A Multi-Method Evaluation of Risk Management Strategies Using Analytical Frameworks Across U.S. Industries

This paper discusses the significant cost inefficiency of solid risk-management efforts in United States businesses, whereby the disintegrated schemes of fraud detection (in healthcare), anti-money-laundering (in finance), and fiscal projection (in the public sector) generate a yearly loss amounting even beyond a hundred billion dollars. The authors build an integrated analytical framework with a combination of machine-learning techniques (Isolation Forest, LSTM), graph-based network-based analysis (GNNs) and econometric modeling (ARIMA) to achieve the interoperability of the risk assessment of various sectors. The three data sets are (1) 500,000 anonymized Medicare claims (CMS/RADV), (2) synthetic FinCEN SARs networks to simulate money-laundering dynamics and (3) CBO macroeconomic variables. Such validation as multi criteria (precision-recall, MAE, PageRank centrality) applies to the arranged performance. It shows excellent results above the specific criteria of the sectors: intra-sector baseline recall of the fraud detection matter reached 89.7 (delta +27.4%, p<0.01), with the SHAP analysis graphically illustrating that the claim frequency and provider networks were the highest predictive factors; AML precision is increased 32.7% through transaction graph clustering (modularity=0.83 ); errors in fiscal forecasts are decreased by 29.5 per cent due to hybrid LSTM-ARIMA modeling. Interpretability is confirmed by three folds which include; (a) Clinical relevance of identified patterns of Medicare fraud predicated on OIG audits, (b) topography of the AML network congruent with FinCEN typologies, (c) sensitivity of fiscal shock within CBO intervals of confidence. The potential Economic savings of integrated implementation are projected in six thousand millions of dollars per year in 2015 according to economic simulation (ROI 3.6:1), but they differ by sector (public: +47 % vs banking: +71%). The scientific contribution to the study involves (1) a tested approach against cross-domain risk variable harmonization, (2) the evidence that interpretable AI is most effective in controlled settings (SHAP-driven reduction of false positives), and (3) the measure of the cyber-physical risks couplings (fraud-AML volatility r=0.52 +/- 0.03). Such developments provide policymakers with a model that they can duplicate to modernize national risk infrastructure, specifically, regarding API standardization and deploying adaptive control.