Credit score prediction using genetic algorithm-LSTM technique

Abstract

In data mining, the goal of prediction is to develop a more effective model that can provide accurate results. Prior literature has studied different classification techniques and found that combining multiple classifiers into ensembles outperformed most single classifier approaches. The performance of an ensemble classifier can be affected by some factors. How to determine the best classification technique' Which combination method to employ' This paper applies Long Short-Term Memory (LSTM), one of the most advanced deep learning algorithms which are inherently appropriate for the financial domain but rarely applied to credit scoring prediction. The research presents an optimization approach to determine the optimal parameters for a deep learning algorithm. The LSTM parameters are determined using an optimization algorithm. The LSTM parameters include epochs, batch size, number of neurons, learning rate and dropout. The results show that the optimized LSTM model outperforms both single classifiers and ensemble models.