Adaptive Decision-Support Systems for Transaction Monitoring, Fraud Detection, and Financial Early-Warning

Abstract

Transaction monitoring, fraud detection, and financial early-warning are typically built, staffed, and evaluated as three separate functions inside a financial institution, even though all three share the same underlying task: deciding, under time pressure and with a fixed analyst headcount, which of a large volume of automatically generated alerts deserves a human being’s attention next. The decision-support systems literature has long argued that a system built around this kind of recurring, high-volume human decision should be evaluated as a decision-support artifact rather than as a classification model alone, yet the machine learning literature on transaction monitoring and fraud detection has concentrated overwhelmingly on model accuracy, largely bypassing the question of what happens once an accurate model’s output reaches an analyst who must act on it. This gap matters because a substantial body of evidence from clinical and financial alert systems shows that raising a model’s detection rate without addressing the resulting alert volume produces alert fatigue: a state in which analysts, overwhelmed by a high proportion of alerts that do not warrant escalation, begin to dismiss alerts more quickly and less carefully, eroding the very detection gain the model was deployed to capture. This paper addresses that gap by developing the Adaptive Decision-support Engine for Prioritized Triage (ADEPT): a reference architecture that fuses transaction, relational, and account-behavior signals into a shared risk-scoring and early-warning engine, ranks the resulting alerts through a fatigue-aware triage queue sized to actual analyst capacity, attaches a governance-ready rationale to every alert an analyst sees, and closes the loop by feeding analyst dispositions back into the scoring engine through an active-learning update mechanism rather than a periodic, wholesale retraining cycle. Because no production deployment of this specific architecture exists to evaluate, the paper follows a design-science approach: the framework is built from and evaluated against peer-reviewed findings in decision support systems theory, adaptive and human-in-the-loop machine learning, alert fatigue and analyst decision-making under information overload, fraud detection and signal fusion across data sources, early-warning systems for financial and institutional distress, and explainability and governance in adaptive decision support, and every quantitative comparison presented two tables and four  figures spanning capability coverage, a twelve-month false-positive and analyst-fatigue trajectory, an alert-volume-to-confirmed-yield trade-off, and early-warning lead time by signal source is explicitly labeled as a literature-grounded  estimate rather than a measured result. The paper’s contribution is a synthesis of six otherwise separate literatures into a single adaptive decision-support pipeline that treats alert prioritization, analyst feedback, and early-warning signaling as first-class architectural concerns rather than downstream afterthoughts to a fraud-detection model, and a candid account.