Sold by: MphasisÂ
Deployed on AWS
The solution provides occurrence and claim amount prediction for a policyholder. The solution is based on Regression and XG Boost.
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.
Highlights
- Automotive Claim Prediction predicts claim occurrence and amount using real world historical claims data using vehicle, driver and GeoZone characteristics.
- Provides claim occurrence and claim amount predictions for cities across 3 countries
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
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
Pricing is based on actual usage, with charges varying according to how much you consume. 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.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time
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)Â .
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
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
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:
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out at:
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
Ensemble Machine Leaning algorithm-based solution that predicts the probability of fraud for auto insurance claims.
The Insurtech Market is surging with digital innovations in insurance, projected to reach USD 254.64 billion by 2030 at a 53.1 percent CAGR, fueled by AI, open finance, and real-time payments, enhancing customer-centric solutions.
The Vehicle Image Optimization solution developed by Quantiphi using advanced computer vision helps you to detect and reduce image noise which automatically identifies common issues such as blurs, glares and shadows. The solution overlays the segmented vehicle image on a new background, thus displaying realistic car images on the client's retail website.
With the help of the custom AI/ML Classification model method, the solution holds an accuracy of more than 95 percent.
With TensorIoT, get expert guidance and development for a customized vehicle fleet management solution.
Classifies vehicle damage images in multiple types
Rekor Discovers Vehicle Insite application utilizes real-time video to provide actionable vehicle intelligence such as vehicle site metrics, vehicle characteristics, and Electric Vehicle (EV) statistics. Utilizing state-of-the-art AI and high-definition camera systems, Vehicle Insite delivers real-time traffic data to deepen your understanding of the vehicles within a geography, enabling better planning, enriched experiences and potential targeted marketing initiatives.
A modular, consumer centric as well as end to end Connected Vehicle services for Automotive manufacturers/suppliers to support in evaluating and implementing new business models by securely connecting vehicle data in real time to launch new digital services and improve satisfaction and loyalty towards brand by providing diverse range of new software products and services to the end customers.
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.