Using Predictive Analytics to Understand Your Claims Process 2017... · 2017-02-16 · Using...
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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
Transcript of 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
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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
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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)
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10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
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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
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Report Lag
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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
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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
Linda Brobeck
Michael Chen
