The role focuses on transforming and automating Anti-Money Laundering (AML) processes to improve efficiency, reduce operational risk, and ensure compliance with regulatory requirements. The specialist will leverage data analysis, workflow optimization, and technology to deliver operational excellence.
Key Responsibilities:
Process Transformation & Automation
- Identify manual and repetitive tasks within AML processes and design solutions to improve efficiency and accuracy.
- Develop and implement process enhancements that reduce operational risk and improve scalability.
- Maintain and continuously improve existing workflows to ensure reliability and compliance.
Operational Excellence
- Conduct end-to-end reviews of AML processes to identify inefficiencies and control gaps.
- Recommend and implement process improvements aligned with regulatory requirements and internal governance standards.
Data Analysis & Reporting
- Build and maintain dashboards and reports to provide actionable insights to senior management.
- Analyze large datasets to identify trends, control weaknesses, and opportunities for improvement.
Compliance & Governance
- Ensure all process changes comply with MAS regulations and internal AML/CFT policies.
- Support audits, assurance reviews, and regulatory inspections by providing accurate documentation and evidence.
Stakeholder Engagement
- Work closely with AML operations teams, risk management, and technology partners to deliver transformation initiatives.
- Promote a culture of innovation and continuous improvement within AML teams.
Qualifications:
- Bachelor's degree in Finance, Business, Risk Management, Computer Science, Information Systems, or related field.
- Minimum 5 years of experience in AML operations, process improvement, or automation roles.
- Strong understanding of AML regulatory frameworks (MAS, FATF).
- Experience in process optimization and workflow design.
- Advanced proficiency in Microsoft Excel and Microsoft Office Suite.
- Strong analytical and problem-solving skills.
- Ability to work independently and with large datasets.
- Detail-oriented with a high level of accountability and integrity.
