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3 Secrets to Becoming a Predictive Enterprise


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Join Decision Management Solutions, Velocity Business Services and Datarobot as we discuss the importance of operational decisions, industrialized predictive analytics and business learning in creating a predictive enterprise.

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3 Secrets to Becoming a Predictive Enterprise

  1. 1. © 2019 Decision Management Solutions Sponsored by Three Secrets to Creating a Successful Predictive Enterprise
  2. 2. Velocity Business Solutions… …is a Hong Kong based data analytics company transforming data into actionable insights. We enable you to prepare, blend and analyse your complex data and present it in a way that’s easy to understand, giving you complete visibility into your entire business operations to empower everyone to make informed decisions faster.
  3. 3. D A T A V I S U A L I Z A T I O N D A T A S C I E N C E M A C H I N E L E A R N I N G Velocity Business Solutions focus on… ✓ Simple Drag & Drop Workflow-based approach ✓ Flexible & Repeatable ✓ Advanced Analytics ✓ Associative Engine ✓ Governed Self-Service Analytics ✓ Smart Visualization and Collaboration ✓ Automated Machine Learning Platform ✓ Fast, Transparent & Accurate ✓ Easy to Deploy D A T A P R E P A R A T I O N & A N A L Y T I C S
  4. 4. © 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
  5. 5. 9 AGENDA © 2019 Decision Management Solutions 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?
  6. 6. © 2019 Decision Management Solutions 10 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.
  7. 7. © 2019 Decision Management Solutions 11 Three Secrets to Success As a Predictive Enterprise Focus on Front-line Operational Decisions Industrialize Predictive Analytics Turn Machine Learning into Business Learning
  8. 8. 12© 2019 Decision Management Solutions Focus on Front-line Operational Decisions
  9. 9. 13© 2019 Decision Management Solutions From presenting data for analysis … Many organizations focus on presenting data to people so they can do analysis or monitor progress.
  10. 10. 14© 2019 Decision Management Solutions … 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.
  11. 11. 15© 2019 Decision Management Solutions “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
  12. 12. © 2019 Decision Management Solutions 16 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.
  13. 13. © 2019 Decision Management Solutions 17 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.
  14. 14. © 2019 Decision Management Solutions 18 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.
  15. 15. © 2019 Decision Management Solutions 19 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?
  16. 16. © 2019 Decision Management Solutions 20 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
  17. 17. 21© 2019 Decision Management Solutions Industrialize Predictive Analytics
  18. 18. . ©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:
  19. 19. . ©2019 DataRobot, Inc. – All rights reserved “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:
  20. 20. . ©2019 DataRobot, Inc. – All rights reserved Case Study Lifetime Customer Value Freddie Mac Mortgage Data
  21. 21. . ©2019 DataRobot, Inc. – All rights reserved 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:
  22. 22. . ©2019 DataRobot, Inc. – All rights reserved 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.
  23. 23. . ©2019 DataRobot, Inc. – All rights reserved 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.
  24. 24. . ©2019 DataRobot, Inc. – All rights reserved But The Data is Truncated… 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
  25. 25. . ©2019 DataRobot, Inc. – All rights reserved But The Data is Truncated… 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.
  26. 26. . ©2019 DataRobot, Inc. – All rights reserved Solution
  27. 27. . ©2019 DataRobot, Inc. – All rights reserved 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
  28. 28. . ©2019 DataRobot, Inc. – All rights reserved Understanding the Data
  29. 29. . ©2019 DataRobot, Inc. – All rights reserved 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
  30. 30. . ©2019 DataRobot, Inc. – All rights reserved 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
  31. 31. . ©2019 DataRobot, Inc. – All rights reserved Demo
  32. 32. 36© 2019 Decision Management Solutions Turn Machine Learning into Business Learning
  33. 33. © 2019 Decision Management Solutions 37 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.
  34. 34. © 2019 Decision Management Solutions 38 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
  35. 35. © 2019 Decision Management Solutions 39 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.
  36. 36. © 2019 Decision Management Solutions 40 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
  37. 37. © 2019 Decision Management Solutions 41 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
  38. 38. 42© 2019 Decision Management Solutions Your Questions Enter questions for the Q&A here
  39. 39. Thank You For more on Decision Management, go to: © 2019 Decision Management Solutions