Fiscally Intelligent Financial Crime Defense
Keywords:
FinOps,Cloud-native architecture,Artificial intelligence in finance,Financial crime detection,Anti-money laundering (AML),Regulatory technology (RegTech),Cost optimization,Compliance automation,Distributed machine learning,Real-time fraud detection,Scalable AI systems,Big data analytics,Microservices architecture,Risk and compliance management,Explainable AI (XAI).Abstract
Financial crime detection systems are becoming increasingly common across the financial industry, yet their infrastructure and operational costs remain a significant concern. Applying Financial Operations (FinOps) principles to AI-driven, cloud-native systems can help govern resource usage and keep costs within allocated budgets. This paper proposes a FinOps-aware AI application framework for detection, monitoring, and forensic systems, supporting both detection pipelines for predefined tasks and continuous monitoring and forensic models for real-time scoring. Resource allocation and auto-scaling policies enforce cost governance by provisioning additional resources during peak loads, when anomalies or fraud are suspected, and during continuous monitoring or batch processing runs. Cost-aware strategies are defined for both model training and inference, while monitoring and alerting for underlying cloud resources, FinOps dashboards for decision-makers, and patterns for automated FinOps-aware deployment of AI services are integrated directly into the architecture.
Open-source, cloud-native software components enable the framework’s implementation and testing across financial institutional, regulatory, and private cloud environments. The full process is mainstreamed and validated within regulatory sandboxes, supporting authorities that require such capabilities but lack the resources to build them independently. The proposed FinOps-aware framework aims to optimize the cost of AI operations across various financial domains, including cross-border transaction supervision and anti-money laundering (AML).
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Copyright (c) 2026 Jai Kiran Reddy Burugulla, David Schneider (Author)
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