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1 WEBINAR Using Predictive Analytics to Avoid Compliance Pitfalls Co-Sponsored by: AICP Mid Atlantic Chapter Presenters: Richard J. Fidei, Esq, Colodny, Fass, Talenfeld, Karlinsky, Abate & Webb Ryan Purdy, FCAS, MAAA, Principal and Consulting Actuary, Merlinos & Associates, Inc. Doug Greer, Senior Director with the Insurance Advisory Services practice of Alvarez & Marsal

2 SAI Global at a Glance Benchmarking Cultural Surveys Best Practices 500+ Courses ,000+ Translations Customization Assessments Code & Policy Development Advisory Services Learning Solutions Optics Certification Storytorials SM Ethical Moments Policy Management Guidance Repository Virtual Evidence Room Certification GRC Software Solutions Hotline Incident Management Audits ERM Assessments Dashboards & Reports Third Party Risk Management SOX

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4 Presenters Ryan Purdy, FCAS, MAAA Principal and Consulting Actuary Merlinos & Associates Rich Fidei, Esq. Shareholder Colodny, Fass, Talenfeld, Karlinsky,Abate & Webb Doug Greer, CTP, CIRA Senior Director Alvarez & Marsal Insurance Advisory Services 4

5 Agenda Overview of Predictive Analytics Insurance Applications for Predictive Analytics Compliance Considerations What s on the Horizon? 5

6 OVERVIEW OF PREDICTIVE ANALYTICS 6

7 What is Predictive Analytics? Forecasting? Strategic planning? Actuarial science? Business analysis that produces a predictive score for each customer, prospect, claim, etc to guide how to treat each of them individually

8 What is the Basis for PA? DATA+ Machine Learning Predictive Analytics Subject Matter Expertise 8

9 How Did PA Come About? 3,500 BC: Written Language Math th& Statisticsti ti 350 BC: Aristotle Classification & logic 300 BC: Euclid s Elements Computer Arithmetic & geometry Science Cheaper 1930s: Ronald storage Fisher Putting math into statistics Faster retrieval 1936: Alan Turing More Universal computing computation power 1940s: Digital Computers Automating computation Lots of Data s: Artificial Intelligence Making computers intelligent 1967: DIALOG Retrieving information from anywhere 1970s: Relational Databases Making relations computable 1980s: Neural Networks Emulating the brain 1989: The Web Collecting the world s information 1994: Yahoo! Hierarchical directory of the web 1998: Google An engine to search the web 2004: Facebook Capturing the social network Source: Excerpted from Big data Lessons from the leaders (by Economist Intelligence Unit, 2012) and 9

10 Data Types and Sources Data Types Demographic Behavioral Data Sources Internal databases Third party vendors Social media 10

11 General Framework Business Understanding Data Understanding & Model Constraints Deployment DATA Data Preparation Testing / Modeling Evaluation Source: Adapted from the CRISP DM model (Cross Industry Standard Process for Data Mining) 11

12 The Language of PA Data Analytics; R; Response Rate Modeling; PMML; SAS; Tableau; Statistical Modeling; Statistica; Conversion Modeling; SPSS; Tiberius; Splus; Python; Weka; Uplift Modeling; TreeNet; Hadoop; CART; Spotfire; KXEN; Predictive Modeling; Insightful Miner; WITNESS Miner; Unica Affinium; i Machine Learning; MATLAB; ALA GGPlot; DataLab; Persuasion Modeling; DBMiner; Starlight; NETMAP; JMP; Neural Nt Networks; K.wiz; Recommendation; KnowledgeMiner; Miner3D Good source of info: 12

13 Types of Predictive Models parametric regression models: ordinary / generalized / robust regression models;neuralnetworks;partialleastsquares;projection pursuit regression; multivariate adaptive regression splines; principal component regression; sparse/penalized models: ridge regression; the lasso; the elastic net; generalized linear models; partial least squares; nearest shrunken centroids; logistic regression; kernel methods: support vector machines; relevance vector machines; least squares support vector machine; Gaussian processes; trees/rule based models: CART; C4.5; conditional inference trees; node harvest, Cubist, C5.0; ensembles: random forest; boosting (trees, linear models, generalized additive models, generalized linear models, others); bagging (trees, multivariate adaptive regression splines); prototype methods: k nearest neighbors; learned vector quantization; discriminant analysis: linear; quadratic; penalized; stabilized; sparse; mixture; regularized; stepwise; flexible; others: naivebayes; Bayesian multinomial probit 13

14 PA in Action (Non Insurance) Source: Siegel, Eric. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die Wiley 14

15 The Power of Prediction U.K. bank 10MM annual profit via improved retention & response U.S. Mortgage Lender $500MM+ first year profit from mortgage prepayment prediction Managed Behavioral ~200% ROI by predicting Health Provider readmissions & reducing by 30% Major P&C Carrier 271% improvement in prediction of injuries in auto accidents based solely onvehicle characteristics 15

16 INSURANCE APPLICATIONS FOR PREDICTIVE ANALYTICS 16

17 Insurance & Predictive Analytics Pricing / Underwriting Claims Function Marketing & Sales 17

18 Pricing / Underwriting NEED: How can we better identify and price risk? DATA: Claim and underwriting information. 18

19 Credit Scoring 19

20 What Model Might Be Used to do This? Commonly Used Statistical Model is called a Generalized dlinear Model (GLM) 20

21 What is a GLM Doing? Historical pricing methods can run into problems in insurance! GLMs are versatile in function and scope. 21

