy, and Khaled Rifai

Put DecisionsFirst™
 We need to improve
these results
 Which means making these
decisions more accurately
 Which will require these
analytics
 Which can be built from
this (Big) data
Focusing on operational
decisions first ensures a strong
business context for predictive
analytics and machine learning
projects.
Starting with the business
goals of the project, the
decisions that have an impact
on those goals can be
identified. Understanding
these decisions will reveal
which analytics are required
and then the data needed to
build these analytics can be
found, organized, cleaned and
prepared.

Strategic Impact Doesn’t Mean Strategic Decisions
Everyone wants analytics to have a strategic impact but changing
executive behavior is hard, they are the most experienced staff in
the company and these are the decisions with least data. Changing
the business in a strategic way can be done by changing the call
center or other front-line organizations instead.
For instance, focusing on how to cross-sell or upsell a new financial
product to each existing customer is an operational decision but has
a strategic impact on the success of that new product.

Focus on Operational Decisions
Strategic Decisions
Tactical
Decisions
Operational
Decisions
Organizations make three kinds of
decisions. They make strategic,
one-off management decisions.
They make repeated, tactical
how-to-run-the-business
decisions. And they make
operational decisions about single
customers, single transactions.
Using predictive analytics in
operational decisions is central to
becoming a predictive enterprise.

Many Operational Decisions Are Suitable
Improving operational decisions has a strategic impact because the
value of improving them is multiplied by the number of times they are
made. Plus operations is where all the data is.
Which Offer?
What Content?
Which Supplier?
Pay Claim?
Which Script?
Allow Transaction?

Automating operational decisions enables straight through, hands-free
“Jet” processing of transactions.
How customers are treated in operational decisions determines their
satisfaction and loyalty.
Fraud is acquired one transaction at a time so making operational
decisions “fraud-aware” reduces fraud
Making decisions about interacting with customers one customer at a
time – as operational decisions – targets them more effectively.
Risk – credit risk, retention risk – should be assessed for each
transaction by embedding risk analytics in operational decisions.
The Power of Operational Decisions
Target
Customers
Manage Risk
And Much
More
Enable STP
Maximize
Loyalty
Reduce
Fraud

Industrialize Predictive Analytics

. ©2019 DataRobot, Inc. – All rights reserved
“Not all customers are created equal. If you’ve
ever run a business (or even just been a
customer yourself), then you know that some
customers provide more revenue (and incur
fewer costs) than others. Figuring out which to
focus on and invest in is critical if you want to
maximize your profit.”
Source: https://hbr.org/2014/07/how-valuable-are-your-customers

“Here’s a basic definition: The amount of
profit your company can expect to
generate from a customer, for the time
the person (or company) remains a
customer (e.g., x number of years).”
Source: https://hbr.org/2014/07/how-valuable-are-your-customers

Case Study
Lifetime Customer Value
Freddie Mac Mortgage Data

Case Study: Freddie Mac Mortgages
The Federal Home Loan Mortgage Corporation, known as
Freddie Mac, is a public government-sponsored enterprise,
headquartered in Tysons Corner, Virginia. The FHLMC was
created in 1970 to expand the secondary market for
mortgages in the US.
They have published detailed data on mortgages, including
mortgage payments, pre-payment, refinancing, default and
loss-given-default here:
http://www.freddiemac.com/research/datasets/sf_loanlevel_dataset.html

Case Study: Freddie Mac Mortgages
Banks regularly purchase Freddie Mac mortgages. These
banks want to assess both risk and return on their
investments, and must hold regulatory capital.
The value of a Freddie Mac mortgage to the bank is the
interest margin (mortgage interest rate less the cost of
funds), for the duration until the mortgage is pre-paid,
refinanced, or matures.

But The Data is Truncated…
We don’t know the full accumulated profit until the end of the loan term for many mortgages,
and definitely not for mortgages written in the past decade.

So we model how much the profit accumulated across different time spans within a mortgage term.
Here is the experimental design:
1. Choose pairs of random points of time, partway between the mortgage origination and now for each mortgage i.e. the
yellow bars in the diagram shown above
2. The features are as at the first point of time i.e. the beginning of the yellow bar
3. The target value is the increase in accumulated profit as at the second point of time i.e. the end of the yellow bar

o Repeat the sampling of time periods for many mortgages, then train the algorithms to learn how to predict the
accumulation of profit within a mortgage term.
o Run scoring predictions using current mortgages and current feature values, but predict out to the full term of the
loan.

