Credit Score Prediction using Support Vector Machine and Gray Wolf Optimization

Credit score prediction is a critical task in the financial industry, where the ability to accurately predict the creditworthiness of an individual is essential for managing risk and making informed lending decisions. In recent years, machine learning techniques have gained popularity in credit score prediction due to their ability to handle large amounts of data and complex relationships. Support Vector Machine (SVM) is a popular machine learning algorithm for credit score prediction. SVMs are a type of supervised learning algorithm that can be used for classification tasks, where the goal is to assign a new data point to one of two or more classes based on a set of features. However, SVM can be sensitive to hyper parameters it influence the performance credit score prediction. So, in this paper Gray Wolf Optimization (GWO) technique is used to optimize the hyper parameters of SVM for better prediction of credit score. GWO mimics the pack hunting behavior of gray wolves to search for the optimal hyper parameters of SVM, drawing inspiration from their social hierarchy and hunting behavior. The experimental results prove that the proposed SVM-GWO method has better performance in terms of accuracy, precision, recall and F-measure than SVM based credit score prediction.