A Bayesian risk aggregation framework that fuses behavioral, transactional, and network-topological signals into a unified posterior fraud probability estimate is proposed, enabling continuous model adaptation without full retraining.
Abstract
Financial fraud detection in enterprise environments presents persistent challenges due to evolving attack patterns, high transaction volumes, and the inherent limitations of static rule-based systems. This paper proposes a Bayesian risk aggregation framework that fuses behavioral, transactional, and network-topological signals into a unified posterior fraud probability estimate. The framework employs conjugate Gaussian-Gaussian updating to incorporate confirmed fraud labels incrementally, enabling continuous model adaptation without full retraining. An adaptive drift detection mechanism monitors rolling classification performance and triggers weight recalibration when statistically significant concept drift is detected. The decision engine applies a three-zone routing architecture — automatic block, human review, and pass-through — with per-signal log-odds attribution to satisfy explainability requirements under applicable regulatory standards. Experimental evaluation against published benchmark systems demonstrates that the proposed framework achieves competitive detection performance while maintaining interpretability and operational adaptability. The architecture is designed for production deployment in enterprise transaction processing environments and addresses key limitations of existing approaches including static thresholds, single-modality detection, and opaque decision logic.
Financial fraud detection is commonly evaluated as a stationary classification problem even though transaction behavior, fraud tactics, reporting practices, and defensive controls change over time. This study examines the consequences of concept drift for predictive performance, drift detection, explanation stability, and governance. A temporally ordered financial transaction stream of 9,000 observations was constructed from a documented financial simulation design, with 14 behavioral and transactional predictors and 530 fraud events. The experiment imposed identifiable sudden, gradual, incremental, and recurring changes after an initial training and validation period. Three static models, logistic regression, random forest, and histogram gradient boosting, were compared with online logistic regression, sliding-window random forest, and drift-triggered random forest under prequential evaluation. Page-Hinkley, DDM, KSWIN, and an ADWIN-style window detector were assessed using detection delay, false alarms, and missed changes. Explanation stability was measured through permutation importance rank agreement across pre-drift, post-drift, and late-stream periods. Static random forest achieved mean PR-AUC of 0.192, while online logistic regression achieved 0.109 but substantially higher mean recall, 0.596 versus 0.234. None of the adaptive models produced a statistically significant PR-AUC improvement over static random forest. Page-Hinkley generated fewer false alarms than the more sensitive window detector, while DDM and KSWIN missed all prespecified changes under the selected operating conditions. Explanation rankings were unstable after sudden drift. The findings show that adaptation is not automatically superior. Effective fraud operations require selective retraining, threshold governance, explanation review, analyst validation, and explicit decision rights. The paper develops the Adaptive Fraud AI Governance Framework to connect technical monitoring with institutional accountability.
Zachary T Caldwell, Chloe A Morrison, L. Fischer· International Journal of Man...· 0 citations
Real-time payment fraud detection in cloud-native platforms is constrained by two forces that are usually optimized separately: the statistical difficulty of highly imbalanced and drifting transaction streams, and the operational latency budget of synchronous microservice inference. Prior cloud-native payment intelligence work has shown that lightweight models can offer attractive latency and footprint properties, while tree ensembles can improve ranking quality at substantially higher inference cost. This paper extends that line of work by proposing UDMR, an uncertainty-aware dynamic model routing framework that invokes a fast-linear scorer for every transaction and routes only high-risk, uncertain, or drift-exposed transactions to a heavier expert model. The router combines fast-model risk, decision-margin uncertainty, and a training-window drift distance into a fixed single-row routing score, then selects the routing threshold and operating threshold on a validation stream under a minimum precision floor. Because the public ULB/Worldline fraud data were not available inside the execution sandbox, the empirical artifact uses a deterministic synthetic transaction stream with 60,000 chronological transactions, a 0.91% fraud rate, and controlled concept drift; the released code automatically uses a real ULB-style creditcard.csv file when supplied. On the synthetic drift benchmark, the random-forest expert achieves the strongest average precision (0.859) and F1 (0.803) but requires serving every request through the heavy model. UDMR routes 39.6% of test transactions to the expert, reaches average precision of 0.557 and F1 of 0.528, and reduces median single-row latency from 2429 microseconds for the expert-only service to 345 microseconds. The results show that uncertainty-aware routing is a practical cloud-native design pattern when median capacity, alert precision, and graceful degradation matter, while also revealing that high routing rates still expose p95/p99 latency to expert-model cost.
