# P2P Loan acceptance and default prediction with Artificial Intelligence

## TL;DR

This paper develops a two-phase AI-based model using Logistic Regression and Deep Neural Networks to predict loan acceptance and default risk, significantly improving credit risk assessment accuracy.

## Contribution

It introduces a novel two-phase approach combining traditional and deep learning models for loan approval and default prediction, highlighting dataset-specific model performance.

## Key findings

- Logistic Regression achieved 77.4% recall in rejection prediction.
- Deep Neural Networks achieved 72% recall in default prediction.
- Models reduced default risk prediction error by up to 70%.

## Abstract

Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. Logistic Regression was found to be the best performer for the first phase, with test set recall macro score of $77.4 \%$. Deep Neural Networks were applied to the second phase only, were they achieved best performance, with validation set recall score of $72 \%$, for defaults. This shows that AI can improve current credit risk models reducing the default risk of issued loans by as much as $70 \%$. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.