Predictive Analytics: Achieving Greater Decision Accuracy, Better Risk Segmentation, and Greater Profitability

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Predictive Analytics:

Achieving Greater Decision Accuracy, Better

Risk Segmentation, and Greater Profitability

Lamont D. Boyd, CPCU, AIM

Insurance Market Director FICO Scoring Solutions

[email protected]

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Agenda

» Overview/Research Findings

» 128 patents – predictive analytics and decision

management

» Insurance Scores

» Credit-Based Insurance Scores

» Property Risk Scores

» Safe Driving Scores

» Custom Predictive Analytics/Custom Modeling

» Risk Segmentation for Marketing, Underwriting,

Product Development, Pricing, Claims…..

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Predictive Analytics: Achieving Greater Decision

Accuracy, Greater Profitability

Towers Watson Survey (4Q/2011)

» Percentage of insurers currently using or planning to use

predictive modeling for underwriting and/or pricing

» 88% - Personal Auto » 83% - Homeowners » 73% - Commercial Property/CMP/BOP » 72% - Workers’ Comp » 63% - Commercial Auto » 52% - General Liability

» Why the growth in predictive modeling?

» 85% - Rate accuracy

» 69% - Loss ratio improvement » 69% - Profitability

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Predictive Analytics: Achieving Greater Decision

Accuracy, Greater Profitability

SMA (Strategy Meets Action) Research (2012)

» 75% of P&C insurers will increase spending on analytics over the

next three years

» 60% currently using and 25% implementing analytics in underwriting to better understand and manage risks

» 50% currently using and 22% implementing analytics for distribution management/agent performance

» 40% using and 15% implementing analytics for claim fraud detection and prevention

» Personal lines carriers/units more aggressively using and pursuing predictive analytics than commercial lines counterparts

» Noted barriers to capitalizing on predictive analytics

» Lack of strategy

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Agenda

» Insurance scores: CBIS and property risk scores

» Custom models: GAM versus GLM

» Premium leakage and pre-claims fraudulent

detection at underwriting

detection activity at

underwriting

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Credit-Based Insurance Scores

» Key industry success!

» FICO introduced Credit-Based Insurance Scores (CBIS) to the

industry in 1993

» CBIS scores currently used by ~95% of all auto and home

insurers

» FICO CBIS scores are monitored for effectiveness re risk

segmentation and amended as required by regulatory/legislative

mandates

» Current generation FICO CBIS scores

» Experian/Fair Isaac Insurance Score 2.0 (via Experian/LexisNexis)

» InScore®_{3.0 (via Equifax)}

» Fair Isaac®_{Insurance Risk Score 2.0 (via TransUnion)}

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Update:

FICO CBIS

in Current Economic Environment

Key Question:

Have FICO CBIS risk segmentation performance/score

distributions changed over time?

» Ongoing FICO and client analysis reveals continuing high levels

of performance

» FICO CBIS score distributions remain essentially the same

» Some consumers are seeing lowering scores

» Economic climate-driven factors

» Majority of consumers are seeing slightly rising scores

» More cautious credit management practices

» No late payments

» Opening fewer accounts

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CBIS Usage

» CBIS usage provides consistent link in all areas that require

strategic decisions

Decision Engine Command Center

Credit-Based Insurance Score

Underwriter Management

Field Office Management Production Source Management

Customer Acquisition

New Business Underwriting Tier Placement

Renewal Underwriting External Info Purchase

Pricing

Cross-Selling

CRM

Billing / Reinstatement Fraud Detection

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Acquisition/Marketing – Client Benefits

» New Customer Acquisition/Marketing Strategy

» Focused application

» Cost-effective acquisition strategies

» Targeting most likely responders

» Response modeling

» Seeking greatest profit potential

» Focused/limited utilization currently » Few regulatory restrictions

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Underwriting and Pricing – Client Benefits

» New Business Underwriting Strategy

» Earliest CBIS application

» Consistent and objective new business risk segmentation decisions

» Quickly accepting applicants with greatest profit potential

» Dedicating resources to appropriate risks

» Minimizing exposure to riskier applicants

» Regulatory considerations

» Pricing Strategy

» Currently focused CBIS application

» Slotting applicants by price/tier based on multiple risk characteristics

» Gaining premiums relative to presented exposures

» Consistent and objective pricing decisions

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New Business Underwriting Strategy

