Sold by: ElectrifAi LLCÂ
Deployed on AWS
Accurately predict various scenarios for regulatory and internal consumption with CCAR (Comprehensive Capital Analysis and Review)
Overview
Accurately predict various scenarios for regulatory and internal consumption with CCAR (Comprehensive Capital Analysis and Review), such as: predicting deposits and acquisitions, balance change, personal loans, etc. Forecast future overpayments by vintage and tenor in different economic scenarios. Improved 60+ CCAR model performance for deposits, loans and personal credit cards for a top US consumer banking company.
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: CCARI-PS-CBK-AWS-001
Highlights
- Accurately predict various scenarios for regulatory and internal consumption with CCAR (Comprehensive Capital Analysis and Review).
Details
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Delivery method
Latest version
Deployed on AWS
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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 should be a zip file containing the two required csv files that contain the input data. These two csv files are Loan.csv and Macroeconomics.csv
- Input MIME type
- multipart/form-data
https://github.com/ElectrifAi/model-aws-ccar-improvement/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-ccar-improvement/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 |
|---|---|---|---|
Loan.csv | Loan.csv (required), Macroeconomics.csv (required) | Type: FreeText | Yes |
Macroeconomics.csv | Loan.csv (required), Macroeconomics.csv (required) | Type: FreeText | Yes |
Resources
Vendor resources
Support
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