MACHINE LEARNING AND DEEP LEARNING FOR LOAN PREDICTION IN BANKING EXPLORING ENSEMBLE METHODS AND DATA BALANCING

Abstract

The prediction of loan defaults is crucial for banks and financial institutions due to its impact on earnings, and it also plays a significant role in shaping credit scores. This task is a challenging one, and as the demand for loans increases, so does the number of applications. Traditional methods of checking eligibility are time-consuming and laborious, and they may not always accurately identify suitable loan recipients. As a result, some applicants may default on their loans, causing financial losses for banks. Artificial Intelligence, using Machine Learning and Deep Learning techniques, can provide a more efficient solution. These techniques can use various classification algorithms to predict which applicants will likely be eligible for loans. This study uses five Machine Learning classification algorithms (Gaussian Naive Bayes, AdaBoost, Gradient Boosting, K Neighbors Classifier, Decision Trees, Random Forest, and Logistic Regression) and eight Deep Learning algorithms (MLP, CNN, LSTM, Transformer, GRU, Autoencoder, ResNet, and DenseNet). The use of Ensemble Methods and SMOTE with SMOTETOMEK Techniques also has a positive impact on the results. Four metrics are used to evaluate the effectiveness of these algorithms: accuracy, precision, recall, and F1-measure. The study found that DenseNet and ResNet were the most accurate predictive models. These findings highlight the potential of predictive modeling in identifying credit disapproval among vulnerable consumers in a sea of loan applications.