AI-Driven Predictive Analytics Framework for Anti-Money Laundering Risk Management and Financial Infrastructure Protection in U.S. Banking Systems

Abstract

U.S. banks run anti money laundering (AML) programs that must detect suspicious activity at scale, document decisions, and file timely Suspicious Activity Reports (SARs) while protecting SAR confidentiality (31 C.F.R. § 1020.320, 2023; FFIEC, 2021). Illicit finance increasingly exploits payment rails and multi account networks, raising compliance exposure and operational risk (FinCEN, 2021). This manuscript proposes a compliance aware, AI driven predictive analytics framework for U.S. banking. The framework integrates three layers: supervised alert ranking and suppression using calibrated gradient boosted trees (Chen & Guestrin, 2016); unsupervised anomaly detection for emerging typologies under label lag (Liu et al., 2008); and graph learning to capture relationship centered laundering structures such as mule rings and layering (Kipf & Welling, 2017; Hamilton et al., 2017). We specify a data and feature pipeline for deposits, wires, ACH, cards, and instant payments, including preprocessing, entity resolution, feature engineering, and class imbalance controls using cost sensitive objectives and precision recall analysis (Elkan, 2001; Saito & Rehmsmeier, 2015). Explainability is embedded as an evidence packet for investigators and validators using SHAP, LIME, counterfactuals, and graph explanations (Lundberg & Lee, 2017; Ribeiro et al., 2016; Wachter et al., 2018; Ying et al., 2019). Privacy preserving options, including federated learning, secure aggregation, and differential privacy, enable cross portfolio learning without centralizing sensitive customer data (Dwork et al., 2006; McMahan et al., 2017). Deployment guidance addresses real time scoring, latency targets, drift monitoring, and integration with case management and BSA e filing.