Analyzed 10,000+ transactional records monthly to identify AML risk indicators, behavioral anomalies, and suspicious activity patterns — supporting SAR/UAR filing decisions and reducing false-positive escalations. Designed and automated data validation and reconciliation workflows in Excel and Python, improving data accuracy by 15% and cutting manual QA time by approximately 30%. Built and maintained 5+ Power BI dashboards and MIS reports tracking KPIs across compliance operations — used by management for weekly performance reviews and capacity planning. Conducted CDD/EDD reviews, PEP screening, and adverse media checks on high-risk customer accounts, contributing to a 20% reduction in compliance review errors. Collaborated with cross-functional stakeholders (compliance, operations, IT) to gather requirements, define data standards, and deliver reporting solutions that improved decision turnaround time. Developed an end-to-end customer behavior analytics pipeline (Python → SQL → Power BI), transforming raw operational data into segmented risk profiles and actionable compliance insights. Contributed to process improvement initiatives by mapping operational workflows, identifying bottlenecks, and recommending data-driven optimizations — aligned with Lean/Kaizen methodologies.