Fraud Analytics as a Sequential Decision System: Integrating Machine Learning, Optimization, and Adversarial Learning
Abstract
Fraud analytics is fundamentally a decision problem under uncertainty, involving tradeoffs among detection performance, investigation costs, and operational constraints. Although statistical and machine learning models are widely used to detect anomalous behavior and estimate fraud risk, they are often deployed without explicit consideration of downstream decisions, strategic adaptation, and system-level dynamics. This tutorial presents a unified framework that reframes fraud analytics as a sequential decision system involving adaptive and adversarial agents. We review descriptive and predictive models and embed them within a decision-theoretic framework that captures tradeoffs between false positives, false negatives, and resource constraints. We further introduce adversarial risk analysis and related approaches to model strategic interactions between fraudsters and detection systems. By connecting prediction, optimization, and adversarial modeling, the tutorial highlights how decisions influence both operational outcomes and future data through feedback effects. This sequential perspective emphasizes the need for adaptive policies that account for evolving fraud behavior and changing system conditions. Examples from healthcare and financial fraud illustrate how analytical models support real-world decision making and resource allocation in high-stakes environments. Intended for researchers and practitioners across operations research, statistics, and data science, this tutorial provides an accessible synthesis that requires no prior background. By the end, the reader will understand how to frame fraud detection as a decision problem, evaluate the limitations of purely predictive approaches, and reason about adversarial adaptation within a unified sequential framework applicable across fraud domains.
Funding: This research was supported by the National Science Foundation [Award 2334268].
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