Sold by: Mphasis
Deployed on AWS
A Machine Learning based solution to predict the risk of delinquency for consumer finance loans without using prior credit history
Overview
Sanctioning loans to consumers without credit history is a challenge for consumer finance companies. This solution predicts the probability of loan delinquency for such consumers. This solution can assist consumer finance companies in their decision-making process while assessing the risk to sanction loans. Companies can use the likelihood of loan delinquency to make targeted interventions to identify and mitigate potential loan defaults.
Highlights
- Ensemble Machine Learning algorithm-based solution that can assist consumer finance companies to make lending decisions by predicting the risk of loan delinquency without using prior credit history.
- Companies can use the likelihood of loan delinquency to make targeted interventions to identify and mitigate potential loan defaults.
- 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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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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $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 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the version 1.2
Additional details
Inputs
- Summary
- The input dataset should be in csv format.
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Consumer-Loan-Delinquency-Predictor/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Consumer-Loan-Delinquency-Predictor/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
LOAN_ID | ID of loan | Type: Integer | Yes |
LOAN_TYPE | Type of loan - Cash loans or Revolving loans | Type: Categorical
Allowed values: Cash loans or Revolving loans | Yes |
GENDER | Gender of the consumer | Type: Categorical
Allowed values: M or F | Yes |
CAR_OWNERSHIP | Car ownership. If the consumer owns a car, enter ‘Y’; else, enter ‘N’ | Type: Categorical
Allowed values: Y or N | Yes |
ANNUAL_INCOME | Annual Income of the consumer in USD | Type: Integer | Yes |
AMOUNT_CREDIT | Credit amount of the loan in USD | Type: Integer | Yes |
AGE_OF_CAR | Age of consumer's car in years | Type: Integer | Yes |
PERSONAL_PHONE | Personal phone number- if consumer provided 1, else 0 | Type: Categorical
Allowed values: 1 or 0 | Yes |
WORK_PHONE | Work phone number- if consumer provided 1, else 0 | Type: Categorical
Allowed values: 1 or 0 | Yes |
HOME_PHONE | Home phone number- if consumer provided 1, else 0 | Type: Categorical
Allowed values: 1 or 0 | Yes |
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