Sold by: MphasisÂ
Deployed on AWS
This solution predicts which customers are more likely to default on their consumer loans in peer-to-peer lending.
Overview
This solution identifies the borrowers who are most likely to default on their Consumer loans in peer-to-peer lending. During the training stage, the solution understands the dataset, handles missing data and class imbalance, conducts feature interaction on the training data and selects a subset of best features based on feature importance. It then trains on multiple classification models, identifies the best performing model and tunes it accordingly . This trained model is then selected for prediction on the test data.
Highlights
- This solution takes in peer-to-peer loan data as input, pre-processes the data, picks out the best features based on feature importance, trains it on several models and gets the best model and predicts the test data, thereby reducing the risk of lending to defaulters and expected loss to the lender.
- The algorithm is specifically designed for analyzing peer-to-peer consumer loans using machine learning.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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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.m5.12xlarge Inference (Batch) Recommended | Model inference on the ml.m5.12xlarge instance type, batch mode | $20.00 |
ml.m5.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.12xlarge instance type, real-time mode | $10.00 |
ml.m5.12xlarge Training Recommended | Algorithm training on the ml.m5.12xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
It is the first version of the algorithm. It requires consumer loan specific data as input
Additional details
Inputs
- Summary
- Once the model is generated after training, the solution can be used to predict the defaulters for a given new data.
- The new data features should be identical to training data features.
- The name of the new file should be sample_input.csv
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Peer-to-Peer-Loan-Default-Prediction/tree/main/data/transform
https://github.com/Mphasis-ML-Marketplace/Peer-to-Peer-Loan-Default-Prediction/tree/main/data/transform
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
not_default | Target Variable, if someone would default the loan or not | Type: Integer
Minimum: 0
Maximum: 1 | Yes |
Resources
Vendor resources
Support
Vendor support
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