What you will be doing:
• Conduct and review transaction surveillance investigations, including complex and high-risk cases.
• Identify unusual transaction patterns, suspicious activities and emerging AML typologies through detailed analysis.
• Apply data analytics, network analytics and link analysis to uncover hidden relationships and financial crime risks.
• Support regulatory examinations, audits and risk reviews relating to transaction surveillance.
• Lead and execute threshold tuning, scenario calibration and optimisation to improve alert quality and surveillance effectiveness.
• Partner with Group and internal stakeholders on transaction surveillance system enhancements, configuration changes and strategic initiatives.
• Support AI/ML model implementation, monitoring and governance for suspicious activity detection.
• Manage system upgrades, UAT testing and deployment of new detection scenarios.
• Develop and maintain documentation for system configuration, tuning decisions and governance requirements.
• Monitor system performance and provide insights to support management decision-making.
• Develop SQL and/or Python scripts for data analysis, scenario testing and reporting.
• Keep abreast of emerging AML typologies, regulatory developments and surveillance technologies.
What we are looking for:
• Bachelor’s degree in Finance, Computer Science, Data Analytics, or related discipline.
• 6–8 years of experience in AML surveillance, with exposure to AI-based detection systems.
• ECF-AAMLP certification or above is required.
• Strong understanding of AML typologies, regulatory standards, and transaction monitoring platforms.
• Proven experience in threshold tuning, scenario calibration and system configuration.
• Proficiency in SQL and Python for data extraction, analysis, and scenario testing.
• Proven ability to manage cross-functional initiatives and collaborate with Group-level stakeholders.
• Strong communication, analytical thinking, and stakeholder engagement skills.
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