Evaluating Risk Management Strategies Using Analytical Frameworks: Multi Method Study Across Industries in U.S.
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Abstract
This study addressed the critical inefficiency of siloed risk management approaches across U.S. industries, where fragmented methodologies for fraud detection (healthcare), AML monitoring (finance), and fiscal forecasting (public sector) incur annual losses exceeding $100 billion. We developed a unified analytical framework integrating machine learning (Isolation Forest, LSTM), network analysis (GNNs), and econometric modeling (ARIMA) to enable cross-sector risk interoperability. The methodology processed: (1) 500,000 anonymized Medicare claims (CMS/RADV), (2) synthetic FinCEN SARs networks emulating money laundering patterns, and (3) CBO macroeconomic indicators, evaluated through multi-criteria validation (precision-recall, MAE, PageRank centrality). Results demonstrated significant improvements over sector-specific baselines: fraud detection achieved 89.7% recall (Δ+27.4%, p<0.01) with SHAP analysis revealing claim frequency and provider networks as top predictive features; AML precision increased by 32.7% through transaction graph clustering (modularity=0.83); fiscal forecast errors reduced by 29.5% via hybrid LSTM-ARIMA modeling. The framework’s interpretability was validated through three lenses: (a) clinical relevance of detected Medicare fraud patterns (OIG audit alignment), (b) AML network topology consistency with FinCEN typologies, and (c) fiscal shock responsiveness within CBO confidence intervals. Economic simulations projected $12.5B annual savings from integrated implementation (ROI 3.6:1), though legacy system integration costs varied by sector (public: +47% vs banking: +71%). The study’s scientific contribution is threefold: (1) a validated protocol for cross-domain risk variable harmonization, (2) demonstration of interpretable AI’s superiority in regulated environments (SHAP-driven false positive reduction), and (3) quantification of cyber-physical risk couplings (fraud-AML volatility r=0.52±0.03). These advancements provide policymakers with a replicable template for national risk infrastructure modernization, particularly in API standardization and adaptive control deployment.