Improving Credit Score Classification Using Long Short-Term Memory and Support Vector Machines Tuned with Whale Optimization Algorithm

Credit scoring is important in the financial industry. While traditional credit scoring has been shown to be useful, machine learning is being explored as an alternative by their ability to find complex patterns that traditional models might miss. This study aims to use the Whale Optimization Algorithm (WOA) for hyperparameter tuning to improve the performance of the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) model in credit score classification, precisely on the Kaggle Credit Score Classification dataset. Hyperparameter tuning is crucial but can be time-consuming and lead to poor selection. The results show WOA's success in optimizing both models with a performance increase of 1- 4% on each evaluation metric. WOA also surpassed Genetic Algorithm (GA) in tuning LSTM and was comparable to GA in tuning SVM. Despite the study's constraints, WOA found more optimal hyperparameters for each model and outperformed GA, indicating the potential for improved credit scoring.