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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Vehicle Insurance Claims Prediction

By: Mphasis Latest Version: 2.0

The solution provides occurrence and claim amount prediction for a policyholder. The solution is based on Regression and XG Boost.

Product Overview

Automotive claims prediction is a component of HyperGraf, which predicts occurrence of a claim and the claim amount for a policyholder. The underlying ML algorithms are based on variations of Regression and XG Boost using important policyholder, vehicle and GeoZone characteristics. Trained on real world claims data from an Insurance company, the algorithm considers important business factors for robustness and accuracy.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input:

Following are the mandatory inputs for predictions made by the algorithm:

  • Age: The owner’s age, between 0 and 99, a numeric vector
  • Gender: Gender of the Vehicle Owner, (0 for Male, 1 for Female)
  • City: The model is trained on cities from US, UK and India. The user has to choose one of the cities from the following list as input to the model.
  • US: 'Chicago', 'New York', 'San Francisco', 'Los Angeles', 'Washington DC', 'Boston', 'San * * Diego', 'Philadelphia', 'Houston'
  • UK: 'London', 'Liverpool', 'Leeds', 'Birmingham', 'Manchester', 'Glasgow', 'Edinburgh'
  • India: 'Bangalore', 'Chennai', 'Hyderabad', 'Kolkata', 'Delhi', 'Mumbai', 'Ahmedabad'
  • EnginePower
  • EnginePowerUnit (Ps/Kilowatts/bhp/hp)
  • VehCurbWeight : Vehicle Curb Weight
  • VehCurbWeightUnit (Kg/Pound)
  • VehAge: Vehicle Age, between 0 and 99, a numeric vector
  • ClaimStatus: Claim Status, taking values from 1 to 7. A new driver starts with bonus class 1; for each claim-free year the claim status is increased by 1. After the first claim the claim status is decreased by 2; the driver can’t return to class 7 with less than 6 consecutive claim free years, a numeric vector
  • PolicyDuration: Policy duration in years
  • Supported content types: 'text/csv'.

Output:

  • Supported content types: 'text/csv'.

Invoking endpoint:

If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it: aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.csv --content-type text/csv --accept text/csv out.csv Substitute the following parameters:

  • "endpoint-name" - name of the inference endpoint where the model is deployed
  • input.csv - input image to do the inference on
  • out.csv - filename where the inference results are written to

Resources:

Additional Resources

End User License Agreement

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Support Information

Vehicle Insurance Claims Prediction

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

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