1. Subrogation Prediction Through Text Mining and Data Modeling
Sergei Ananyan, Ph.D.
Megaputer Intelligence

2. Why Subrogating?
While only a few percent of cases have subrogation potential, significant amounts of money can be recovered
Estimates: Missed subro opportunities in USA ~ $15Billion annually
Efficient subrogations facilitate in keeping insurance premiums low, providing an extra competitive edge

3. Challenges of Subrogation
Overwhelming volume of claims:
Over 5 million reported workplace injuries in the USA annually
Over 6 million auto insurance claims in the USA annually
Subrogation opportunities comprise only a few percent of all claims
Subro decisions involve manual analysis of textual notes in claims
Thorough investigations can be lengthy and costly
Missed subrogation opportunities can be even more costly
Subro decisions should be made soon after the accident. Relevant evidence may disappear quickly.

4. Who makes a subro decision?

5. Traditional Way: Adjusters
Individual Adjusters determine subrogation cases
Pros:
Subro decisions can be made at early stages of claim handling
Investigation can be conducted on the spot
Cons:
Subrogation determination is at the bottom of a long list of actions
Verifying coverage
Determining compensation
Approving payments
Reporting
Different experience of adjusters: no consistency across organization
Either the lack of formal rules or a set of rules that is too rigid to determine subrogation potential of many cases
Looking for “a needle in a haystack”: easily overlooked

6. Traditional Way: Recovery Teams
Specialized Recovery Teams determine subrogation opportunities
Pros
Highly trained professionals: better determination of opportunities
Consistency across the organization
Cons
Small group of investigators: overloaded with large numbers of claims
Located remotely: need to coordinate efforts with local adjusters
Delays in starting investigations

7. Recovery Teams are Overloaded

8. Subrogation Prediction Objectives
A perfect solution for subrogation prediction should be
Accurate
Automated
Objective
Consistent
Fast

9. New Way: Automated Modeling
New predictive modeling tools can identify subro opportunities
They provide many benefits
Timely detect good new candidate claims for subrogation
Capture missed opportunities throughout closed cases
Focus attention of investigators on cases with high potential
Eliminate wasted time and efforts
Standardize subrogation prediction practice across the enterprise
Enhance customer satisfaction

10. Modeling and Text Mining
Knowledge discovery tools for business users
Easy-to-understand actionable results
Data Overload
Useful Knowledge

11. What is Data Modeling?
Computer models learn from historical data and predict outcomes of future situations
Models are developed through training on data with known outcomes
Training is based on machine learning and statistical algorithms
The Megaputer solution PolyAnalyst™ for Subrogation Prediction offers a selection of modeling algorithms:
Decision Trees
Neural Networks
CHAID
Bayesian Networks
Random Forest
Best model can be selected automatically
Developed models are used for scoring new data to predict:
Probability of the subrogation success
Potential recovered amount

12. Training and Applying the Model
Model Training:
Modeling is carried out on data collected from claim forms and notes
Successful past subrogation cases are considered as positive examples
“No subrogation” cases are negative examples
A model learns combinations of features determining positive cases
Another model predicts the amount of possible subrogation
The developed model is stored for future use
Model Application
Models are applied to new data to produce scores
Calculate:
Subrogation probability
Subrogation amount
Claims with the highest scores on these two attributes are presented for investigation by a human

13. Investigations involve data analysis
Decision Maker
Interactive up-to-date reports
Data Analyst
Visual analytic scenario

14. Behind the Scenes

15. Output: Subrogation Prediction
Probability of the subrogation success
Estimated recovery amount

16. Data Integration

17. Data Cleansing

18. Aggregation – keys and attributes

19. Aggregations - measures

20. Derivative Attributes

21. Complications of Text Analysis
The need to analyze free text notes further complicates things
Statistical tools are good at processing structured data, but not text
Human analysts had to read text notes to extract relevant features

22. Text Mining Technology
Text Mining is an automated process of analyzing text to extract information from it for particular purposes
Text Mining is different from traditional search technology:
In search, the user is typically looking for something that is already known and has been written by someone else
Text Mining involves pushing aside irrelevant material in order to extract relevant information
Text Mining extracts relevant features from natural language notes. These features are included in modeling.

23. Typical Text Mining Tasks
Categorization
Feature and entity extraction
Summarization

24. Complications of Text Analysis
Typical textual descriptions
SLIPPED OFF BACK OFVAN LOADING TOOLS
PUSHED WHILE CONFRONTING AN ALLEGED SHOPLIFTER
TRIPPED ON A SHEET OF WIRE MESH & FELL ON PAKRING LOT
REACHING FOR PAKAGES ON BELT WHEN HE TRIPPED OVER PAKAGES THAT WERE IN FRONT OF BELT AND FELL
EE WAS CUTTING ONIONS ON THE SLICER AND HE CUT OFF THE TIP OF HIS RIGHT THUM
CLT WAS STRUCK ON HEAD WITH ICE IN THE FREEZER
EMP WAS WALKING BACK TO PKG CAR WHEN 2 DOGS BEGAN TO CHASE HIM, HE RAN & SLIPPED ON STEPS OF PKG CAR
EE WAS USING A BAND SAW TO CUT IRON FOREIGN BODY ENTERED LT EYE

25. Intelligent Spell-Checking

26. Categorization: V2 rear ended V1
Key points of the claim

27. Categorization: policy holder arrested
Key points of the claim

28. Domain-specific Dictionaries

29. Patterns related to Pain

30. Predicted Subro Probability for a Claim

31. Predicted Subro Amount for a Claim

32. PolyAnalyst Subro Prediction flow
New claim
Text Mining
Extracted
Features
Historical claims data
Modeling
Subrogation
Model
Subrogation prediction

33. Touch Points for Modeling
First Report of Incident
Detect subro opportunities, while evidence is still available
Focus efforts only on claims that have good subro potential
Perform timely and thorough investigations
Retrospective Analysis of Claims
Check closed and still open claims
Identify missed subro opportunities
Pursue recovery whenever still possible

34. First Report of Incident (work comp)
Available data
Date
Injury Type
Body part injured
Textual description of the incident
Build models based on historical data
Use a pre-built model to score new claims

35. Retrospective Claims Analysis
Extra data (new)
Claim notes
Financial results
Applicable legislation, Arbitration notices, etc.
Build models based on historical examples
Discover missed subrogation opportunities

36. PolyAnalyst Benefits Dramatic time and cost reduction
Increase in quality and speed of the analysis
Objective and uniform data-driven analysis
Discovery of even unexpected issues suggested by data
Automated monitoring of known problems
Timely discovery of newly developing issues
Utilization of 100% of available data: structured and text
Up-to-date reports for executives
Easy to use and to maintain solution

37. Data and Text Mining in Insurance
Fraud Detection
Subrogation Prediction
Database Marketing
Response Prediction
Cross-sell Analysis
Market Segmentation
Text Analysis
Call Center transcripts analysis
Survey analysis
Competitive intelligence
Compliance analysis

38. Select Customers Government Insurance Financial High Tech
Pharmaceutical
Marketing
Manufacturing

39. Contacting Megaputer (812) 330-0110 info@megaputer.com Call or email
120 W Seventh Street, Suite 314
Bloomington, IN USA