Predictive Analytics 101 Current Trends in Predictive Modeling and Analysis Frank A. Alerte, Esq.* *We would like to acknowledge the following individuals for their contribution and valuable input in preparing this presentation: Doug Greer Senior Director, Alvarez & Marsal, LLC Ryan Purdy Principal and Consulting Actuary, Merlinos & Associates, Inc. Jason Masch Managing Director, Guy Carpenter & Company, LLC Robin Westcott Vice President, Government Affairs, American Association of Insurance Services 1 The views and opinions expressed therein are exclusively those of the author.
Agenda Overview of Predictive Analytics Insurance Applications for Predictive Analytics Compliance Considerations What s on the Horizon? 2
OVERVIEW OF PREDICTIVE ANALYTICS 3
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 65 11 53 84 16 27 33 48 4
What is the Basis for PA? DATA+ Machine Learning Predictive Analytics Subject Matter Expertise 5
How Did PA Come About? 3,500 BC: Written Language Math & Statistics 350 BC: Aristotle Classification & logic Computer Science 300 BC: Euclid s Elements Arithmetic & geometry 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 1950-60s: 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 www.wolframalpha.com Big data - Lessons from the leaders (by Economist Intelligence Unit, 2012) and http://techcrunch.com 6
Data Types and Sources Data Types Demographic Behavioral Data Sources Internal databases Third party vendors Social media 7
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) 8
INSURANCE APPLICATIONS FOR PREDICTIVE ANALYTICS 9
Insurance & Predictive Analytics Pricing / Underwriting Claims Function Marketing & Sales 10
Pricing / Underwriting NEED: How can we better identify and price risk? DATA: Claim and underwriting information. 11
Credit Scoring 12
What Model Might Be Used to do This? Commonly Used Statistical Model is called a Generalized Linear Model (GLM) 13
What is a GLM Doing? Historical pricing methods can run into problems in insurance! GLMs are versatile in function and scope. 14
Testing of Model Results Hold-out Data Set Testing Subsequent Period Testing Impact / Dislocation Testing 15
Testing of Model Results (continued) Policyholder Rate Impacts from Model Implementation 400 350 300 250 200 150 100 50 0-45% -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 16
What Else Can We Do With Predictive Analytics? 17
Claim Fraud Detection NEED: Better process for determining which claims are potentially fraudulent. DATA: Claims data Underwriting data Public information (?) 18
Reserve Setting NEED: Better process for establishing case reserves and estimates of the ultimate settlement value of claim. DATA: Claims data, Underwriting files, Demographic information 19
Strategic Planning NEED: Use predictive analytics, Cat models and other information to set strategy DATA: Claims Underwriting Cat Modeling Pricing Expense 20
Marketing and Insurance NEED: Identify better targets for sales and increase effectiveness of marketing. DATA: Quote/bind data Underwriting data 21
Target Marketing Sell to Buyers Fill the Holes 22
Increasing Retention Better Understanding Business Effects of Pricing Changes Develop Retention Marketing Adverse Selection 23
COMPLIANCE CONSIDERATIONS 24
Compliance Considerations Compliance Considerations Marketing Underwriting / Pricing Claims Unfair trade practices Discrimination Privacy Record Retention 25
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 tactics The use of social media is subject to state insurance laws that govern unfair trade practices. 26
Discrimination Issues What information is being used? Is it protected class information? Is the net effect discriminatory even if not intended? 27
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? 28
Record Retention Requirements for Advertising Compliance Development of protocol and retention of specific factors used to establish marketing. Appropriate disclaimers on push marketing. 29
Sliding Issues Trying to improperly move a customer to purchase another product. Parameters on what is appropriate and what may cross the line. Computer models and results may be important in market conduct exams and litigation. 30
Pricing / Underwriting: Key Compliance Questions The 2013 Florida Statutes 627.062 Rate standards. (1) The rates for all classes of insurance to which the provisions of this part are applicable may not be excessive, inadequate, or unfairly discriminatory. 31
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? Is the 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? 32
Pricing / Underwriting: Transparency for Regulators Can the regulator see how the model works? Can the regulator understand the information obtained and how the information is used? Can the regulator be assured the information will be used consistently and uniformly? 33
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? 34
Claims Compliance Issues: Legitimacy of Factors Used Market Conduct concerns Litigation exposure bad faith and class action Affecting individual adjuster judgment on evaluation and disposition of claims 35
Claims Compliance Issues: Public Info / Social Networks for Fraud Insurance companies collect information to determine if claims are legitimate Can use Facebook instead of private investigator 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. 36
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? 37
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 38
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 39
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 40
Surplus Lines Market Data Element Frequency and Severity of Risks Regulatory Constraints 41
Questions? 42