Machine Learning Models for Predicting Bank Loan Eligibility

Ugochukwu, Orji,; Ugwuishiwu, Chikodili; Nguemaleu, Joseph. C. N.; Ugwuanyi, Peace. N.

doi:10.1109/nigercon54645.2022.9803172

Cited by 12 publications

(1 citation statement)

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“…In recent years, machine learning has brought about a paradigm shift in the domain of loan approval. Various studies have explored the application of machine learning algorithms to enhance the accuracy and efficiency of decision-making processes (Alaradi & Hilal, 2020;Orji et al, 2022;Sheikh et al, 2020). A widely adopted machine learning framework known for its efficiency and effectiveness is the Light Gradient Boosting Machine (LightGBM) (Ke et al, 2017).…”

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confidence: 99%

Enhancing Loan Approval Decision-Making: An Interpretable Machine Learning Approach Using Lightgbm for Digital Economy Development

Noviandy,

Idroes,

Hardi

2024

MJoC

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This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the accuracy and transparency of loan approval processes. We employed LightGBM, a gradient-boosting framework for loan approval classification, optimized via Random Search hyperparameter tuning and validated using 10-fold cross-validation. We incorporated the Shapley Additive exPlanations (SHAP) framework to address the challenge of interpretability in machine learning. The LightGBM model outperformed conventional algorithms (Decision Tree, Random Forest, AdaBoost, and Extra Trees) in accuracy (98.13%), precision (97.78%), recall (97.17%), and F1-score (97.48%). The study demonstrates that using an interpretable machine learning approach with LightGBM and SHAP can significantly improve the accuracy and transparency of loan approval decisions. This method offers a promising avenue for financial institutions to enhance their loan approval mechanisms, ensuring more reliable, efficient, and transparent decision-making in the digital economy. The study also underscores the importance of interpretability in deploying machine learning solutions in sectors with significant socio-economic impacts.

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confidence: 99%