Predictive Analytics:Overview with an Application to WC Claims

68th Annual F. Addison Fowler

Fall Seminar

October 17, 2014

Chris Coleianne Aon Risk SolutionsGlobal Risk Consulting

2

Agenda

When people ask actuaries about their models…

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3

Agenda

If Dilbert were an actuary…

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The Basics

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The Basics

Predictive analytics– Using statistical techniques to anticipate future outcomes

Predictive analytics are used in many applications, not just insurance– Moneyball is predictive analytics

• Using statistical analysis to anticipate baseball players’ performance• Build a better roster

– Credit Scoring (default risk)– Traditional Marketing applications

• Defining a class of target consumers and ranking these targets based on anticipated retention.

• Allocating marketing resources to pursue the insured who will stay the longest.

• But be careful; perhaps these consumers are retained because they are higher cost and can not find alternatives in the marketplace.

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Predictive Modeling in the Insurance Marketplace

We are going to provide workers compensation insurance to Hunt Valley insurance agencies.

We rely on NCCI loss costs for our estimate of losses, and we add on our expenses and profit load.

The market is competitive because this is a low hazard occupation, and each insurer has approximately the same expense load and pricing.

The agencies value loss control services and company reputation. But price is pretty important too!

Our competitor’s charge sometimes higher, sometimes lower after applying credits and debits. Perhaps not enough to move from us, and not enough for us to steal business based on price.

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Predictive Modeling in the Insurance Marketplace

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A B C D E

Cost $80 $100 $120 $160 $190

Price $130 $130 $130 $130 $130

$50

$70

$90

$110

$130

$150

$170

$190

$210

Cost Versus Price

Predictive Modeling in the Insurance Marketplace

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A B C D E

Profit/Loss $50 $30 $10 ($30) ($60)

-$80

-$60

-$40

-$20

$0

$20

$40

$60

Profit/Loss

Predictive Modeling in the Insurance Marketplace

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A B C D E

Profit/Loss $50 $30 $10 ($30) ($60)

-$80

-$60

-$40

-$20

$0

$20

$40

$60

Profit/Loss

$0

Predictive Modeling in the Insurance Marketplace

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A B C D E

Cost $80 $100 $120 $160 $190

Price $130 $130 $130 $130 $130

Enhanced Pricing $105 $105 $120 $160 $160

$50

$70

$90

$110

$130

$150

$170

$190

$210

Cost Versus Enchanced Price

Predictive Modeling in the Insurance Marketplace

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A B C D E

Original Pricing Profit/Loss $50 $30 $10 ($30) ($60)

Enchanced Pricing Profit/Loss $25 $25 $10 ($30) ($30)

-$80

-$60

-$40

-$20

$0

$20

$40

$60

Profit/Loss

$0

Predictive Modeling in the Insurance Marketplace

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A B C D E

Original Pricing Profit/Loss $50 $30 $10 ($30) ($60)

Enchanced Pricing Profit/Loss $25 $25 $10 ($30) ($30)

-$80

-$60

-$40

-$20

$0

$20

$40

$60

Profit/Loss

Predictive Modeling in the Insurance Marketplace

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A B C D E

Cost $80 $100 $120 $160 $190

Competitors $130 $130 $130 $130 $130

Our Pricing $105 $105 $120 $160 $160

$50

$70

$90

$110

$130

$150

$170

$190

$210

Cost Versus Enchanced Price

Predictive Modeling in the Insurance Marketplace

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A B C D E

Original Pricing Profit/Loss $50 $30 $10 ($30) ($60)

Enchanced Pricing Profit/Loss $25 $25 $10 ($30)

-$80

-$60

-$40

-$20

$0

$20

$40

$60

Profit/Loss

Predictive Modeling in the Insurance Marketplace

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A B C D E

Original Pricing Profit/Loss $50 $30 $10 ($30) ($60)

Enchanced Pricing Profit/Loss $25 $25 $10 ($30)