22 Testing of Model Results Hold out Data Set Testing Subsequent Period Testing Impact / Dislocation Testing 22

23 Testing of Model Results (continued) Policyholder Rate Impacts from Model Implementation % 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% # Policyholders 23

24 What Else Can We Do With Predictive Analytics? 24

25 Claim Fraud Detection NEED: Btt Better process for determining which claims are potentially fraudulent. DATA: Claims data Underwriting data Public information i (?) 25

26 Reserve Setting NEED: Btt Better process for establishing tblihi case reserves and estimates of the ultimate settlement value of claim. DATA: Claims data, Underwriting files, Demographic information i 26

27 Marketing 27

28 Marketing and Insurance NEED: Identify better targets for sales and increase effectiveness of marketing. DATA: Quote/bind data Underwriting data 28

29 Target Marketing Sell to Buyers Fill the Holes 29

30 Increasing Retention Better Understanding Business Effects of Pricing Changes Develop Retention Marketing Adverse Selection 30

31 COMPLIANCE CONSIDERATIONS 31

32 Compliance Considerations Compliance Considerations Marketing Underwriting / Pricing Claims Unfair trade practices Discrimination Privacy Record Retention Suitability Sliding 32

33 Unfair Trade Practices Unfair trade practices in insurance have existed as long as the industry of insurance itself. Unfair trade practices laws and regulations are consumer protection mechanisms that traditionally focus on two aspects: Unfair claims tactics Unfair marketing/advertising kti /d tii tacticsti The use of social media is subject to state insurance laws that govern unfair trade practices. 33

34 Discrimination Issues What information is being used? Isitprotected classinformation? Is the net effect discriminatory even if not intended? 34

35 Privacy Issues & Policies Insurers are subject to Gramm Leach Bliley, the Fair Credit Reporting Act, and the Fair and Accurate Credit Transactions Act. Privacy policies set forth the terms by which the company will handle the personal information collected from consumers. Key compliance questions include: What personal information is collected? How is it being used? Are appropriate safeguards applied to protect it? Is information being shared in violation of privacy policies? 35

36 Record Retention Requirements for Advertising Compliance Development of protocol and retention of specific factors used to establish marketing. Appropriate disclaimers on push marketing. 36

37 Sliding Issues Trying to improperly move a customer to purchase another product. Parameters on what htis appropriate it and what htmay cross the line. Computer models and results may be important in market conduct exams and litigation. 37

38 Suitability Issues Possible exposure since broader based computer learning created solicitation without specific evaluation of individual s circumstances. Follow up evaluation necessary. Potential exposure to market conduct issues on advertisingand and solicitation directed to inappropriate audience. 38

39 Pricing / Underwriting: Key Compliance Questions How was the book of business actively composed? What information does the black box model obtain? How does the model use this information? Isthe info consistent with underwriting guidelines? Does the computer acquire new info over time? Can you verify that the computer obtained and used only the information represented? What is the impact on pricing and underwriting? 39

40 Pricing / Underwriting: Transparency for Regulators Can the regulator see how the model works? Canthe regulator understandthe the information obtained and how the information is used? Can the regulator be assured the information will be used consistently and uniformly? 40

41 Pricing / Underwriting: Use of Social Media There is little to no specific regulation regarding use of information obtained through social media. Does social media accurately predict behavior? What can of worms is opened up? 41

42 Claims Compliance Issues: Legitimacy of Factors Used Market tconduct concerns Litigation exposure bad faith and class action Affecting individual adjuster judgment on evaluation and disposition of claims 42

43 Claims Compliance Issues: Public Info / Social Networks for Fraud Insurancecompanies companies collect information to determine if claims are legitimate Can use Facebook kinstead of private investigator t to see physical health (i.e. day in the life ) Example: A woman was on medical leave for depression. Her disability benefits stopped after an insurance employee found photos on her Facebook page of her at the beach and hanging out at a local pub. 43

44 Claims Compliance Issues: Other Considerations Review by human vs. machine Misinterpretation of photos or status updates Fake social media accounts Human error What other risks do we run into? 44

45 Claims Compliance Issues: Validation of Settlement Amount (2010 Example $10MM Settlement) Regulatory settlement related to injury claims from automobile accidents The issue was the carrier s use of a software program that was intended to standardize the claims process by providing consistent valuation of bodily injury claims for settlement offers. Specific issues included: Inconsistencies in the carrier s management and oversight of the claims software across its different claims handling regions Failure to account for medical bills, lost wages or job loss Inability to consider pain and suffering of an injured person 45

46 Claims Compliance Issues: Validation of Settlement Amount (2010 Example Continued) Under the settlement: The carrier must ensure that claims are handled consistently across all of its claim handling regions The carrier paid $10MM to 45 states to train state examiners in the use of the claims adjusting software Claimants will be better informed as to how the carrier arrives at a claim offer 46

47 What s on the Horizon? Telematics for the Masses Focus on Human Behavior New Data Sources Expansion of Social Media Mining Incremental vs. Disruptive Innovation Chief Analytics Officers 47

48 Questions? FOR MORE INFORMATION Ryan Purdy Merlinos & Associates Rich Fidei Colodny, Fass, Talenfeld, Karlinsky, Abate & Webb Doug Greer Alvarez & Marsal Insurance Advisory Services

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