Machine Learning Assisted Credit Risk Assessment in Financial Institutions

Abstract

This research introduces a novel, hybrid machine learning framework for credit risk
assessment that diverges from conventional statistical and single-model approaches. The
proposed 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 neural
networks, support vector machines, and a novel variant of the C4.5 decision tree algorithm
modified for imbalanced data. The innovation lies not in the individual algorithms, but
in their orchestration: the SOM’s segmentation is dynamically informed by a meta-learner
that analyzes temporal shifts in macroeconomic indicators, allowing the cluster definitions
to adapt pre-emptively to emerging financial stress. We formulate the credit decision not as
a binary classification, but as a multi-objective optimization problem seeking to balance default probability, expected loss, and customer lifetime value, a perspective seldom adopted
in mainstream literature. Testing on a proprietary dataset of over 500,000 anonymized
loan applications from a European banking consortium reveals that our cascaded ensemble
achieves 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-risk
clusters. The findings demonstrate that a structured, heterogeneous modeling approach,
cognizant of both micro-feature patterns and macro-economic contexts, can substantially
advance 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 predictive
accuracy.