Decline Referral Automated Approval

1 2 3 4 40 50 60 70 80 90 100 110 120 130 140 0 5 6 7 8 9 10 L o ss Rat io Population by deciles Average

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Renewal Underwriting – Client Benefits

» Renewal Underwriting Strategy

» Secondary CBIS risk segmentation application

» Updating CBIS scores at renewal where allowed

» Identifying renewal policyholders for swift processing

» Highlighting renewal policyholders requiring greater attention

» Renewal tier-placement and pricing relative to exposure

» Consistent and objective renewal underwriting decisions

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CBIS Enhanced Uses – Client Benefits

» CBIS enhances decision-making and profitability for the

enterprise

» Book management

» Managing books of business at varying level for profitability

» Underwriting management

» Managing underwriter responsibilities to match risk complexity and varying levels of production and profitability goals

» Distribution source management

» Managing at agency or other production source levels to assure a focus on greater profitability through better managed and strengthened

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Regulatory Environment Forecast

» Regulatory/legislative questions will continue

» Individual state actions

» NAIC committees/task forces

» FTC Study—Auto (2007) and Home (2012/2013)

» Education is the key

» Education to help consumers understand and change habits to influence credit-based insurance scores is available at

www.insurancescores.fico.com

» Education to help consumers understand and improve their credit habits to influence the FICO® scores lenders use is available at

www.myfico.com

» Under the 2003 Fair and Accurate Credit Transactions Act (FACT Act), consumers can access each credit report annually via

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Property Risk Scores

» Property PredictR® Insurance Scores – launched in 2007 to help

insurers harness predictive value from property inspections

performed by FICO’s partner – Millennium Information Services

» Snapshot of risk based on property inspection data » Rank-orders properties by loss ratio relativity

» Precise and objective measurement of a property risk

» Property PredictR provides strong additive value to CBIS or

where regulatory constraints make credit difficult to use

» Property PredictR scores are used as a key risk predictor in

conjunction with other risk variables

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1.26 1.10 0.86 0.76 0.64 0.92 1.53 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Below 180 11.7% 180-201 9.1% 202-222 19.3% 223-244 17.6% 245-272 20.7% 273-293 11.1% 294 or more 10.5% Score by Percent Premium

Los s R a tio R e la tiv it y

Property PredictR

®

_{Score Retro Analysis Loss Ratio}

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CBIS with Property PredictR

®

_{Loss Ratio}

Relativity Matrix

SCORE RANGE

0.56 Consistently Good Risks

Highest Scoring 20% 1.37 0.71 1.18 1.11 1.83 1.85

Missing Credit Score

1.00 0.70 0.87 0.93 1.10 1.41 Total 0.47 0.66 0.66 Highest 20% 0.77 0.58 0.59 0.73 1.52 Medium High 20% 0.89 0.68 0.86 0.63 1.65 Medium 20% 0.97 0.65 0.73 0.97 1.83 Medium Low 20% Medium High 20% Medium 20% 1.48 Consistently Poor Risks

Lowest Scoring 20% Total Medium Low 20% Lowest 20% SCORE RANGE SCORE RANGE 0.56 Consistently Good Risks

Highest Scoring 20% 1.37 0.71 1.18 1.11 1.83 1.85

Missing Credit Score

1.00 0.70 0.87 0.93 1.10 1.41 Total 0.47 0.66 0.66 Highest 20% 0.77 0.58 0.59 0.73 1.52 Medium High 20% 0.89 0.68 0.86 0.63 1.65 Medium 20% 0.97 0.65 0.73 0.97 1.83 Medium Low 20% Medium High 20% Medium 20% 1.48 Consistently Poor Risks

Lowest Scoring 20% Total Medium Low 20% Lowest 20% SCORE RANGE Cr ed it -Based In su ran ce S co re

Note - Statistic listed in cell is the loss ratio relativity for that cell

Poor Risks Medium Risks Good Risks

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Safe Driving Scores – UBI/Telematics

» FICO Safe Driving Scores available in partnership with

DriveFactor, a leading telematics provider

» Continuous view of risk based on driving behavior » More closely align pricing to loss potential

» Accurate measurement of an automobile risk

» Consumer-facing views to gradually improve driving habits

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Agenda

» Insurance scores: CBIS and property risk scores

» Custom models: GAM versus GLM

» Premium leakage and pre-claims fraudulent

detection at underwriting

detection activity at

underwriting

Custom Predictive Analytics/

Custom Modeling

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Insurance Analytic Solutions

Insurance Industry P&CPersonal Health Homeowners P&C Commercial Auto Government Federal Medicare State Medicaid Fraud Detection Loss Reserving Subrogation

Risk Scores Rules Mgmt.

