Living Agent Networks for Fraud and Risk Analytics
DOI:
https://doi.org/10.5281/achs0e66Keywords:
Adaptive Multi-Agent Systems (AMAS),Fraud Detection Analytics,Enterprise Risk Forecasting,Secure Distributed Intelligence,Agent-Based Decision Support,Machine Learning for Risk Assessment,Collaborative Autonomous Agents,Cybersecurity and Fraud Prevention,Predictive Risk Modeling,Trustworthy AI for Financial Security.Abstract
Fraudster behavior modeling and enterprise risk forecasting, though pursued as separate research areas, exhibit underlying similarities that point toward opportunities for integration. Recent work on a multi-agent architecture built around similarity-based reasoning and adaptation to evolving user behavior has shown promise in fraud prediction, echoing the logic behind the classical k-nearest neighbor algorithm and its many contemporary variants across diverse application domains. This resemblance suggests that adaptive architectures designed for fraud prediction may generalize effectively to enterprise risk forecasting. This study examines adaptation strategies, learning paradigms, and associated security and privacy considerations, with particular emphasis on fraud analytics, data ingestion and preprocessing, and risk modeling, in order to establish an evidence-based and conceptually coherent foundation for the field.
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No new datasets were generated or analyzed in this study. Data sharing is not applicable to this article.
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