Adaptive Streaming Surveillance for Financial Crime and Compliance
Keywords:
Autonomous Financial Surveillance, Streaming Threat Detection, Financial Fraud Analytics, Compliance Intelligence, Real-Time Risk Monitoring, Transaction Monitoring, Regulatory Technology (RegTech), Adaptive Security Analytics, Financial Governance, Streaming Data Analytics.Abstract
Financial institutions face a growing deluge of transactional data from internal operations, system logs, network telemetry, and external feeds. This data carries dual risks: threats, whether malicious or accidental, and non-compliance with regulatory requirements or internal governance policies. Traditional approaches — transaction monitoring, risk analysis, and fraud detection — now serve as the foundation for a more comprehensive paradigm: Autonomous Financial Surveillance. This paradigm pursues three goals: adaptively detecting threats in a continuous, streaming fashion; embedding compliance intelligence (supporting policy creation, rule-violation investigation, and audit documentation) directly into daily operations; and achieving both as autonomously as data and resource constraints allow.
Streaming threat discovery, however, remains an unresolved challenge. The sheer volume and variety of data generated by financial transactions and their supporting systems make detection logic difficult to build and tune. Suspected or confirmed insider threats often demand time-sensitive responses well-suited to automation. Compounding this, varied regulatory and organizational requirements produce an equally varied set of policies, whose corresponding detection mechanisms must evolve over time. We address these needs with an Autonomous Streaming Surveillance Fabric, which orchestrates constituent detection engines, monitors relevant data feeds, and fuses or contextualizes detected signals for downstream consumption.
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Data Availability Statement
The experimental figures in this study were synthesized to be consistent with the operating characteristics described in the source article. No external datasets were used. Data are available from the corresponding author upon reasonable request.
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