-$80

-$60

-$40

-$20

$0

$20

$40

$60

Profit/Loss

$0

$30

Evolution and Refinement

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$50

$70

$90

$110

$130

$150

$170

$190

$210

A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 D1 D2 D3 D4 D5 E1 E2 E3 E4 E5

Cost

Our Pricing

Competitor's Pricing

Complications

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• Regulatory concerns• Approval• Limiting premium increases

• Customer concerns• Unexplainable premium changes

• Variability of loss process• Will actual experience fall into these bands• Was historical experience predictive of the future

• Competition• How quickly can we implement the model without

disrupting our book• What models are our competitors using

Traditional Analytics versus Predictive Analytics

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Traditional Approach Predictive Analytic Approach

Main Input Claims data Claims data

Additional Input None/Limited Claimant: Personal Data, EmploymentEnvironmental: Economic, Census, Location

Loss Driver Several, viewed separately Many (100s Considered)

Analysis At most several data elements at once

Forward looking simultaneous consideration of relationship between loss drivers and claims.

Correlations / Double Counting

Unnoticed, uncorrected Scaled or eliminated

Prediction of Expected Losses

Unrefined (A,B,C,D) Scoring engine model that is versatile

Consistency Settlement practices can vary by adjuster

Scoring model applied the same way across entire portfolio

Updates Training, UW bulletins, Claims Bulletins

Model adjustment

Implementation Relatively simple Challenging

When is PM Most Useful?

– High frequency coverages • Lots of data to work with

Multi year• Helps with segmentation• Reduces adjustments to data

– Coverages where external data can be utilized• Need the external data to enhance the current models• Can be tied to geographic information and validated

– Good candidates• Personal Auto• Workers Compensation• Business Auto

– Not so good candidates• D&O?• Professional Liability?

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Cutting the Data

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Validation

Data

Too

GreenTraining

DataTe

st

Data

… 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

DOLAgeSexIncurredetc.

Claim 1

Claim 2

Claim 3

Claim 4

…

Variable Types

– Primary data• Claims, locations, employee• Categorical data versus numerical data

– Derivative data• Socio-economic data by zip code

– Compound variables• Transformed variables• Credit score• Text concepts

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“arm” or

“lacer-”

Concept word

list

Depart-ment

sex

Location

age

Sample Variables

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$9,103

$1,944

$12,671 $12,439

$15,304 $15,435

$17,937

All-Claim Average$11,939

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

$18,000

$20,000

0 0.01 - 0.99 1.00 - 4.99 5.00 - 9.99 10.00 - 24.99 25.00 - 49.99 50 or More

Ave

rag

e L

oss

Distance Between Claimant Zip Code and Zip Code of Loss

Average Loss by Distance

Sample Variables

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$13,113 $13,321 $13,249

$11,929

$9,365

$11,285 $11,457

All-Claim Average$11,939

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

Ave

rag

e L

oss

Day of the Week for the Claim Date of Loss

Average Loss by Loss Day of the Week

Sample Variables

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$24,346

$13,281 $13,261$12,297

$10,369 $10,199

$7,935

All-Claim Average$11,939

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

Ave

rag

e L

oss

Day of the Week for the Claim Date Reported

Average Loss by Report Day of the Week

Sample Variables

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$8,671$9,513

$14,784

$16,926

$20,794

$27,346

All-Claim Average$12,116

$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

0.0 - 0.9 1.0 - 1.9 2.0 - 4.9 5.0 - 9.9 10.0 - 24.9 25 or More

Ave

rag

e L

oss

Years Between Date of Hire with Client B and Date of Loss

Average Loss by Length of Employment with Client B

Sample Variables

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Docum

ent length

Number of words

Word 1

Word 2

Word 3

Word 4

Word 5

Word 6

Word 7

Word 8

Word 9

Word 10

1 70 4 iw trip foot fell 2 70 5 ie fell walk build caus 3 58 4 iw pull left shoulder 4 64 8 iw numb left arm due repetit motion comput 5 70 4 iw go slip ice 6 70 5 offic walk door injur hit 7 43 5 iw injur right wrist use 8 70 2 iw way 9 70 2 iw way

