Enhancing Banking Data Classification Through Hybrid L2 Regularisation and Early Stopping in Artificial Neural Networks

The demand for robust data-driven classification (DDC) techniques remains critical in banking applications, where accurate and efficient decision-making is paramount. Artificial Neural Networks (ANNs), particularly Multi-Layer Perceptrons (MLPs), are widely used due to their strong learning capabilities. However, their performance often depends on effective hyperparameter tuning and regularisation strategies to avoid overfitting. This study aims to enhance the efficiency of the MLP training process by introducing a hybrid approach that integrates L2 regularisation with Early Stopping (ES) into the hyperparameter tuning procedure. The key contribution lies in embedding both techniques within a grid search framework, thereby streamlining the search for optimal hyperparameters. The proposed method was evaluated using three real-world banking datasets: two related to loan subscription (16 and 20 features) and one concerning credit card default payment (23 features). Experimental results demonstrate that the hybrid approach reduces hyperparameter tuning time by over 90% while achieving high classification performance. Notably, the Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) scores of 93.89% and 91.21% were achieved on the loan datasets, and 73.28% on the credit card dataset, surpassing previous benchmarks. These findings highlight the potential of the L2ES hybrid method to improve both the accuracy and computational efficiency of DDC in financial applications.