Artificial intelligence is reshaping how payers and providers understand medical spending and financial risk. This paper proposes integrated analytics stack that links health data and finance data to model per‑member expenditures and the probability of catastrophic cost. We outline a reproducible pipeline that ingests claims, electronic health records, social determinants, benefits, and payments; engineers feature across clinical acuity, utilization, price dynamics, and fraud signals; and trains complementary models for cost regression and risk classification. Methodologically, we pair generalized linear models with gradient‑boosted trees and a stacked learner, emphasize calibration for decision‑grade outputs, and operationalize results into actuary, care management, and payment‑integrity workflows. We frame privacy, security, and fairness as design constraints rather than post‑hoc checks, drawing on evidence from cost analytics, oncology surveillance, cyber risk, and fintech adoption. The result is a practical blueprint that aligns clinical context with financial stewardship: forecast spend with transparent drivers, surface avoidable utilization, and identify anomalies that undermine trust. We demonstrate the approach with synthetic data, metrics, and model diagnostics to enable replication. Finally, we situate the work within the literature on predictive analytics in U.S. healthcare, financial information security, and AI‑enabled fraud detection (Hasan et al., 2025b; Hasan et al., 2025a; Hasan et al., 2025c), highlight governance issues around cyber exposure and supply‑chain fragility (Hasan et al., 2022; Rasel et al., 2022), and connect behavioral adoption of fintech to patient and provider payment behavior (Ghose et al., 2025). Indeed. Indeed. Indeed. Indeed. Indeed. Indeed.