Pragatheswari D

Transaction Monitoring Analyst with 4+ years of experience investigating AML/CTF alerts, identifying suspicious transactions and customer activity, and escalating matters in line with regulatory obligations within large, regulated financial institutions. Skilled in reviewing, managing, and clearing alerts generated by AML transaction monitoring systems, applying typology-based red flag identification, and supporting Suspicious Matter Report escalation processes. Hands-on experience with Actimize for alert investigation and case management, and exposure to broader AML/CTF workflow tools.

Strong working knowledge of the AML/CTF Act, suspicious activity investigation standards, and regulatory reporting requirements, with experience supporting Quality Assurance feedback and remediation actions. Comfortable managing competing priorities and high alert volumes while maintaining audit-ready documentation and strong attention to detail.

Australian Permanent

Resident available immediately.

Financial Crime Strategic Associate at JP Morgan Chase & Co. (2025-01 – Present)

Lead financial crime investigation governance across enterprise-wide fraud and AML/CTF programmes; design and maintain oversight frameworks — dashboards, decision logs, and tollgates — supporting 100+ stakeholders across risk, compliance, and operations

• Analyse customer and transactional data to identify emerging financial crime patterns, anomalies, and control gaps; escalate findings and develop remediation plans in line with AML/CTF regulatory obligations, supporting timely escalation to regulatory reporting functions where required
• Maintain audit-ready investigation documentation and governance artefacts ensuring full traceability, data integrity, and compliance with internal policy and regulatory requirements
• Review Quality Assurance and First Line Monitoring feedback on investigation outcomes, identifying and implementing remediation actions to strengthen investigation quality and consistency
• Support testing and implementation of new and enhanced detection rules; assess the impact of policy and procedure changes on detection controls and key performance metrics
• Present complex financial crime findings and risk assessments clearly to senior management and cross-functional stakeholders; chair governance forums and strategy review meetings
• Automated monthly financial crime reporting pipeline through SQL and Python, reducing operational effort by 92% and improving reporting accuracy and consistency
• Recognised with JP Morgan Chase Values in Action Award – Curiosity (June 2024) for innovation and excellence in analytical delivery

Financial Crime Analyst – Fraud Detection & AML Analytics at JP Morgan Chase & Co. (2023-04 – 2024-12)

• Reviewed, investigated, and cleared high volumes of transaction monitoring alerts by analysing large-scale customer and transactional datasets; identified fraudsters and mule accounts, applied typology and red-flag based assessment, and escalated high-risk and potentially suspicious matters in accordance with AML/CTF obligations
• Used Actimize to manage end-to-end alert investigation workflow, including case documentation, evidence gathering, and outcome recording for audit and regulatory purposes
• Escalated confirmed suspicious activity cases to senior investigators and regulatory reporting functions, contributing to Suspicious Matter Report assessments and supporting documentation
• Developed and optimised a mule detection framework targeting Zele token-based payment fraud; engineered a Python rule detection engine for Zele token validation, strengthening detection of scam-facilitated transactions
• Performed gap analysis and risk identification across 50+ detection controls; addressed Quality Assurance feedback and implemented remediation actions, documenting findings and maintaining 99%+ accuracy across all investigative outputs
• Achieved 22% reduction in monthly fraud losses through systematic detection rule optimisation, control refinement, and threshold calibration driven by quantitative transactional data analysis
• Built enterprise-scale transaction monitoring dashboards consolidating 50+ fraud prevention strategies via multi-source SQL integration through Snowflake; provided real-time operational visibility enabling rapid remediation of underperforming controls
• Clearly communicated financial crime risk findings, investigation outcomes, and detection performance metrics to 100+ stakeholders through Tableau and Power BI dashboards and written reporting

Data Analyst – Risk Analytics & Business Intelligence at Tata Consultancy Services (2021-05 – 2022-08)

Citi Bank Engagement

• Designed end-to-end analytics solutions supporting credit loss forecasting and financial risk assessment within a regulated banking environment; built interactive dashboards and reports across complex multi-dimensional transactional datasets
• Established data validation and governance frameworks through SQL-based quality checks and testing protocols; maintained 99%+ accuracy across all analytical outputs with comprehensive audit-ready documentation and full data lineage traceability
• Optimised reporting workflows and data pipelines through SQL and Python automation, achieving 35% improvement in reporting efficiency and enabling consistent delivery across global time zones
• Produced requirements documentation, gap analysis artefacts, and best practice guides; facilitated knowledge transfer sessions and built analytical capability across the team

Bachelor of Engineering in Computer Science & Engineering – Bannari Amman Institute of Technology (2021)