Classification Performance for Credit Scoring using Neural Network

Credit scoring is an important part in controlling risk in financial companies. With the high number of non-performing loans, the assessment of potential new customers in financial companies has become a major focus of the financial industry. High accuracy credit scoring system can give better predictions on new customers and can change the company's economic growth and for better capital. This study will use a real world dataset, where data is obtained directly from a financial company and will be used to feed an Artificial Neural Network to differentiate between good and bad potential new customers. In this research, the Artificial Neural Network will be created, and then will be applied with the ensemble method. The idea of applying the parallel is to test whether it can increase accuracy or not. Finally, the output accuracy from the final class of the ensemble methods that resulted from the voting will be compared with the original unmodified single model and it shows that the modified multiple model surpasses the unmodified single model in terms of accuracy with a tradeoff on duration in the process