Explainable AI for Credit Scoring in FinTech

Abstract

Credit scoring is a critical component in the FinTech sector, influencing lending decisions that impact both financial institutions and consumers. Traditional credit scoring models often rely on statistical or rule-based approaches, but recent advances in Artificial Intelligence (AI) have introduced machine learning models that improve predictive accuracy. However, these models—especially complex ones like deep neural networks and ensemble methods—suffer from a lack of transparency and interpretability, which raises concerns around trust, fairness, and regulatory compliance. Explainable AI (XAI) techniques have emerged to address these challenges by providing insights into the decision-making processes of AI models. This paper explores the application of XAI techniques in credit scoring within the FinTech industry. We review key methods such as feature importance, local interpretable model-agnostic explanations (LIME), SHapley Additive exPlanations (SHAP), and rule-based models to make AI-driven credit decisions more interpretable. The research methodology includes developing credit scoring models on real-world datasets and applying XAI methods to interpret model outcomes. Our results demonstrate that XAI techniques can effectively identify the critical features influencing credit decisions, enabling stakeholders to better understand, validate, and trust AI predictions. We discuss the trade-offs between model complexity, accuracy, and interpretability, emphasizing the need for transparent models in high-stakes environments such as lending. The paper concludes by highlighting the importance of integrating explainability in AI-driven credit scoring systems to comply with regulatory requirements like the EU’s GDPR and to promote ethical lending practices. Future research directions include improving the robustness of explanations, user-friendly visualization tools, and integrating explainability with fairness metrics.