Articlegold2023

An ensemble machine learning based bank loan approval predictions system with a smart application

Nazim Uddin · Md. Khabir Uddin Ahamed · Md. Ashraf Uddin · Md. Manwarul Islam · Md. Alamin Talukder · Sunil Aryal

80 citations0/0 files storedInternational Journal of Cognitive Computing in Engineering

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

Abstract

Banks rely heavily on loans as a primary source of revenue; however, distinguishing deserving applicants who will reliably repay loans presents an ongoing challenge. Conventional selection processes often struggle to identify the most suitable candidates from a pool of loan applicants. In response to this challenge, we present an innovative machine learning (ML) based loan prediction system designed to identify qualified loan applicants autonomously. This comprehensive study encompasses data preprocessing, effective data balancing using SMOTE, and the implementation of diverse ML models, including Logistic Regression, Decision Tree, Random Forest, Extra Trees, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, AdaBoost, Gradient Boosting, and advanced deep learning models such as deep neural networks, recurrent neural networks, and long short-term memory models. The model's performance is rigorously assessed in terms of accuracy, recall, and F1_score. Our experimental analysis reveals that the Extra Trees outperforms its counterparts. Furthermore, we successfully predict bank loan defaulters through an ensemble voting model, which includes the top three ML models, achieving a remarkable 0.62% increase in accuracy compared to the Extra Trees. To facilitate user interaction, we have developed a user-friendly desktop-based application. Notably, our findings demonstrate that the voting-based ensemble model surpasses both individual ML models, including Extra Trees, and existing state-of-the-art approaches, achieving an impressive accuracy of 87.26%. This innovative system has the potential to significantly streamline and enhance the efficiency of bank loan approval processes, ultimately benefiting both financial institutions and loan applicants alike.

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References

2003Finance, investment, and growth 2022Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning 2019A study on predicting loan default based on the random forest algorithm 2021Loan default prediction using decision trees and random forest: A comparative study 2022Prediction of Loan Behaviour with Machine Learning Models for Secure Banking 2023Dark-Side Avoidance of Mobile Applications With Data Biases Elimination in Socio-Cyber World

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