Sold by: ElectrifAi LLCÂ
Deployed on AWS
Increase APR for certain customers to reduce risk while not affecting customer satisfaction with this sensitivity-based segmentation model.
Overview
Increase APR interest rates for certain customers to reduce risk while not affecting customer satisfaction with this sensitivity-based segmentation model. This model supports customized pricing offers to attract customers while preventing potential future risk from higher risk card holders. Risky customers include those with delinquent or over-the-limit customers. Using data input of pricing, customer behavior and bureau data, the model outputs price sensitivity and pricing recommendations. To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data. Our machine learning models return actual Output Data and are available through a private offer. Please contact info@electrifai.net for subscription service pricing. SKU: PENPS-PS-CCC-AWS-001
Highlights
- Increase APR interest rates for certain customers to reduce risk while not affecting customer satisfaction with this sensitivity-based segmentation model.
- Technical highlights include two parts: 1) the use of elasticity as the ultimate metric for pricing sensitivity and 2) leveraging group level sensitivity tree to build a risk-based pricing model.
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-2021-3177 ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input: A zip file containing 2 comma separated (csv) files. Reference file: sample.zip creditcard_pnl.csv (REQUIRED) bureau_info.csv (REQUIRED)
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-penalty-pricing-strategies/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-penalty-pricing-strategies/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 |
|---|---|---|---|
A zip file containing 2 comma separated (csv) files. Reference file: sample.zip | creditcard_pnl.csv (REQUIRED)
bureau_info.csv (REQUIRED) | Type: FreeText | Yes |
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