Specialist: AML (Client Risk Assessment) at Capitec Bank

Main Purpose

• If you’re passionate about combating financial crime and enjoy harnessing the power of technology, this role will let you make a real impact. As a specialist in Anti Money Laundering (AML), Client Risk Assessment, you’ll leverage your expertise to deliver innovative solutions and high-quality results that protect the bank and its clients. By collaborating with teams across the organization, you’ll help shape the future of financial crime prevention. This is your opportunity to tackle the challenges of financial crime head-on while using cutting-edge tools and your specialist knowledge to make a meaningful difference every day.

Specialist: Anti-Money Laundering (Client Risk) your primary responsibilities will include:

• AML Standards & Frameworks: Design, implement, and continuously improve AML policies and best practices across the organisation.
• Specialist Oversight: Provide expert guidance on AML controls, monitoring, and project delivery.
• Client Risk Assessment: Lead development of CRA methodologies, including model design, risk factor analysis, and regulatory alignment (FICA, FATF).
• Technology Partnership: Act as the SME for CRA systems, translating regulatory needs into technical requirements and product roadmaps.
• Stakeholder Management: Collaborate with Compliance, Tech, and Business Units, and present updates to senior management and governance forums.
• Industry Engagement: Advocate for AML risk management in regulatory meetings and industry groups.

The ideal candidate will have:

• At least 5 years’ relevant Financial Crime experience, risk management or data analytics role  within a financial / banking environment  
• Demonstrable, hands-on experience with the design, implementation, or management of client risk assessment models and methodologies. 
• AML regulatory requirement experience and proficiency 
• Proven experience in translating complex business and regulatory requirements into functional specifications for technology teams 
• Experience with data analysis and a strong understanding of statistical modelling concepts is essential. 
• Strong understanding of risk modelling frameworks and the model development lifecycle. 
• Familiarity with data query and analysis tools (e.g., SQL, Python, R) is a significant plus