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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.