Machine Learning Assisted Credit Risk Assessment in Financial Institutions (gjstudies, 2026)
This research introduces a novel, hybrid machine learning framework for credit riskassessment that diverges from conventional statistical and single-model approaches. Theproposed methodology integrates a cascaded ensemble architecture, where a primary selforganizing map (SOM) performs an initial, topology-preserving segmentation of the applicant population based on high-dimensional behavioral and transactional features. Each resulting cluster is then processed by a specialized, secondary predictor—a committee of neuralnetworks, support vector machines, and a novel variant of the C4.5 decision tree algorithmmodified for imbalanced data. The innovation lies not in the individual algorithms, butin their orchestration: the SOM’s segmentation is dynamically informed by a meta-learnerthat analyzes temporal shifts in macroeconomic indicators, allowing the cluster definitionsto adapt pre-emptively to emerging financial stress. We formulate the credit decision not asa binary classification, but as a multi-objective optimization problem seeking to balance default probability, expected loss, and customer lifetime value, a perspective seldom adoptedin mainstream literature. Testing on a proprietary dataset of over 500,000 anonymizedloan applications from a European banking consortium reveals that our cascaded ensembleachieves a 12.7% improvement in the area under the receiver operating characteristic curve(AUC-ROC) and a 19.3% reduction in expected loss compared to a benchmark logistic regression model, while significantly enhancing the interpretability of decisions for high-riskclusters. The findings demonstrate that a structured, heterogeneous modeling approach,cognizant of both micro-feature patterns and macro-economic contexts, can substantiallyadvance the predictive robustness and economic utility of automated credit scoring systems.This work contributes a new architectural paradigm for risk modeling that prioritizes adaptive segmentation and multi-stakeholder outcome optimization over monolithic predictiveaccuracy.