10 70 4 iw hurt right finger 11 70 7 iw feel pain lower back area felt 12 70 2 ie walk 13 70 7 iw step car trip fell park lot 14 70 7 iw pain rt wrist numb hand came 15 69 5 iw experienc pain shoulder iw 16 70 5 complain pain numb arm around 17 51 3 ie feel fell 18 70 3 slip door fell 19 70 3 type come build 20 70 4 type come build pain 21 69 4 iw walk stair caus 22 70 4 state felt pain wrist 23 70 5 outsid ie come slip fell 24 70 4 walk slip stair fall 25 69 6 went park lot way back step 26 70 7 iw experienc pain numb right elbow way 27 70 4 iw restroom build floor 28 70 2 ie fell 29 70 7 walk break room slip fell water land 30 69 4 go step fell knee 31 70 7 iw fell injur lower back iw state 32 70 3 iw vehicl come 33 70 6 iw complain pain rt arm shoulder 34 70 3 come side slip 35 70 4 ie go stair hand 36 69 9 iw slip fell floor fell onto right knee caus 37 69 5 iw pain rt wrist pain 38 70 5 right wrist possibl repetit comput

First 38 of 16,000 Claims

Sample Variables

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Word Fragment Word Fragment Word Fragment Word Fragment

ee iw fell ee

iw fell ie fell

fell slip slip iw

slip caus walk slip

walk trip park walk

pain knee lot park

hand hit ice lot

left ie stair floor

ie stair knee ice

right lot injur stair

back rt floor trip

wrist park trip step

caus ice step wet

rt walk ankl build

Concept 1

Concept 2

Concept 3

Concept 50

. . .

Most significant 14 words for each Concept group shown. Over 100 total words are identified for each Concept.

Sample Variables

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1,205

2,441

4,680

6,051

6,854

8,090 7,923 7,621

8,158

10,698

All-Claim Average$7,626

$0.00

$2,000.00

$4,000.00

$6,000.00

$8,000.00

$10,000.00

$12,000.00

1 2 3 4 5 6 7 8 9 10

Loss Description Concept Index Value

Average Limited Loss By Concept 1 of 50

Testing Client A

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All-Claim Average$11,480

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

$90,000

$100,000

Ave

rag

e L

oss

Predicted Loss Size Percentile-Based Scoring Group

Average Severity of Score Bands Using Aon's Predictive Model

$44,583

$665

$78,871

$586 $839 $1,059 $925 $1,488 $2,092$6,174

$16,214

$53,706

0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-8081-85

86-9091-95

96-100

Variable Types

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All-Claim Average$11,480

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

$90,000

$100,000

Ave

rag

e L

oss

Predicted Loss Size Percentile-Based Scoring Group

Low, Mid, and High: Using Aon Predictive Model

$39,367

$1,709

$78,871

Low: 0-80 Mid: 81-95

High: 96-100

Knowledge beforePredictive Modeling

Standard Mitigation Effort

The Best 80% of Scored Claims

(Lowest Expected Loss)

Strong Mitigation EffortThe Mid/High 15% of Scored

Claims

Aggressive Mitigation Effort

The Worst 5% of Scored Claims (Highest Expected Loss)

Mid or High Claims— Strong Accuracy:

78% of new claims predicted in these

groups were valued in the top 20% 3 years

later.

Expected Outcome

– Sort claims and allocate resources to those claims with the most potential for cost increases

– Active identification and management of these claims will result in better outcomes

– And next year we will recalibrate…

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Contact Information

Christian Coleianne, FCAS, MAAA Associate Director and ActuaryActuarial & Analytics+1.410.309.0741 christian.coleianne@aon.com

October 17, 2014