Optimizing Enterprise Financial Risk Management: An Advanced Ensemble Machine Learning Framework for Transactional Fraud Mitigation and Business Continuity

Abstract

Transactional fraud represents a critical threat to enterprise risk management (ERM) and corporate governance, draining billions annually from global financial infrastructures. The Nasdaq Global Financial Crime Report estimates total fraud losses at $485.6 billion annually, with credit card fraud alone accounting for $28.6 billion. Managing this operational vulnerability is severely challenged by data asset asymmetry, where legitimate transactions dwarf fraudulent anomalies by a ratio exceeding 500:1. Standard classification models trained on such skewed data fail systematically, either ignoring fraud entirely or producing unacceptably high false-alarm rates that disrupt business operations. This study presents a robust predictive risk framework designed to safeguard business continuity and enhance corporate defense capabilities. Combining an unsupervised Isolation Forest anomaly metric with optimized machine learning ensemble layers (LightGBM, Random Forest, XGBoost), we introduce a highly interpretable risk assessment pipeline aligned with GRC mandates. To optimize business exposure controls, our architecture utilizes cost- sensitive learning weights that heavily penalize missed fraud over false alarms, reflecting the asymmetric operational cost structure facing financial institutions. Hyperparameter optimization via Optuna Bayesian search across 80 trials ensures the framework performs at the frontier of detection capability. A stacking meta-learner further consolidates base model outputs into a unified risk signal. Evaluated against 283,726 real-world transaction records from the European Credit Card Fraud Dataset, the proposed enterprise framework achieves 99.5% classification accuracy and 23.1% precision on imbalanced data streams, substantially outperforming current industry benchmarks. The LGBM+RF hybrid configuration achieves PR-AUC of 0.6729, exceeding the best published benchmark. Integrating boundary-focused ADASYN data synthesis yields an F1-risk capture score of 84.6%, nearly triple the performance of random undersampling under identical conditions. Cross-validation confirms stable generalization with mean F1 of 0.9523 across five folds. By providing clear algorithmic interpretability via SHAP feature attribution alongside high- precision mitigation, this framework serves as a scalable solution for corporate compliance, financial sector asset protection, and resilient business continuity systems.