A Comparative Study of Machine Learning Approaches for Non Performing Loan Prediction

Abstract

Credit risk estimation and the risk evaluation of credit portfolios are crucial to financial institutions which provide loans to businesses and individuals. Non-performing loan (NPL) is a loan type in which the customer has a delinquency; because they have not made the scheduled payments for a time period. NPL prediction has been widely studied in both finance and data science. In addition, most banks and financial institutions are empowering their business models with the advancements of machine learning algorithms and analytical big data technologies. In this paper, we studied on several machine learning algorithms to solve this problem and we propose a comparative study of some of the mostly used non performing loan models on a customer portfolio dataset in a private bank in Turkey. We also deal with a class imbalance problem using class weights. A dataset, composed by 181.276 samples, has been used to perform the analysis considering different performance metrics (i.e. Precision, Recall, F1 Score, Imbalance Accuracy (IAM), Specificity). In addition to these, we evaluated the performance of the algorithms and compared the obtained results. According to these performance metrics, LightGBM gave the best results among the logistic regression, SVM, random forest, bagging classifier, XGBoost and LSTM for the dataset.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 25 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.