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Home > AI for Banking > Credit Risk Scoring

Credit Risk Scoring

Predict the Risk of Default per Credit Applicant (not being able to pay the loan installments on time). Assign individualized credit score based on factors including current income, employment opportunity, recent credit history, 

Business value: less default losses, reduction in loan management time, better accuracy than traditional methods, and scalability

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1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Kaggle: Give Me Some Credit

Historical data are provided on 250,000 borrowers. Variables include: monthly income, age, monthly debt payments, number of open loans, number of times borrower has been 90 days or more past due, and more

Kaggle: Loan Default Prediction - Imperial College London

This data corresponds to a set of financial transactions associated with individuals. The data has been standardized, de-trended, and anonymized. You are provided with over two hundred thousand observations and nearly 800 features.

Statlog (German Credit Data) Data Set

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix


Off-the-Shelf Products using AI for Credit Risk Scoring

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