Solution

DataRobot Finds the Drivers of Lifetime
Customer Value
Start with the total interest margin
made between 2 points of time. Join in
economic data and movements in
property prices since the mortgage
was originated. Include the mortgage
status at the earlier of the 2 points of
time.
DataRobot will automatically find
the patterns, the links between the
mortgage details and the actual
profit on each mortgage.
You can use DataRobot’s accurate
predictions to estimate the future profit
for the remaining mortgage term,
allowing for pre-payment, refinancing
and default.
id_loan FromAge ToAge CurrentBalance delq_sts Margin countLatePaymentRefinanceRateRelativityRealGDPChangeUnemploymentRateCurrentLCV FutureLCV fico flag_fthb cd_msa mi_pct
F101Q4305297 109 174 $92,073.87 0 0.75 0 -2.345 -1.5 -0.5 -$283.74 $5,038.23 661 N 38060 0
F110Q2180384 3 10 $206,000.00 0 -0.125 0 -0.651 2.5 0.0 -$34.33 -$507.34 681 N 30460 0
F110Q1205431 6 61 $171,000.00 0 -0.525 0 0.053 2.7 -0.1 -$1,007.81 -$263.42 770 N 40140 0
F110Q2203630 70 74 $50,862.85 0 0.2 0 -0.245 1.4 0.0 -$2,768.40 $46.07 685 N 47260 0
F114Q2153614 16 27 $293,354.38 0 -0.075 0 -0.252 0.9 -0.2 -$664.47 $417.06 741 N 19740 0
F110Q3292175 41 73 $146,027.03 0 -0.55 0 -0.060 -1.2 -0.2 -$931.56 $290.66 814 9 35380 0
F100Q1081946 16 58 $73,538.99 0 0.7 0 -1.028 -1.3 0.4 $251.94 $969.32 701 9 35300 0
F103Q4156766 11 61 $185,443.76 0 -0.675 0 0.043 3.5 0.0 -$1,414.34 -$462.45 603 N 31084 0
F111Q1120275 16 43 $141,184.59 0 1.4 0 -1.547 0.5 -0.2 $1,466.92 $750.58 743 N 0
F108Q3090607 6 11 $234,000.00 0 1.425 0 -1.295 -5.4 1.4 $1,303.27 $976.65 751 N 39580 0
F111Q3107733 33 54 $112,558.30 0 -0.5 0 0.136 5.0 -0.1 -$697.76 -$232.03 765 9 14060 0
F109Q1294965 16 78 $95,811.46 0 0.6 0 -0.758 2.7 -0.1 -$217.54 $3,569.18 722 N 44700 0
F104Q3209180 27 36 $98,427.08 0 -1.175 0 0.448 0.2 0.1 -$2,285.80 -$853.96 718 9 26420 0
F115Q2293648 15 16 $146,752.39 0 -0.325 0 -0.492 3.1 -0.2 -$237.70 -$39.69 717 9 40380 0
F104Q4135958 99 120 $113,336.12 35 1.6 36 -2.409 2.8 -0.1 -$1,961.83 $717.80 688 N 35840 0
F114Q1015374 6 33 $109,000.00 0 -0.125 0 -0.347 5.0 -0.1 -$189.73 $291.20 732 N 26620 0
F104Q4039132 67 101 $69,215.73 0 -0.1 0 -0.797 3.9 -0.2 -$3,957.59 $186.63 796 N 28140 0
F104Q2278774 35 63 $152,038.84 0 -0.9 0 -0.060 3.1 0.0 -$2,894.44 -$9.65 769 N 36540 25
F103Q1190789 52 161 $143,517.54 0 -1.175 0 0.386 2.7 0.2 -$5,851.49 $4,520.60 700 N 15980 30
F103Q4148486 18 56 $217,926.30 0 -1.075 0 0.503 2.1 -0.2 -$4,305.52 -$9,731.23 745 N 14740 0
F105Q2076475 8 108 $51,357.11 0 -1.075 0 0.366 4.9 -0.3 -$330.67 -$815.84 695 N 17140 0
F105Q1087486 17 87 $351,836.50 0 -1.125 0 0.486 0.4 0.0 -$4,028.81 -$1,591.95 653 N 48864 0
F102Q3279531 7 68 $181,934.27 0 0.075 0 -0.142 3.8 0.2 -$426.25 $22.71 653 9 0
F100Q3153225 2 27 $60,000.00 0 -0.175 0 0.174 2.3 -0.1 -$26.25 $539.12 765 N 25860 0
F103Q3365911 149 153 $144,027.76 0 1.375 3 -1.575 0.8 -0.1 -$2,615.45 $728.83 717 9 0
F113Q3311887 15 34 $204,460.65 0 -0.55 0 0.230 2.3 -0.4 -$2,440.80 -$976.78 779 Y 18700 0
F108Q4043280 4 61 $127,000.00 0 0.175 0 -0.617 -5.4 1.4 -$13.77 $0.00 758 N 0
F115Q2091086 14 17 $263,687.79 0 -0.35 0 0.308 3.5 0.0 -$2,735.77 -$359.88 802 9 33874 0
F113Q4141925 10 35 $150,550.71 0 -0.05 0 -0.270 2.3 -0.4 -$490.87 $251.04 803 N 12420 0
F100Q4021520 3 31 $86,000.00 0 0.375 0 -0.985 2.1 0.2 $150.50 $53.13 747 N 0
F107Q3139405 103 107 $91,832.95 0 2.7 12 -2.888 1.4 0.0 $14,059.60 $867.36 773 N 10740 0
F104Q2242464 8 66 $201,671.09 0 -0.275 0 -0.692 4.3 -0.1 -$366.85 -$828.14 623 Y 33124 30
id_loan Prediction
F112Q4226070 -$3,034.21
F114Q2167787 -$2,187.73
F114Q2191267 -$1,518.69
F114Q2173235 $543.20
F113Q3410438 -$1,427.63
F115Q2316706 -$8,335.81
F104Q1265613 $1,273.85
F115Q3133523 -$3,377.70
F109Q2603005 $1,339.38
F116Q2257668 -$3,788.82
F112Q2239600 -$2,134.66
F110Q1141313 $1,864.99

