Sustainable Data Mining Framework for Regulatory Compliance and Settlement Analytics in Global Financial Markets

The paper provides a Sustainable Data Mining Framework (SDMF), which is aimed at enhancing regulatory compliance and settlement analytics in world financial markets. The architecture incorporates big data processing, AI-based anomaly detection, and energy-saving computing into a cloud-native architecture. Quantitative experiments were conducted to compare SDMF with conventional systems based on the real time data of financial transactions. This resulted in an increased speed of processing, less use of energy and more accuracy of compliance. The statistical analysis regarding the significance of these gains proved right. It has been shown that sustainability can co-exist with high-computational performance, making regulation transparency and environmental responsibility in the current financial data ecosystem possible.