Subrogation Prediction Through Text Mining and Data Modeling

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

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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.

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Who makes a subro decision?
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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

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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

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Recovery Teams are Overloaded
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Subrogation Prediction Objectives

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

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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

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Modeling and Text Mining

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

Data Overload
Useful Knowledge
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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

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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

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Investigations involve data analysis
Data Analyst
Visual analytic scenario
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Behind the Scenes
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Output Subrogation Prediction

• Probability of the subrogation success
• Estimated recovery amount

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Data Integration
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Data Cleansing
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Aggregation keys and attributes
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Aggregations - measures
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Derivative Attributes
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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

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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.

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Typical Text Mining Tasks

• Categorization
• Feature and entity extraction
• Summarization

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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

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Intelligent Spell-Checking
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Categorization V2 rear ended V1
Key points of the claim
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Categorization policy holder arrested
Key points of the claim
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Domain-specific Dictionaries
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Patterns related to Pain
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Predicted Subro Probability for a Claim
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Predicted Subro Amount for a Claim
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PolyAnalyst Subro Prediction flow
New claim
Text Mining
Extracted Features
Modeling
Historical claims data
Subrogation Model
Subrogation prediction
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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

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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

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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

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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

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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

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Select Customers
Government Insurance Financial High
Tech Pharmaceutical Marketing Manufacturing
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Contacting Megaputer
Call [phone] or email info_at_megaputer.com
120 W Seventh Street, Suite 314 Bloomington, IN
47404 USA