Understanding
the Data

Data Summary
Note:
• Use default settings in DataRobot
• Optionally can use monotonicity constraints
Target:Type Regression
Target Column FutureLCV
Rows 50,000
Columns 35
Categorical/Boolean 12
Date 0
Text 0
Numeric 23
Metric RMSE

Data DictionaryFeature Column Type Special Column? Monotonicity Description
id_loan Categorical
ID column
Do not use
LOAN SEQUENCE NUMBER - Unique identifier assigned to each loan.
FromAge Numeric First point of time, the time at which future profit will be estimated, the as at date for the features. Measured in units of months since the note origination month of the mortgage.
ToAge Numeric Second point of time, the final time at which future profit is recorded and totalled. Measured in units of months since the note origination month of the mortgage.
currentBalance Numeric increasing
CURRENT ACTUAL UPB - The Current Actual unpiad balance reflects the mortgage ending balance as reported by the servicer for the corresponding monthly reporting period. For fixed rate mortgages, this UPB
is derived from the mortgage balance as reported by the servicer and includes any scheduled and unscheduled principal reductions applied to the mortgage.
delq_sts Numeric decreasing
CURRENT LOAN DELINQUENCY STATUS – A value corresponding to the number of days the borrower is delinquent, based on the due date of last paid installment (“DDLPI”) reported by servicers to Freddie Mac,
and is calculated under the Mortgage Bankers Association (MBA) method.
margin Numeric increasing The interest margin earned by the bank, euqalling the mortgage interest rate, less the cost of funds.
countLatePayment Numeric The number of times in the past that a monthly mortgage payment was missed. Must always be >= delq_sts
RefinanceRateRelativity Numeric decreasing How much the mortgage interest rate will change if the mortgage is refinanced. A negative value means that the mortagee will save money if they refinance.
RealGDP Numeric increasing Economic growth rate, measured in gross domestic product, adjusted for inflation
ChangeUnemploymentRate Numeric Change in unemployment rate in the quarter that the default occurs versus the previous quarter
CurrentLCV Numeric Accumulated profits via interest margin, as at the FromAge
FutureLCV Numeric target Increase in accumulated profits via interest margin, after the FromAge and up to and including the ToAge
fico Numeric decreasing A credit score summarizing the borrower’s creditworthiness.
flag_fthb Categorical
Indicates whether the Borrower, or one of a group of Borrowers, is an individual who (1) is purchasing the mortgaged property, (2) will reside in the mortgaged property as a primary residence and (3) had no
ownership interest (sole or joint) in a residential property during the three-year period preceding the date of the purchase of the mortgaged property.
cd_msa Numeric Metropolitan Statistical Area
mi_pct Numeric
MORTGAGE INSURANCE PERCENTAGE (MI %) - The percentage of loss coverage on the loan, at the time of Freddie Mac’s purchase of the mortgage loan that a mortgage insurer is providing to cover losses
incurred as a result of a default on the loan.
cnt_units Numeric NUMBER OF UNITS - Denotes whether the mortgage is a one-, two-, three-, or four-unit property.
occpy_sts Categorical OCCUPANCY STATUS - Denotes whether the mortgage type is owner occupied, second home, or investment property.
cltv Numeric increasing
ORIGINAL COMBINED LOAN-TO-VALUE (CLTV) – In the case of a purchase mortgage loan, the ratio is obtained by dividing the original mortgage loan amount on the note date plus any secondary mortgage loan
amount disclosed by the Seller by the lesser of the mortgaged property’s appraised value on the note date or its purchase price.
dti Numeric
ORIGINAL DEBT-TO-INCOME (DTI) RATIO - Disclosure of the debt to income ratio is based on (1) the sum of the borrower's monthly debt payments, including monthly housing expenses that incorporate the
mortgage payment the borrower is making at the time of the delivery of the mortgage loan to Freddie Mac, divided by (2) the total monthly income used to underwrite the loan as of the date of the origination
of the such loan.
orig_upb Numeric decreasing ORIGINAL UPB - The unpaid balance of the mortgage on the note date, rounded to the nearest $1,000.
ltv Numeric increasing
ORIGINAL LOAN-TO-VALUE (LTV) - In the case of a purchase mortgage loan, the ratio obtained by dividing the original mortgage loan amount on the note date by the lesser of the mortgaged property’s
appraised value on the note date or its purchase price.
int_rt Numeric increasing ORIGINAL INTEREST RATE - The original note rate as indicated on the mortgage note.
channel Categorical CHANNEL - Disclosure indicates whether a Broker or Correspondent, originated or was involved in the origination of the mortgage loan.
ppmt_pnlty Categorical
PREPAYMENT PENALTY MORTGAGE (PPM) FLAG – Denotes whether the mortgage is a PPM. A PPMis a mortgage with respect to which the borrower is, or at any time has been, obligated to pay a penalty in the
event of certain repayments of principal.
prod_type Categorical PRODUCT TYPE - Denotes that the product is a fixed-rate mortgage. FRM= fixed rate mortgage
st Categorical PROPERTY STATE - A two-letter abbreviation indicating the state or territory within which the property securing the mortgage is located.
prop_type Categorical PROPERTY TYPE - Denotes whether the property type secured by the mortgage is a condominium, leasehold, planned unit development (PUD), cooperative share, manufactured home, or Single Family home.
zipcode Numeric Zip code of the building that was mortgaged
loan_purpose Categorical LOAN PURPOSE - Indicates whether the mortgage loan is a Cash-out Refinance mortgage, No Cash-out Refinance mortgage, or a Purchase mortgage.
orig_loan_term Numeric ORIGINAL LOAN TERM- A calculation of the number of scheduled monthly payments of the mortgage based on the First Payment Date and Maturity Date.
cnt_borr Numeric NUMBER OF BORROWERS - The number of Borrower(s) who are obligated to repay the mortgage note secured by the mortgaged property.
seller_name Categorical SELLER NAME - The entity acting in its capacity as a seller of mortgages to Freddie Mac at the time of acquisition.
servicer_name Categorical SERVICER NAME - The entity acting in its capacity as the servicer of mortgages to Freddie Mac as of the last period for which loan activity is reported in the Dataset.
flag_sc Categorical SUPER CONFORMING FLAG – For mortgages that exceed conforming loan limits with origination dates on or after 10/1/2008 and settlements on or after 1/1/2009

