Big Data and Price Optimization in General Insurance

Session 171 PD, Big Data and Price Optimization in General Insurance

Moderator: Ann Weber

Presenters:

Amy Bach, J.D. Angela Nelson Mark Smith

Mary Jane Wilson‐Bilik, J.D.

SOA Antitrust Disclaimer SOA Presentation Disclaimer

Big Data and Price Optimization in General InsuranceOCTOBER 26, 2016

Panelists

Moderator:Ann WeberGovernment Affairs DirectorSociety of Actuaries

Panelist:Amy Bach, J.D.Executive DirectorUnited Policyholders

Panelist:Angela L. Nelson, AMCMDirector, Division of Market Regulationand Chief Industry LiaisonMissouri Dept. of Insurance

Panelist:Mark Smith, CPCU, AIS, APIAsst. Vice President National AffairsGovernment Relations DepartmentInsurance Services Office, Inc.

Panelist:Mary Jane Wilson‐Bilik, J.D.PartnerSutherland Asbill & Brennan LLP

Data mining’s impact on consumers in the context of insurance underwriting,

pricing and sales

Amy Bach, Esq., Executive DirectorUnited PolicyholdersEmail: amy.bach@uphelp.orgWebsite: www.uphelp.org

October 26. 2016Society of ActuariesLas Vegas, Nevada

Dramatic yes, but…

• Legitimate cause for concern on many fronts

• Equal access to data seems a good approach

Unhealthy market segmentation

- Low/moderate income consumers easy to identify- Less attractive to insurers - Unfavorable pricing (CFA alleges low income

households are paying more)

How Big Data Enables Economic Harm to Consumers, Especially to Low-Income and Other Vulnerable Sectors of the Population, Nathan Newman, J.D., Federal Trade Commissionhttps://www.ftc.gov/system/files/documents/public_comments/2014/08/00015-92370.pdf)

Potential underground use of prohibited rating factors

• Ethnicity/Race• Political affiliation• Religion• Hobbies/Interests unrelated to risk of loss• Economic status

Insurance is different

• Mandatory and de facto mandatory purchase means free market competition is insufficient to protect policyholders

• Less “desirable” customers still have to buy insurance and deserve to pay a fair price

Benefits of big data skew heavily in insurers’ favor:

Access to much more detailed info about the risks they’re undertaking

Insurers can cherry pick at a granular level

Insureds don’t have equivalent new tools to compare quality of coverage/policies and performance of insurance companies

Unequal access to information

• Regulators can’t afford to buy the data

• Are smaller insurers that can’t either unfairly disadvantaged vis a vis risk distribution.

• Unfair competition?

Undermining effective regulatory review of rate filings?

• Regulators can’t penetrate to determine whether there are prohibited factors embedded in the underlying models

How much can we charge before the customer walks?

• McKinsey & Co: “Harnessing the flood of data available from customer interactionsallows companies to price appropriately—and reap the rewards.” See:

http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/using-big-data-to-make-better-pricing-decisions

How large a deductible can we add before the customer balks?

How much coverage can we carve out via exclusions before a regulator notices?

How can we harm our competitors with what we learn about their customers?

Price Optimization• Seems unfair when used in the insurance context – Whether

or not someone comparison shops is not a fair predictor of risk.

• Penalizes loyal customers– Who think they’re getting a benefit by being loyal and don't

know they may be overpaying– Who may have bundled to get a better deal but aren’t

getting one

State prohibitionsMaryland, Ohio, Washington, Vermont, Indiana, Colorado, California, Connecticut, Delaware, Minnesota, Montana, Missouri, Pennsylvania, and Florida, and Rhode Island and other states have restricted or banned the practice through administrative bulletins. Legislation under consideration in Illinois, Oklahoma, and Montana.

• Deem price optimization to be not a legitimate rating factor/no actuarial value as a predictor of risk.

Privacy concerns:• Pay as you drive products were tagged with privacy

concerns from the get-go.

• Consumers routinely give what they consider to be benign data to companies (e.g, social media apps) but don’t realize that it gets sold to third parties, including insurers

• Insurance is different b/c you have to buy it

The new redlining?• An increase in redlining by prohibited characteristics (race,

ethnicity, income) buried in models. Insurers always prefer clients who are wealthy and have more than one major asset to insure

• Consumers advocates are pushing for FIO data call on auto ins affordability – national data collection would be beneficial but state

regulators resisting Fed encroachment

NAIC whitepaper: http://www.naic.org/documents/committees_c_catf_related_price_optimization_white_paper.pdf

Wells Fargo – an illustration of how TMI can lead to abuses

• Data-driven targets/goals (8 accounts per customer) led to abuse of consumer personal information

The art of underwriting

• Flows with humanity as it evolves

• Impacted by social media

• Based on expertise of experts who know people and the math of probabilities too

• Very important to preservation of healthy risk transfer through insurance

Amy Bach, Esq.

