An Integrated Explainable Deep Learning Framework for Loan Default Prediction and Credit Risk Decision Support System

Abstract

The credit risk assessment remains a critical challenge for financial institutions as manual and semi-automated loan evaluation processes often produce inconsistent decisions, high default rates, and operational inefficiencies. This paper discusses an Integrated Data-Driven Loan Management Framework that comprises an Artificial Neural Network (ANN) for credit default prediction, Structured Query Language (SQL) to systematically extract and transform data, and Interactive Visual Analytics Dashboards to aid in providing transparency within the decision-making process. For the study, a public loan dataset was used to pre-process the target dataset containing a total of 38,577 records and 24 attributes with pre-processing techniques such as feature engineering, one-hot encoding and class re-balancing with the use of Synthetic Minority Oversampling Technique (SMOTE) yielding 43 model-ready inputs. The final ANN architecture was developed with 2 hidden layers (43 and 21 neurons with ReLU activation) and a sigmoid output neuron for binary classification. SHapley Additive exPlanations (SHAP) were computed to provide interpretability for each prediction made by the model. The overall accuracy of the model is 87% and the weighted precision, recall, and F1-score are 0.87 for the held-out test set (n = 12,858). The framework is implemented as a web-based decision support tool using Flask that enables users to receive risk scores in real-time along with explainable outputs and visual dashboards. The results from this experimentation indicate that an integrated pipeline (including data querying, predictive modeling, interpretability, and visualization) provides better decision-making and stakeholder transparency than using a model in isolation; therefore, it serves as a proof-of-concept prototype for intelligent loan management in banks and other financial institutions.