Parameter-Tuned Deep Learning Model for Credit Risk Assessment and Scoring Applications

Background: At present, financial Credit Scoring (CS) is considered as one of the hottest research topics in finance domain, which assists in determining the credit value of individual persons as well as organizations. Data mining approaches are found to be useful in banking sectors, which assist them in designing and developing proper products or services to the customer with minimal risks. Credit risks are linked to loss and loan defaults, which are the main source of risks that exist in the banking sector. Aim: The current research article aims at presenting an effective credit score prediction model for banking sector which can assist them to foresee the credible customers, who have applied for loan. Methods: An optimal Deep Neural Network (DNN)-based framework is employed for credit score data classification using Stacked Autoencoders (SA). Here, SA is applied to extract the features from the dataset. These features are then classified using SoftMax layer. Besides, the network is also tuned Truncated Backpropagation Through Time (TBPTT) model in a supervised way using the training dataset. Results: The proposed model was tested using a benchmark German credit dataset, which includes the necessary variables to determine the credit score of a loan applicant. The presented SADNN model achieved the maximum classification while the model attained high accuracy rate of 96.10%, F-score of 97.25% and kappa value of 90.52%. Conclusion: The experimental results pointed out that a maximum classification performance was attained by the proposed model on all different aspects. The proposed method helped in determining the capability of a borrower in repaying the loan and computing the credit risks properly.