using predictive analytics to understand your claims process 2017... · 2017-02-16 · using...

February 16, 2017

Roosevelt C. Mosley Jr., FCAS, MAAA

Linda K. Brobeck, FCAS, MAAA

Michael K. Chen, FCAS, MAAA

Using Predictive Analytics to Understand Your Claims Process

1

About the Presenters

• Roosevelt C. Mosley Jr., FCAS, MAAA• Principal and Consulting Actuary• Bloomington, Illinois

• Linda K. Brobeck, FCAS, MAAA• Senior Consulting Actuary• San Francisco, California

• Michael K. Chen, FCAS, MAAA• Consulting Actuary• Des Moines, Iowa

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Agenda

• Objectives, Variables/Data

• Examples

– Estimating Claim Settlement Values

– Using Unstructured Data

– Process Improvement

– Fraud

In Tokyo, Fukoku Mutual Life Insurance Co. this month said an IBM Watson Explorer artificial intelligence system handles claims assessment and payout, reducing 30% of its operation workload. Used earlier to analyze customer feedback and complaints, its role was expanded to claims management, Fukoku Mutual Life said (Best’s News Service, Jan. 22, 2017)

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Polling Question #1

In what areas within claims has your company applied predictive analytics (check all that apply)?

Estimating claim settlement values

Evaluating third party service providers

Assignment of claims to adjusters

Fraud detection

Claim satisfaction

A

B

C

D

E

Do not use predictive analytics for claimsF

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• Estimate Claim Settlement Value

• Identify Untapped Subrogation/Salvage Opportunities

• Improved Assignment of Claim to Proper Handler

• Reduce Claim Cycle Time

• Benchmark Claim Offices

• Benchmark Third Party Claim Service Providers

• Explore Drivers of Claim Customer Satisfaction

• Alert for Potential Adverse Development

• Alert for Likelihood of Litigation Involvement or Re-Open

• Fraud

Claim Analytics Potential Objectives

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• Geography

• Time

• Claimant Characteristics

• Attorney Involvement

• Preferred Claim Network

• Other Claim Features

• Unstructured Data (Adjuster Notes, Documents, Photos)

• External Data

Possible Explanatory Variables & Data for Analytics

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• Accurate estimate of ultimate claim liability

• Increasing accuracy of estimate as information develops

• Identifying higher cost claims earlier

Business Problem

• Policy and Claim information

• Policyholder and Claimant information

• External data

• First Notice of Loss (FNOL) and claim adjuster notes

Information Used

• Establishing reserves

• Claim assignment

• Early warningApplications

Estimating Claim Settlement Values

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Claim Settlement Value by Industry

8

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Yes No Unknown

1.00

0.54

0.74

Claim Settlement Value Modeling – Attorney Involvement

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• Large claims

• Exceptional claims

• Delayed recovery

• Unexpected number of medical treatments

• Lawsuit development

• Coverage development

Early Warning Signs

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Likelihood of Liability Claim

Predicted Likelihood of Liability Recovery

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Exceptional Claim Prediction

(20,000)

(10,000)

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

I

n

c

u

r

r

e

d

L

o

s

s

D

i

f

f

e

r

e

n

c

e

Group

Total Incurred Difference Lift Chart

Mean Target Mean Predicted

Target Variable: Ultimate Incurred minus Incurred as of X Days

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consistently uses freeform text data in analysis.

has performed a freeform text data analysis in the past.

has researched/thought about using freeform text data.

Polling Question #2

Does your company use freeform text data in any of their analyses?

My company…

does not use freeform text data

A

B

C

D

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• Derive relevant and useable variables via text parsing

• Analyze words used in combination to discover ultimate meaning

Business Problem

• Claim diaries

• Customer Service notes

• Other unstructured data

Information Used

• Identify major types of claims not yet codified

• Uncover emerging trends or issues

• Confirm or dispel existing belief

Applications

Using Unstructured Data

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Quantify Qualitative Data

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Text Mining Example

Use trees to visualize themes in a set of documents without having to read through thousands of claim diaries.

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• Identify claims with complex claim issues early in the adjustment process

• What process improvements most improve satisfaction?

Business Problem

• Claim information

• External data

• FNOL and claim adjuster notes

Information Used

• Claim assignment

• Proactive claim handling

• Manage costs

• Improve claim customer satisfaction

Applications

Claim Process Improvement

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• Delays in stages of the claim settlement process

• Can occur in several stages

– Report

– Contact

– Settlement (company, service provider, etc.)

• In general, delays are costly

Lags

18

Report Lag

19

Drivers of Claim Customer Satisfaction

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Polling Question #3

Approximately what percent of your company’s Special Investigations Unit’s (SIU) time is spent looking for suspicious claims to investigate versus investigating suspicious claims?

0% (claims adjusters refer claims to SIU)

1-20%

21-40%

41-60%

61%+

A

B

C

D

E

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• Investigators should investigate not identify

• Referrals take too long

• Rare response variable

Business Problem

• Claim information

• External data

• FNOL and claim adjuster notes

Information Used

• Scoring (claim referral, past fraud, claim anomaly)

• Reason codes identified for high scoresApplications

Claim Fraud

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• Types

– Referrals

– Likelihood of Fraud

– Anomalies

– Networks

– Combination Analytics and Adjuster Expertise

• Benefits

– More consistent referral of claims to SIU based on internal fraud triggers

– Reduction of false positives – claims currently referred to the SIU that shouldn’t be

– Better identification of fraudulent cases currently being missed

– Prioritization of suspicious claims identified

Fraud Detection Models

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Analysis of Referrals: Severity of Injury

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Using Anomalies to Identify Suspicious Claims

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Differences in Claim Clusters

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80.8

2.3 2.5 0

18.3

0

10

20

30

40

50

60

70

80

90

Theft Fire Water Weather Other

Public Adjuster

The use of a public adjuster significantly increases the level of

suspicion for certain types of claims.

Suspicious Claim Characteristics

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• Association Analysis

– Technique used in market basket analysis

– Identification of items that occur together in the same record

– Produces event occurrence as well as confidence interval around the occurrence likelihood

– Can lead to sequence analysis as well, which considers timing and ordering of events

Identification of Networks

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Network Example

Network association can lead to increased claim suspicion.

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Application of Results – Claim Fraud Report

Claim Details

Arbitration 3 Accident Date 10/18/2009

Report Lag 3 days Report Date 10/21/2009

Days Open 932 Coverage Bodily Injury

Lawsuit Suit Filed

State 46

Accident Location Small Town

Injury SeverityNo Information

Available

Claimant Age 46

Fraud Model Scores

Score Indicator

SIU Referral 53

Past Identified Fraud 36

Claim Anomaly 13

Composite 34

Fraud Model Reason Codes Reason Code Description

1 Delayed Reporting

2 Accident in Small Town

3

4

Claim suspicion scorecards can be

implemented.

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Questions

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Join Us for the Next APEX Webinar

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• We’d like your feedback and suggestions

• Please complete our survey

• For copies of this APEX presentation

• Visit the Resource Knowledge Center at Pinnacleactuaries.com

Final notes

33Commitment Beyond Numbers

Thank You for Your Time and Attention

Roosevelt Mosley

rmosley@pinnacleactuaries.com

lbrobeck@pinnacleactuaries.com

Linda Brobeck

mchen@pinnacleactuaries.com

Michael Chen