Sold by: Virtusa Corporation - vLife
Overview
Claims Fraud is a serious problem for Insurance Companies as it brings down their profits considerably. Currently, This problem is handled using either internal scoring based engines or rely on Third party agencies for investigations. These rule based systems are static in nature and involve lot of manual efforts, making the process slow and prone to errors. To tackle this, Virtusa-GCTS has developed a Machine-Learning based solution which will flag suspect claims as ‘fraud’ and those claims can be subjected to more scrutiny. It uses Boosting based AI models and saves considerable effort.
Highlights
- Solution leverages web of information including Claim Details, Policy Information, and Claimant details to predict the likelihood of fraud.
- Solution is capable of handling General Liability and Healthcare related claims.
Details
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Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
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Version release notes
Claims Fraud is a serious problem for Insurance Companies as it brings down their profits considerably. Currently, This problem is handled using either internal scoring based engines or rely on Third party agencies for investigations. These rule based systems are static in nature and involve lot of manual efforts, making the process slow and prone to errors. To tackle this, Virtusa-Xlabs has developed a Machine-Learning based solution which will flag suspect claims as ‘fraud’ and those claims can be subjected to more scrutiny. It uses Boosting based AI models and saves considerable effort.
Additional details
Inputs
- Summary
• Claim Related information: Claim related information such as Loss date, Claim type, Location and loss description etc. are taken from the claim database. • Insured/Claimant Details: This solution accesses the Claimant details pertaining to Claimant demographic and details about the historically filed claims by the claimant. • Policy Info: Policy related information such as Policy Start and End Dates, LOB, etc. are mapped to identify the specific patterns in fraudulent claims.
- Input MIME type
- text/csv
https://aws-marketplace-models.s3-us-west-2.amazonaws.com/insurance-claim-fraud/sample+notebook/data/input/real_inference.csv
https://aws-marketplace-models.s3-us-west-2.amazonaws.com/insurance-claim-fraud/sample+notebook/data/input/batch-transformation.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
INDEMNITY_PAID_TO_DATE_USD_AM | Amount in USD that is claimed by Third party | Type: Continuous | Yes |
EXPENSE_PAID_TO_DATE_USD | Expense in USD | Type: Continuous | Yes |
TYPE_NM | Type of injury | Type: FreeText | Yes |
FATAL_INJURY_IN | Flag for fatal injury | Type: Categorical
Allowed values: Y,N | Yes |
HAS_INJURY_DETAILS | Flag for injury details | Type: Categorical
Allowed values: Yes,No | Yes |
CLAIM_TYPE | Claim Type | Type: FreeText | Yes |
LOSS_DT | Loss date (date format) | Type: FreeText | Yes |
BUSINESS_CREATE_TS | Timestamp when applied for claim | Type: FreeText | Yes |
POLICY_EFF_DT | Policy start date | Type: FreeText | Yes |
POLICY_EXP_DT | Policy expired date | Type: FreeText | Yes |
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