Towards Intelligent Lending: Predicting Loan Approvals with Machine Learning

Correctly predicting whether a loan will be approved can have the potential to assist banks and financial institutions by minimizing risks, promoting responsible lending, and enhancing customer experience through decision-making automation. We employ a machine learning pipeline for this research with a structured approach to handle the dataset like missing values, encoding categories, and scaling numerical features. We employ various machine learning algorithms, primarily Decision Trees, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and ensemble models such as XGBoost, CatBoost, and LightGBM, and we employ SMOTE (Synthetic Minority Oversampling Technique) for addressing class imbalance. Conventional classifiers are not effective with imbalanced data, suffer from overfitting, and lack interpretability that makes it difficult to extract useful information from the model prediction. To overcome these limitations, we employ SHapley Additive exPlanations (SHAP) to explain model decisions and ensemble models, which combine multiple weak learners to enhance accuracy and generalization. The ensemble outperforms the conventional classifiers, with a peak precision of $\mathbf{9 1 \%}$. Our approach offers a consistent and interpretable method of improving automated loan approval processes in the financial sector by integrating ensemble learning with interpretable AI methods.