Sold by: ElectrifAi LLCÂ
Deployed on AWS
Balance potential to increase credit lines to encourage clients to spend more money on credit against the risk if the client cannot pay.
Overview
Increasing credit lines encourages clients to spend more money with their credit card. This model periodically checks the potential to increase the credit line but not to take on risk if the client cannot pay the bill. Line sensitivity model and risk guardrail policy applied to develop model-driven credit line management framework for top US credit card issuers which drove 14% profit lift compared to previous policy-based framework. 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: CLINE-PS-CCC-AWS-001
Highlights
- Periodically check potential to increase credit lines to encourage clients to spend more money, but not to take on risk if the client cannot pay.
Details
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Delivery method
Latest version
Deployed on AWS
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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.
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge 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 |
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 |
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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 with 6 comma separated csv files. Reference file: sample.zip target_week.csv (REQUIRED) creditcard_pnl.csv (REQUIRED) bureau_info.csv (REQUIRED) creditcard_transaction.csv (OPTIONAL) mechant_info.csv (OPTIONAL) cli_campaign.csv (not required for scoring,but required for training)
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-credit-line-management/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-credit-line-management/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 with 6 comma separated csv files. Reference file: sample.zip | target_week.csv (REQUIRED)
creditcard_pnl.csv (REQUIRED)
bureau_info.csv (REQUIRED)
creditcard_transaction.csv (OPTIONAL)
mechant_info.csv (OPTIONAL)
cli_campaign.csv (not required for scoring,but required for training) | Type: FreeText | Yes |
Resources
Vendor resources
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