Strengthening U.S. Financial and Cybersecurity Infrastructure with AI‑Driven Fraud Detection and Risk Analytics

Abstract

The U.S. financial system faces converging threats from digital payment fraud, account takeover, and cyber intrusions that exploit highly connected infrastructure. This paper advances a practical blueprint for strengthening U.S. financial and cybersecurity infrastructure with artificial intelligence (AI) driven fraud detection and risk analytics. We synthesize pre–February 2023 research on supervised, unsupervised, temporal, and graph approaches; review evaluation for extreme class imbalance; and integrate privacy‑preserving collaboration and rigorous model risk management. The proposed reference architecture combines streaming decisioning with gradient‑boosted trees, sequence models, and graph risk scores; fuses cyber telemetry under zero‑trust principles; and embeds governance aligned to SR 11‑7/OCC 2011‑12, NIST SP 800‑53 and 800‑207, and the NIST AI Risk Management Framework 1.0 (Federal Reserve, 2011; OCC, 2011; NIST, 2020, 2023). A cost‑aware operating‑point procedure uses precision–recall curves and expected‑loss optimization to balance prevented fraud with analyst review costs and customer friction (Saito & Rehmsmeier, 2015; Davis & Goadrich, 2006). We also outline sector‑level coordination through CISA initiatives and FFIEC and NYDFS expectations to ensure operational resilience and auditability (FFIEC, 2011, 2021; NYDFS, 2017; CISA, 2022). Illustrative figures and tables show evaluation and governance mapping using simulated data. The discussion provides implementation guidance, policy alignment, and research opportunities on drift‑aware learning, adversarial robustness, and interoperable data sharing. Taken together, these measures can reduce fraud losses, compress attacker dwell time, and dampen systemic propagation, strengthening trust in digital finance without sacrificing privacy or accountability.