Articlediamond2022

An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending

Much Aziz Muslim · Yosza Dasril · Muhammad Sam’an · Yahya Nur Ifriza

11 citations0/0 files storedIndonesian Journal of Electrical Engineering and Computer Science

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Financial Distress and Bankruptcy Prediction FinTech, Crowdfunding, Digital Finance Imbalanced Data Classification Techniques

Abstract

Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.

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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 2015Risk assessment in social lending via random forests 2020A novel construction cost prediction model using hybrid natural and light gradient boosting 2019LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry 2019A study on predicting loan default based on the random forest algorithm 2019Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market 2020Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending 2017FinTechs in China – with a special focus on peer to peer lending

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