An Hybrid Algorithmic Framework for Credit Risk Assessment Based on Ensemble Learning Techniques

Abstract

Credit risk assessment plays a crucial role in the functioning of lending institutions, as it impacts loan approval decisions, pricing, capital structure, portfolio management, and financial stability. While traditional score cards and single predictive models can deliver clear and effective decisions that are also operationally efficient, they can be difficult to build to capture the non-linear relationships, the complex interactions, evolving borrower patterns and the data heterogeneity. It suggests a hybrid algorithmic framework which integrates data preparation, interpretable baseline modeling, ensemble learning, probability calibration, optimization of decision-threshold and post-deployment monitoring. The framework adopts logistic regression as a transparent benchmark and ensembling the same through validation-based ensemble method, by combining tree-based learners such as random forests and gradient-boosted decision trees. It also includes methods for handling class imbalance, temporal validation, explainability, fairness checks and governance controls. The paper does not make any claims of empirical performance without data, but rather provides a research design and evaluation protocol to be used to test the framework with borrower-level repayment outcomes. Some key metrics for core evaluation are: area under the receiver operating characteristic curve (ROC-AUC), precision-recall AUC, Brier score, calibration curves, expected credit loss, and subgroup performance. The paper proposes a new evaluation method of the usefulness of a hybrid system beyond accuracy ranking that takes into account calibration, stability, interpretability, fairness and decision utility. The proposed approach provides a well defined structure for building credit-risk models that are technically sound and appropriate to responsible use in the lending business and subject to local regulatory and institution-specific model validation.