Optimizing Banking Sector Loan Fraud Detection through Machine Learning Methods

Loan fraud has been a permanent challenge for the financial sector. It is crucial to ensure the stability of economic and customer trust. Predicting loan fraud is extremely important to eliminate the possibility of the occurrence of crises like the subprime mortgage crisis in 2008. Moreover, the immense number of loan applicants makes it impossible for employees to perform this task manually, especially considering the different number of parameters that need to be investigated. This paper proposes automated artificial intelligence models to detect loan fraud through predictive analysis techniques, with an emphasis on the integration of neural networks and deep learning techniques. The approach combines the autoencoder-based architecture with gradient boosting to ensure the detection of fraud activities. The model was applied to an online dataset from Kaggle, which contains 100,000 credit loan transactions. The model achieves optimal accuracy in fraud detection. This emphasizes the effectiveness of combining deep learning techniques with the autoencoder-based architecture for fraud detection. Additionally, the promising results present the effectiveness of the dimensionality reduction techniques of feature space to enhance the accuracy of the proposed models.