Sold by: ElectrifAi LLCÂ
Deployed on AWS
Predict renewal likelihood for a group insurance plan and its sensitivity to different pricing.
Overview
Predict the likelihood of group insurance plan renewal and the sensitivity to different pricing options. Previous usage of the model generated an estimated annual increase in premiums by $8-9MM across a 16k plan. Using the data input of group plan level data (e.g. coverages and provisions, tenure, plan size, SIC), employee level data (e.g. gender, salary, dependents, dental claims) and agent level data, the model output the predicted score for plan renewal at various price levels.
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SKU: PRENP-PS-GIS-AWS-001
Highlights
- Predict the likelihood of group insurance plan renewal and the sensitivity to different pricing options.
- Technical highlights include extracted and integrated relevant information from complicated insurance data. Leveraged state-of-the-art machine learning tools to train a propensity model to predict the renewal probability. Also designed a novel framework to analyze price elasticity to find the perfect price for each customer.
Details
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Delivery method
Latest version
Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.00 |
ml.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge 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
Vulnerability CVE-2021-3177 (i.e. https://nvd.nist.gov/vuln/detail/CVE-2021-3177Â ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input: A zip file containing upto 8 csv files with required input columns.
- Input MIME type
- multipart/form-data
https://github.com/ElectrifAi/model-aws-plan-renewal-insurance/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-plan-renewal-insurance/blob/main/sample.zip
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Input zip file contains following required and optional csv files | Member.csv (required), Plan.csv (required), Coverage.csv (required), Claims.csv (required), Billing.csv (required), Broker.csv (required), Customer_service.csv (required), Dependent.csv (optional)
| Type: FreeText | Yes |
Resources
Vendor resources
Support
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