© 2008 megaputer intelligence inc. subrogation prediction through text mining and data modeling...

© 2008 Megaputer Intelligence Inc.

Subrogation Prediction Through Text Mining and Data Modeling

Sergei Ananyan, Ph.D.Megaputer Intelligencewww.megaputer.com


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


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.


Who makes a subro decision?


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


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


Recovery Teams are Overloaded


Subrogation Prediction Objectives

• A perfect solution for subrogation prediction should be– Accurate– Automated– Objective– Consistent– Fast


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


Modeling and Text Mining

• Knowledge discovery tools for business users• Easy-to-understand actionable results

Data OverloadUseful Knowledge


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


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


Investigations involve data analysis

Data Analyst

Visual analytic scenario

Decision Maker

Interactive up-to-date reports


Behind the Scenes


Output: Subrogation Prediction

• Probability of the subrogation success

• Estimated recovery amount


Data Integration


Data Cleansing


Aggregation – keys and attributes


Aggregations - measures


Derivative Attributes


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


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.


Typical Text Mining Tasks

• Categorization

• Feature and entity extraction

• Summarization


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


Intelligent Spell-Checking


Categorization: V2 rear ended V1

Key points of the claim


Categorization: policy holder arrested

Key points of the claim


Domain-specific Dictionaries


Patterns related to Pain


Predicted Subro Probability for a Claim


Predicted Subro Amount for a Claim


PolyAnalyst Subro Prediction flow

Text Mining

ModelingSubrogation

Model

Historical claims data

Subrogation prediction

New claim

ExtractedFeatures


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


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


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


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


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


Select Customers

Government

Insurance

Financial

High Tech

Pharmaceutical

Marketing

Manufacturing


Contacting Megaputer

Call(812) 330-0110

or [email protected]

120 W Seventh Street, Suite 314Bloomington, IN 47404 USA

© 2008 megaputer intelligence inc. subrogation prediction through text mining and data modeling...

Documents

megaputer intelligence

subrogation potential

subrogation determination

subrogation capture

possible subrogation

overloaded slide

data integration slide

data cleansing slide