Rules Mgmt, UW Profit, Claims

Rules Mgmt. Fraud Detection

Property & Casualty

Bureau Scores Risk Scores UW Decisioning Bureau Scores Risk Scores UW Decisioning

Property & Liability Workers’ Comp

Private Payors Life

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Development &

Implementation

Model Development Methodology

Performance Definitions

Project Design

Generate Complex Characteristics

Model Development & Testing

Delivery Meeting Data Analysis Sampling Plan Performance Def Options Analytic Review Implementation/Tracking Plan Timing Analysis Final Performance

Definitions Review & Selection

Sample Selection –Pull & Audit Start Up

documentation Customer & FICO _{Project Team}

Meeting Project Specifications

Characteristic Selection

Analytic Documentation

Segmentation Analysis

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Analytics Spectrum

Different decisions lead to different methodologies

Benefit

• Brings all predictive analytics into a single

decision framework • Assigns the optimal

action for each prospect/account • Creates micro segments by matrixing 2 or 3 predictive models • Rank orders prospects on a single dimension • Establishes broad segments based on customer profile data HIGH Multi-Dimensional Trade-Off Assessment Predictive Models or “Scores” Profiling & Segmentation X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Decision Optimization Benefit

• Brings all predictive analytics into a single

decision framework • Assigns the optimal

action for each • Creates micro segments by matrixing 2 or 3 predictive models • Rank orders prospects on a focused dimension • Establishes broad segments based on customer profile data

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Custom Analytics/Modeling

A model that will improve decisions should be …

» Flexible

» Can capture complex data relationships » Palatable

» Interpretable

» Satisfies operational constraints

»Legal concerns

»Implementation friendly

» Able to be computed quickly and seamlessly deployed » Robust into the future

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Project design

Understanding business challenges

» Data Mining - “Try multiple algorithms and see which one best matches the business challenge”

» Objectives - “Understand the

business problem and design an

Prepare Modelling Datasets Visualise & Explore Build Standard Models Forecast Predictive Performance Access Data Deploy Analytics in Decision Build Scorecard Models  Data Mining   Decision 

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Model Development

Initial data analysis

Fine Binning

Coarse Binning

» What are we trying to predict?

» Metrics

» How do we know the model is working?

» What data sources are available?

» What are costs vs. benefits?

» Common data issues

» Incomplete and/or inaccurate data » Outlier anomalies can skew statistics

» Highly correlated items blur the incremental value contributed by each predictive variable » Is the data biased? Censored?

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Model Development

Segmentation

Segment 1 Scorecard 1 Segment 1.1 Segment 2 Total Applicant Population

Scorecard 2

Segment 1.2 Segment 2.1 Scorecard 3 Scorecard 4 Segment 2.2 0 1 _{# Scorecards}3 4 5 .066 .064 .062 .060 .058 .056 .054 .052 Low est C lai m Fr equency 2 6 .050 .068 .080 ...

… how many scorecards to deploy:

Which segments matter most? Where the ROI is justified?

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Model Development

Score Engineering

» Imposing constraints on the score formula to achieve various objectives » Objectives

» Palatability

» Legal requirements

» Robustness over changing times

» Adjustments for known sample biases » Engineering techniques

» Choose the “right” objective function – possibly more than one » Individual weight restrictions

» Characteristic restrictions

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Modeling Repository

Z

Implementation

Can I make it work in practice?

x’ X Y Runtime Package (Java) Modeling Process

IT Model Deployment Process

Data Preparation Variables Scoring Stats Data Mapping Stats Deploy Stage Modeling Data Production Data Variables Scoring Compare y’z’

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Agenda

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Predictive Analytics:

Achieving Greater Decision Accuracy

» Capitalizing on predictive analytics

» Consider and establish value of predictive analytics to achieving strategic goals

» Develop focused strategy toward specifically targeted areas and needs

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Q&A

Lamont D. Boyd, CPCU, AIM

Insurance Market Director FICO Scoring Solutions

[email protected]