Sold by: Virtusa Corporation - vLife
Deployed on AWS
Detection of Fraudulent claims in General and Health Insurance
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
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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.
Deploy the model on Amazon SageMaker AI using the following options:
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 |
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
An Ensemble Learning based solution to predict potential fraud in Healthcare Claims
Ensemble Machine Leaning algorithm-based solution that predicts the probability of fraud for auto insurance claims.
This solution is a deep learning based algorithm capable of training on transactional data, to identify spurious transactions.
Detect and prevent fraud before it happens with this collection of models using transactional history, payments and non-monetary activity.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.