Demo

Turn Machine Learning into Business Learning

Predictive Analytics Don’t DO Anything
Having a prediction doesn’t improve results
Acting on the prediction does
Simply having a prediction is not enough to improve
results. Organizations must act on those predictions,
they must change the way they behave because of the
prediction, if that prediction is to have value.

Put Machine Learning in a Decision Context so you can ACT
Decision Modeling shows all the elements of a
decision and enables you to mix and match the
right technologies to automate enough of the
decision to take advantage of your predictive
analytic models and machine learning.
Automate Policies
Enforce Regulations
Encapsulate Expertise
Put Predictive Analytics to Work

Close The Loop To Learn About The Business
 Gather data
 What was decided
 Why was that decided
 How did that work out?
 Change the way you decide
Good Machine Learning platforms keep models learning
as new data is gathered. Add data about the decisions
you made, and how they worked out in business terms,
and you can understand your decision-making and turn
your machine learning into business learning.

Be a Hero
Make Your Enterprise a Predictive Enterprise
 DecisionsFirst
 Know what operational decision
you are improving
 Industrialize Analytics
 Machine Learning and automation
drive scale
 Business Learning
 Focus on business value and
business change

Act Now On Digital Decisioning
Enterprises waste time and money on
unactionable analytics
Digital decisioning can stop this insanity
It is the highest-value next step for … a
successful digital transformation
The Dawn Of Digital Decisioning: New Software Automates Immediate Insight-To-Action Cycles Crucial For Digital Business John R. Rymer and Mike Gualtieri
Forrester Recommendations:
Institute a culture of digital decisions-first design thinking
Think of digital decisioning as the nexus of business rules, data, analytics, and machine learning models.
One smarter, automated decision can be worth millions in terms of customer acquisition, retention, and/or operational efficiency

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3 Secrets to Becoming a Predictive Enterprise

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