OPTI-Recourse: Interpretable Credit Risk Prediction and Actionable Recourse

The main objective of OPTI-Recourse is to create a modern, ethically sound, and transparent system for evaluating credit risks that would efficiently combine the high predictive ability with features like fairness, interpretability, and the provi- sion of loan applicants with the feedback that they can act upon. OPTI-Recourse combines various cutting-edge machine learning models, including an Optuna-optimized XGBoost, and several benchmark algorithms, e.g., logistic regression and random forest, that have been trained on real-world, highly imbalanced credit datasets. The pivotal point of this research work is aOPTI-Recourse is an AI system designed for credit scoring with a focus on fairness, interpretability, and user empowerment. The system leverages SHAP and LIME for global and local interpretability, respectively, helping analysts and end users to understand the model’s decision-making process. Besides, the project implements recourse and counterfactual explanations that, among other things, will allow rejected applicants to receive a clear, feasible roadmap to improving their creditworthiness and, consequently, the probability of getting approved in the future. The whole pipeline is internally capable of comprehensive data preprocessing, sophisticated feature engineering, fairness and bias measurement, hyperparameter tuning, model validation, as well as deployment via a user-friendly Streamlit dashboard. By embracing transparency, precision, and the provision of actionable feedback, OPTI-Recourse is a socially responsible AI that can play a vital role in trust building, ease the process of regulatory compliance, and change the credit scoring ways of the modern era.