| Title | Explainable Artificial Intelligence for Credit Risk Assessment in Digital Lending Platforms |
| Research Area | Explainable Artificial Intelligence |
| Abstract | The rapid growth of digital lending platforms has transformed the traditional credit assessment pro-cess. Financial institutions and technology-driven lending organizations can now evaluate borrowers using large volumes of structured and behavioral information. Conventional credit-scoring ap-proaches generally depend on predefined rules, historical financial records, and manually designed indicators. Although machine learning can improve predictive capability, highly complex models may produce decisions that are difficult for borrowers, financial institutions, and regulatory authori-ties to understand. This lack of transparency creates an important challenge when artificial intelli-gence is used to make or support credit decisions. Explainable Artificial Intelligence (XAI) provides a potential solution by generating understandable explanations of model predictions while preserving the predictive capabilities of advanced machine-learning algorithms.
This paper proposes an explainable artificial intelligence framework for credit risk assessment in digital lending platforms. The proposed framework integrates borrower-data preprocessing, feature engineering, machine-learning-based risk prediction, explanation generation, fairness assessment, and human review. The research discusses the application of interpretable models as well as post-hoc explanation techniques for complex models. A credit-risk score is formulated by combining pre-dicted default probability with borrower and application characteristics. The paper further introduces an explanation-ranking mechanism intended to identify the most influential factors behind individual credit decisions. Special attention is given to data imbalance, feature leakage, fairness, privacy, model drift, and explainability. The proposed framework is designed to support responsible lending by allowing financial institutions to understand not only whether a borrower is considered high risk, but also which factors contributed to that assessment. The paper concludes with an evaluation meth-odology, limitations, and future research directions involving privacy-preserving machine learning, counterfactual explanations, multimodal financial data, and continuously monitored responsible-AI systems |
| Keywords | Explainable Artificial Intelligence, Credit Risk, Machine Learning, Digital Lending, Credit Scoring, Responsible AI, Financial Technology, Risk Assessment, Fairness, Model Interpret-ability |
| Paper Status | Published |
| Volume | 2 |
| Issue | 4 |
| Published On | 09/08/2026 |
| Published File |
IJSRTD_4889.pdf
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