Improving Credit Risk Assessment through Deep Learning-based Consumer Loan Default Prediction Model
importance 5/10
partially solved
Financial Distress and Bankruptcy Prediction consumer banking stage: research open-source model
Problem
Banks need to identify which consumer loan applicants are likely to default, to reduce credit losses and speed up borrower review.
Why it matters
Loan defaults create major financial losses for banks and restrict access to credit.
What the AI did
A deep learning model built with Keras on TensorFlow was trained on survey data from customers of top banks to predict an applicant's default likelihood.
Results
Test set accuracy 95.2%, correctly predicting default state of 238 out of 250 respondents.
Impact
The model predicted default status with 95.2% accuracy on a small 250-record test set.
Record
| Venue | International Journal of Finance & Banking Studies (2147-4486) |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Muhammad Jumaa, Muhammad Saqib, Arif Attar |
| Citations | 14 (OpenAlex, at import time) |
| References | 17 |
| Model or method | Keras neural network on TensorFlow |
| Organization | not stated in source |
| Primary source | https://doi.org/10.20525/ijfbs.v12i1.2579 |
| Open access | diamond |
| Keywords | credit risk, deep learning, loan default, Keras, TensorFlow, banking |
Importance rating
5
real but narrower advance
Credit risk and loan defaults matter to banks and borrowers, but abstract gives no scale of losses or affected population; evidence limited to a 250-record test set.
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