Kenji Mori· Journal of Mathematical Fina...· 0 citations
The typical approach to fraud detection in retail environments relies on rules-based, static detection engines. Such systems perform well against simple, known attack types but struggle against sophisticated, emerging threats and organized financial syndicates. Moreover, rules-based systems produce high false positive rates that seriously degrade the experience of legitimate customers. This paper presents a Graph Artificial Intelligence framework to support high-throughput and streaming retail transactions with built-in explainability. Our approach treats transactions as a dynamic and heterogeneous graph to capture complex relational dependencies and structural anomalies in real time. We validate the framework using a curated subset of the publicly available IEEE-CIS Fraud Detection dataset under a streaming scenario, evaluating both latency and predictive accuracy. Combined with PyTorch Geometric for graph neural network construction and SHAP (Shapley Additive exPlanations) for post-hoc model interpretation, our system delivers high prediction accuracy and human-interpretable decision making. The proposed model shows strong accuracy and recall compared to traditional baselines, while flagged transactions are explained through visual subgraph evidence. This transparency enables fraud analysts to validate alerts efficiently, reducing review time and operational cost in e-commerce environments.
Jose Prabhu Michael Singarayan, Jayakumar Ramalingam, Neetu Uthaman· 2026 International Conferenc...· 0 citations
Fraud detection in digital financial transactions is traditionally formulated as a binary classification problem, although real-world fraud investigation requires analysts to prioritize a limited number of suspicious transactions according to operational risk and potential financial impact. This study reformulates fraud detection as an amount-aware operational ranking problem for fraud-risk prioritization. Transactions are organized into time-window query groups, and fraudulent transactions are assigned graded relevance based on training-only transaction-amount quartiles, enabling the ranking objective to distinguish low- and high-severity fraud without relying on proprietary cost matrices. The proposed formulation is implemented using a representative Learning-to-Rank framework based on LambdaMART, while an out-of-fold XGBoost risk score is incorporated as an auxiliary feature to refine the ranking representation rather than serve as the primary contribution. Experiments conducted on a public credit-card fraud dataset using chronological validation and future-holdout testing demonstrate that amount-aware relevance consistently improves severity-aware top-rank ordering compared with conventional binary relevance. The proposed HybridLTR_amount model significantly outperforms XGBClassifier and PureLTR_binary in terms of all-query NDCG@10, whereas its performance is not statistically different from PureLTR_amount, indicating that the primary empirical improvement is attributable to the amount-aware ranking formulation rather than the auxiliary hybrid component. Additional operational analyses show that high-risk transactions and fraudulent financial losses are concentrated within a compact top-ranked segment, while budget-oriented evaluation demonstrates the practical value of the proposed formulation under limited analyst review capacity. These findings establish amount-aware operational ranking as an effective formulation-centric framework for operational fraud-risk prioritization rather than as a new classification algorithm.
H. Hartatik· Journal of Computing Theorie...· 0 citations
Results indicate that while academic models effectively detect fraud rings in digital transactions offline, the proposed multimodal model achieves higher throughput and protects the authentication perimeter of the transaction system from synthetic media and prompt injection attacks.
David Dimitriu· Business Administration Stud...· 0 citations
The suggested framework employs a stacked ensemble approach in which Logistic_Regression, Decision_Tree, and XGBoost act as base models, while a meta-classifier produces the final fraud prediction.
G. S. Rahul Gorpade, A. M· International Research Journ...· 0 citations
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