Work2020

Loan Default Prediction with Machine Learning Techniques

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43 citations0/0 files storedInternational Conference on Computer Communication and Network Security (CCNS)

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Financial Distress and Bankruptcy Prediction Microfinance and Financial Inclusion Imbalanced Data Classification Techniques

Abstract

Loan business is one of the major income sources for bank. However, loan default problem is a major issue for loan business. With the rise of big data era and the development of machine learning techniques, nowadays we have more options for classifying and predicting loan default, other than manual processing. With a real-world dataset from a prestigious international bank, we demonstrate that the AdaBoost model can achieve a 100% accuracy for predicting loan default, outperforming other models including XGBoost, random forest, k nearest neighbors, and multilayer perceptrons. Our result shows the promising application of machine learning techniques in the financial industry.

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References

2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning 2006Application of support vector machines to corporate credit rating prediction 2013Consumer credit risk: Individual probability estimates using machine learning 2018Predicting mortgage default using convolutional neural networks 2011A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach 2015Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach 2013Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings 2019Machine learning models for credit analysis improvements: Predicting low-income families’ default

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2025Data-Driven Loan Default Prediction: A Machine Learning Approach for Enhancing Business Process Management 2022A review of Artificial Intelligence approach for credit risk assessment

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