Articlediamond2023

Explainable prediction of loan default based on machine learning models

Xu Zhu · Qingyong Chu · Xinchang Song · Ping Hu · Lu Peng

73 citations0/0 files storedData Science and Management

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Financial Distress and Bankruptcy Prediction Microfinance and Financial Inclusion Explainable Artificial Intelligence (XAI)

Abstract

Owing to the convenience of online loans, an increasing number of people are borrowing money on online platforms. With the emergence of machine learning technology, predicting loan defaults has become a popular topic. However, machine learning models have a black-box problem that cannot be disregarded. To make the prediction model rules more understandable and thereby increase the user’s faith in the model, an explanatory model must be used. Logistic regression, decision tree, XGBoost, and LightGBM models are employed to predict a loan default. The prediction results show that LightGBM and XGBoost outperform logistic regression and decision tree models in terms of the predictive ability. The area under curve for LightGBM is 0.7213. The accuracies of LightGBM and XGBoost exceed 0.8. The precisions of LightGBM and XGBoost exceed 0.55. Simultaneously, we employed the local interpretable model-agnostic explanations approach to undertake an explainable analysis of the prediction findings. The results show that factors such as the loan term, loan grade, credit rating, and loan amount affect the predicted outcomes.

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References

2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring 2021Predictably Unequal? The Effects of Machine Learning on Credit Markets 2014Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending 2021Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression 2015Risk and Risk Management in the Credit Card Industry 2017Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending 2015An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market 2019Default prediction in P2P lending from high-dimensional data based on machine learning

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