Sold by: ElectrifAi LLCÂ
Deployed on AWS
Analyze low-utilization credit card users customer data to discover spend behavior and relationships between products or services.
Overview
Gain a more holistic view of low-utilization credit card users by analyzing user spend behavior from customer data to find clear relationships between products or services. Leverage look-alike model to identify customers truly passionate about a spend category. Third-party data resources integrated to strengthen customer profiles. This model drove a 5x increase in spend life in comparison to random campaigns and $500 million to $1 billion potential spend impact for top US and China credit card issuers.
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: SPEND-PS-CCC-AWS-001
Highlights
- Gain a more holistic view of low-utilization credit card users by analyzing user spend behavior and find relationships between products or services.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
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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.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 |
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
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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.
Deploy the model on Amazon SageMaker AI using the following options:
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
A zip file containing 5 comma separated csv files. Reference file: sample.zip Bureau.csv (REQUIRED) PNL.csv (REQUIRED) Infobase.csv (REQUIRED) Scoring_date.csv (REQUIRED) Transaction.csv (OPTIONAL)
https://github.com/ElectrifAi/model-aws-spend-passion/blob/main/input_output_description.mdÂ
- Input MIME type
- multipart/form-data
https://github.com/ElectrifAi/model-aws-spend-passion/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-spend-passion/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 5 comma separated csv files. Reference file: sample.zip | **_Bureau.csv_** (*REQUIRED*)
**_PNL.csv_** (*REQUIRED*)
**_Infobase.csv_** (*REQUIRED*)
**_Scoring_date.csv_** (*REQUIRED*)
**_Transaction.csv_** (*OPTIONAL*) | Type: FreeText | Yes |
Resources
Vendor resources
Support
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.
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