Sold by: ElectrifAi LLCÂ
Overview
Better identify those likely to cancel or fade out of loan process and take steps to keep applicants involved by using unstructured data from sales activity, demographics and financial profiles. Applied topic mining and unsupervised clustering techniques and qualitative assessments to improve acceptance rate of personal loans for Personal Loans group within Consumer Banking Division of a top US Credit Card 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: PLOAN-PS-CBK-AWS-001
Highlights
- Utilize unstructured data from sales activity alongside demographic and financial profiles to better identify those likely to cancel or fade out of the loan process and to take steps to keep applicants active.
- Designed queue segmentation using critical decision points. Performed topic mining on agent comments to extract latent topics using Latent Semantic Indexing (LSI) technique. Used unsupervised cluster approach (Self-Organizing Map) to group customer population by their financial and demographic profiles within each channel. Recommended treatment of customers based on their segment driver DNA.
Details
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Delivery method
Latest version
Deployed on AWS
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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-2021-3177Â ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
Input: A zip file with upto 6 comma separated (csv) files. Reference file: sample.zip
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-personal-loan-acceptance/blob/main/sample.zip
https://github.com/ElectrifAi/model-aws-personal-loan-acceptance/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 upto 6 csv files | Agent.csv (REQUIRED), Bureau.csv (REQUIRED), Application.csv (REQUIRED), Account.csv (REQUIRED), PNL.csv (REQUIRED), Transaction.csv (OPTIONAL) | Type: FreeText | Yes |
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