Robert D. Helfand Carlton Fields Jorden Burt P.A. (860)

Transcription

1 Robert D. Helfand Carlton Fields Jorden Burt P.A. (860)

2 P&C insurers expect big data use in many key business areas will more than double in the next two years. ID/Insights/Newsletters/Global/emphasis/2016/emphasis insurance-big-datacan-improve-business

3 What can Big Data do for P&C insurers? What litigation exposures does Big Data create?

4 Both actuarial science and data science apply mathematical and statistical methods to assess risk Data science has developed because of Increased computational power Scalable data storage New types of database Data science involves New sources of data Used in new ways

5 Telematics Clickstream data (tracking customers use of your websites) Other customer interactions Data from smart products Social media Open-source databases (e.g. data.gov) Third-party sources (credit agencies, data brokers) In-house data in legacy systems

6 Actuaries test theories based on human reasoning about causal relationships They work with data that appear relevant to the theories they want to test E.g., Experienced drivers have fewer accidents

7 Data scientists take all available data and look for correlations, which can reveal unexpected causal relationships E.g., Purchasers of premium cat food make fewer property claims

8 Data scientists don t need to explain why the correlation exists There doesn t have to be an underlying theory of causation E.g., High credit scores correlate with fewer auto claims Correlation Causation, but correlation still counts Current actuarial science also depends on the consistency of correlations, not on proof of causal relationships

9 Problems? Different data sources have different levels of accuracy and validity Choosing data sources on the basis of what s available, rather than what s relevant, can skew results

10 Extends the principles of data science Teaching chess vs. learning chess Data scientists define an end result (winning a game) and direct the computer to modify its algorithms, based on the actual results of real and simulated games The computer responds to correlations and relationships the programmer hasn t anticipated DeepMind and AlphaGo

11 Problems? Machine learning reduces transparency In ratemaking, only the machine knows what the variables are But machine learning can supplement other methods, to create new variables eliminate irrelevant variables identify other shortcomings in a rating model

12 Claim Benchmarking Using predictive analytics to improve reserving and forecasting Supplementing Reporting Automatically collecting information on claims from third-party sources Activity Optimization Using predictive analytics to determine which types of professionals to assign to a claim Premium Audits Using data from outside sources to supplement records provided by insureds

13 Fraud Prevention Using predictive analytics to identify claims that might warrant fraud investigations Litigation Assessment Using analytics to predict which claimants are likely to retain counsel and assign those cases to senior adjustors Commercial Underwriting Using data about small business owners to underwrite business policies Sales and Marketing Price Optimization

14 Theories used to challenge automated systems that pre-dated Big Data : Unreasonable valuations Auto insurers must pay the reasonable cost of certain medical services Automated systems allegedly fail to pay reasonable amount Strawn v. Farmers Ins. Co., 350 Or. 521 (2011) Due to statutory presumption of reasonableness, jury could find insurer breached its contract in a class action without a showing that even one payment was unreasonable fa-4e66-a18c-a8e3baf028d4

15 Theories used to challenge automated systems that pre-dated Big Data : Failure to conduct prompt and reasonable investigation Assumptions underlying automated analysis of claims were allegedly unreasonable Strawn In re Farmers Med-Pay Litig., 229 P.3d 551 (Okla. Ct. App. 2009)

16 Theories used to challenge automated systems that pre-dated Big Data : Fraud Insurance contract promised reasonable payment; insurer knowingly implemented an unreasonable payment system Bad Faith Failure to offer reasonable amount breaches duty of good faith to first-party insureds Note that reasonable amount is sometimes what a jury would award Hensley v. Computer Science Corp. ( Colossus )

17 Under all these theories, plaintiffs tried to establish intent or bad faith by showing: Corporate policy of reducing costs Strawn: Claim office was instructed to realize PIP dollar savings Nardelli v. Metropolitan Group, 230 Ariz. 592 (2012) b4e-e10e-4b3e-bd00-e1d0482b8b4d

18 Under all these theories, plaintiffs tried to establish intent or bad faith by showing: Vendors promising savings in marketing Strawn: vendor sold system as a cost containment software program Hensley: vendor promised 15% savings in first year No role for adjusters independent judgment Strawn: adjusters were downgraded if they ignored UCR reductions

19 Challenges to systems used for underwriting: Unfair Discrimination Pricing not based on expectations of loss Disparate Impact Disproportionate burden on protected classes Viens v. America Empire Surplus Lines Ins. Co. (D. Conn. June 23, 2015); Jones v. Travelers Cas. Ins. Co. of Am. (N.D. Cal. May )

20 Failure to Pay Reasonable Amount/ Bad Faith Systems to Supplement Reporting Does insurer s ability to collect data quickly give it an unfair advantage in early negotiations? Per LexisNexis, more early data produces 15-25% lower severity payments on BI claims, and 25-49% lower attorney involvement One vendor: Settlement Optimization [T]he cost of a claim is nearly 40 percent greater if the claimant delays reporting the claim by as few as four days

21 Failure to Pay Reasonable Amount/Bad Faith Litigation Assessment Will insurers offer settlements earlier to claimants deemed likely to retain counsel? Is it bad faith to offer less than what a lawyer is likely to demand for a first-party bodily injury claim?

22 Failure to Conduct Reasonable Investigation Claim Benchmarking and Activity Optimization Benchmarking systems will assign some claimants to inexperienced adjusters and/or direct specialists away from those claimants If the claim is mishandled, the claimant has an incentive to attack the benchmarking system Variables developed through predictive analytics and machine learning may be ripe targets Result might be extra-contractual liability, punitive damages or class action exposure

23 Disparate Impact Fraud Prevention Will claims submitted by disadvantaged groups be flagged as potentially fraudulent at higher rates than others? Will flags delay resolution of those claims? Commercial Underwriting Will use of credit scores of small business owners make it harder for members of disadvantaged groups to get insurance for their companies? Price Optimization

24 Consumer advocates fear special disparate impact risks associated with Big Data Target variables can be defined in a way that incorporates unconscious biases Data on disadvantaged communities is more likely to be inaccurate or incomplete Accurate analyses can reflect results of historic discrimination and help perpetuate those results

25 Privacy and Data Regulation Collecting, using and/or sharing customer data exposes insurers to the same risks as other businesses Are you protecting customer data? Does your website advise users about data collection? Are you using regulated types of data? Telematics and HIPAA One vendor advises insurers to use providers that [o]ffer non- FCRA data solutions, to minimize regulatory requirements. On-line interactions create other types of exposure E.g., inadequate disclosures

26 Train your professionals and document your training Warn professionals against potential abuses Monitor results Insulate the relevant operations Don t press claims handlers for savings Don t give machines the last word Create opportunities for policyholders to express concerns and for handlers to respond Don t let vendors marketing speak for you State your legitimate expectations in the contract Don t take legal advice from vendors

27 Don t Be Afraid! Don t Apologize!

28 New data tools serve legitimate and important functions Your company has every right to use them

29 Questions? Robert D. Helfand Carlton Fields Jorden Burt P.A. (860)