FDM is a global business and technology consultancy seeking a Project Leader within AI Transaction Monitoring to work for our client within the financial services sector. This is initially a 12-month contract with the potential to extend and will be a fully remote role.
Our client is looking for a results-orientated seasoned Project Leader with a strong background in Anti–Money Laundering (AML) and financial crime prevention, combined with a proactive and value-driven approach to delivery. You will join a high-performing team responsible for driving strategic development initiatives to strengthen transaction monitoring capabilities, spearheading the AI-driven Transaction Monitoring programme. You’ll be at the forefront of the fight against financial crime, ensuring timely detection of suspicious activity while applying cutting-edge AI and machine learning in transaction monitoring (TM). This role helps in the detection of money laundering and sanctions violations, effectively using AI powered systems. You’ll support the AML control framework through strategic leadership of AI enabled TM initiatives.
The ideal candidate will be motivated, structured, self-driven with a passion for simplification and a strong commitment to capturing and communicating requirements clearly. You will have the ability to collaborate effectively across diverse teams with the opportunity to expand your own capabilities, contribute to a robust change execution structure, and put ideas into action.
Responsibilities
1. Influence and coordinate cross-functional teams to deliver AI/Machine Learning (ML) TM solutions aligning with business needs and regulatory requirements
2. Define and manage overall delivery roadmap, budgets, timelines, dependencies, milestones, and deliverables
3. Oversee end-to-end testing, validation, performance monitoring, and reliability of AI models in live TM environments while working closely with cross functional and governance teams
4. Liaise with senior stakeholders, ensuring transparent governance, audit readiness, and effective risk reporting while taking ownership across the full change lifecycle
5. Establish metrics, dashboards and KPIs to monitor model effectiveness, alert rates, false positive reduction and operational performance
6. Drive process improvements and change management, embedding continuous feedback loops from analysis and investigators to refine detection scenarios as well as downstream investigation workflows
7. Manage implementation of AI/ML enhancement into new products or regions, ensuring compliance, consistency and scalability
8. Maintain strong customer and business focus, ensuring all change initiatives deliver value to the organisation, reduce investigator burden and enhance end-to-end user experience