• Co-founder of United Policyholders

• Career insurance consumer advocate

• Former insurance litigator, legislative staffer

• Member, Federal Advisory Committee on Insurance

• 7 term NAIC consumer representative

Big Data and Price Optimization:The NAIC White Paper and Beyond Society of Actuaries Annual MeetingLas Vegas, NV

Mary Jane Wilson-Bilik, PartnerOctober 26, 2016

Presenter

Mary Jane Wilson-BilikPartner, SutherlandWashington, DC202.383.0660mj.wilson-bilik@sutherland.com

Price Optimization: Regulators Respond

NAIC White Paper on Price Optimization- Adopted by the Property and Casualty Insurance (C) Committee at

the NAIC Fall Meeting in November 2015 Likely to remain controversial with advances in vehicle telematics

18 State have issued bulletins on price optimization - 12 bulletins have required insurers to revise their filings- Many have clarified what is NOT prohibited, including: Use of sophisticated data analysis Certain pricing practices applied on a group basis

Ongoing Focus on Big Data at the NAIC- Big Data (D) Working Group

Overview

The NAIC Price Optimization White Paper

The advent of sophisticated data mining tools and modeling allows for more detailed quantitative info on aspects of rate setting traditionally left to judgment or anecdotal evidence PO = Process of maximizing or minimizing a business

metric using sophisticated tools and models to quantify business considerations: marketing goals, profitability and policyholder retention Ratebook; Individual and Hybrid Optimization

How does the NAIC define price optimization?

The NAIC White Paper on Price Optimization

PO may improve rate stability and lower insurer’s long-term costs But voluminous rate information is difficult for regulators to distill Regulators do not have data and tools necessary for independent

evaluation Critics charge that PO is unfairly discriminatory

Insurers argue PO is a technological improvement to current practices - Also argue that criticism is aimed at “individual price optimization –

not ratebook optimization” used in setting rates

What potential benefits and problems with price optimization does the NAIC identify?

Regulators should address price optimization through state laws prohibiting rates that are “excessive, inadequate or unfairly discriminatory” Rating plans should be derived from sound actuarial

analysis and be cost-based Two customers with same risk profile should be charged

the same premium for the same coverage Capping and transitional rules may be appropriate

What policy recommendations does NAIC set forth?

Insurance rating practices that adjust the current or actuarially indicated rates should not be allowed when practice cannot be shown to be cost-based The following practices are inconsistent with statutory

requirement that rates not be unfairly discriminatory: Price elasticity of demand Propensity to shop for insurance Retention adjustments at the individual level and A policyholder’s propensity to ask questions or complain

Policy recommendations, continued:

Rating plans in which insureds are grouped into homogeneous groups should not be so granular that resulting classes have little actuarial or statistical reliability Use of sophisticated data analysis to develop finely tuned

methodologies with a multiplicity of possible rating cells is not, in and of itself, a violation of rating laws, so long as classes and factors are cost-based

Policy recommendations, continued:

Issue a bulletin (18 states) Enhance requirements for rate filings

Define constraints: e.g., only apply models to specific class sizes – not so small as to be at the individual insured level or small group

Analyze models to ensure model adheres to state law and actuarial principles

What options for state regulatory responses does the White Paper present?

1SLIDE

Audience: October 4, 2016

Big Data: Regulator Perspective

Angela NelsonDirector, Market Regulation Division

and Chief Industry LiaisonMissouri Department of Insurance

2SLIDE

Audience: October 4, 2016 2

Regulator View on Big DataLack of transparency

FilingsMarket Conduct

Difficult to maintain regulatory balanceEncourage efficiency in underwritingAdequate consumer protection

Struggle with causality“P-Hacking”

3SLIDE

Source: http://www.tylervigen.com/spurious-correlations

4SLIDE

5SLIDE

Big Data Working GroupAddressing regulatory challenges

Regulatory transparency• Third party models

Disparate treatmentSegmentationResources for DOIs for complex model reviewsCoordinated regulatory reviews

Session 171 Panel Discussion: Big Data and Price Optimization in General Insurance

Society of Actuaries Annual Meeting 2016October 26, 2016Mark Smith, CPCU

Agenda•Evolution of data used for insurance

•Data throughout the product lifecycle

•Benefits of Big Data products

•Big Data Challenges

Evolution of data used for insurance

How did the data get so “big”?

5All pictures are from istockphoto

• Evolution of computing power and storage capabilities

• Collection and digitization of all different types of data

• Traditional and non-traditional

• Correlation revolution• GLM and post GLM analysis

•Looking for the next big thing

• Increased granularity of datasets and rating plans

• Matching losses to exposure

• Additional non-traditional variables that are highly correlated with risk

• Use of predictive variables in all parts of the insurance cycle

Looking for the next “big” thing

•Expansion of insurance data sources and use of external data sources

•Expansion of data set size and granularity

•Models to find correlations using non-traditional data

•Regulator uncertainty in understanding and reviewing, looking for help and reassurance

•Consumer group focus

“Big data” – Why now?

Data throughout the product lifecycle

•Underwriting, reserving and marketing tools

•Lifetime value of policy, cross-selling

• Investment and portfolio analysis

“Big data” informing decisions

•Risk Analyzer

•Telematics

•Fireline

•Xactimate

•Decision Net

•Cargo net

ISO products

Consumer Benefits of Big Data

•Emerging markets

•Precision marketing

•Risk segmentation

•Claims settlement

•Underwriting

“Big data” benefits

•Terrorism

•Flood

•Cyber liability

•Pet insurance

Emerging markets

•Customer experience

• Innovative services

•New business models

•Cross selling and up selling

•Product personalization

Precision marketing

• Increased market share competition

•Decreased residual market share

•Specialization

•More clearly defined groupings

Risk Segmentation

•More accurate pricing

•Expanded market availability

• Increased objectivity

•More efficient, less costly

Underwriting

•Expedite claims payments

•Reduce investigation and litigation expense

•Detect and prosecute fraud

•Predictive value

Claims

Challenges

•Confidentiality

•Centralized review and regulation

•Constraints on innovation

•Blurred regulatory responsibilities

•Cost and compliance

“Big data” challenges

Questions?

Big Data and Price Optimization in General Insurance

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