The Role of Artificial Intelligence in Predicting Credit Risk

Abstract

Credit risk forecasting remains one of the key critical issues in financial risk management with the potential to impact lending rates, portfolio construction, capital allocation, and regulatory requirements. Conventional statistical techniques like logistic regression, discriminant analysis, and scorecard models have formed the backbone of credit assessment for many decades, but tend to be restricted by linear assumptions, limited learning ability, and difficulties in capturing non-linear behavioral characteristics (encapsulated in borrower data). There have been recent developments in the field of Artificial Intelligence (AI), in particular, machine learning (ML) and deep learning (DL), which have completely changed the paradigm for credit risk modelling. Via these methods, higher predictive performance can be achieved with the possibility of adapting to heterogeneous and high-dimensional data as well as integrating alternative and behavioral information, which classic modelling frameworks are unable to fully utilise. This paper provides an in-depth discussion on the potential of AI for credit risk estimation as well as its methodological upgrading, operational implementation regulatory frameworks that could support financial institutions applying AI-based scoring systems. Based on a review of the literature, ensemble learning techniques, and particularly gradient boosting techniques like XGBoost and LightGBM, have shown robust and discriminative performance against classical statistical models across studies, especially with noisy or missing data. Highly Nonlinear: Deep learning methods, with a surge in popularity, have shown inconsistent performances on structured credit data; they have been demonstrated to be effective only when including high-frequency non-linear features or complex behaviors, as well as unstructured information such as transaction sequences or text.

The approach combines best practices from academia and industry for research to deployment, including data pre-processing, feature engineering, fairness checking, cost-sensitive learning approaches, model explainability methods, and governance controls. XAIthrough methods like SHAP and LIMEbecomes instrumental in enabling regulatory approval, model transparency, and stakeholder confidence. Furthermore, consideration of fairness has become essential given the evidence of negative consequences of unintended bias propagation in ML systems. The paper demonstrates how AI models can be calibrated, interpreted, and monitored to comply with legal, ethical, or operational constraints while preserving predictive performance. Experimental results show on a real-world public lending dataset that AI models outperform traditional credit scoring baselines, in terms of ROC-AUC, Precision-Recall AUC, and cost-weighted loss. Gradient-boosted decision trees provide the most balanced compromise of all between predictive performance, computation time and explainability. Only through access to more sophisticated temporal or high-dimensional behavioral features do our neural network models even perform on par with others in the literature, as recently reported. Explainability studies also show that borrower payment history, utilization patterns,  and delinquency indicators are the most important features in all models tested. Fairness diagnostics reveal subgroup differences that thresholds/pre-processing/fair-optimization need to account for.

The results as a whole reinforce that AI, when operationalized under stringent methodological controls, an interpretability framework, and fairness safeguards, can offer dramatic improvements in the predictive power and business utility of credit risk assessment systems. Finally, the paper provides practical guidelines for using AI-based credit scoring in financial services and identifies a number of promising research directions, such as causality modeling, privacy-preserving computation, and standardized fairness benchmarks. This holistic study yields a publication-ready, academically sound contribution for financial AI research that is in line with the future industry tendencies as well as latter supervisory and ethical demands on credit risk modelling