To strengthen the organisation's defence against fraud and scams through data-driven threat intelligence and analytics. This role is pivotal in shaping an intelligence-led fraud management strategy, providing actionable insights to anticipate, prevent, and detect emerging threats, and ensuring the bank maintains a robust defense against increasingly sophisticated financial crimes.
Job Responsibilities:
- Drive the transition to an intelligence-led, data-driven approach to fraud prevention, detection, and response
- Identification, analysis, and disruption of emerging mule networks, scam typologies and new fraud vectors
- Design and develop models to detect fraud in collaboration with data science and analytics team
- Translate intelligence and typology insights into actionable risk indicators and detection logic
- Collaborate with law enforcement, industry groups and regulators to exchange intelligence and strengthen the bank's threat posture
- Review and optimise fraud rule policies/parameters across systems for CASA and Cards to effectively identify potential fraud or scam activities.
- Conduct regular rule performance reviews, ensuring thresholds and parameters remain calibrated to evolving risk typologies.
- Collaborate with relevant teams and to ensure rules alignment with industry standard and regulatory expectations
- Compile accurate, timely and insightful fraud reports to regulators and senior management
Job Requirements:
- Degree in Cybersecurity/Fraud-related/Compliance, Data Science/Analytics, Banking/Finance, Business IT or other relevant degree
- At least 5 years of experience in fraud risk management, intelligence or analytics, preferably in banking or financial services
- Experience working with regulatory bodies (e.g. MAS), law enforcement agencies and cross-industry fraud intelligence networks
- Strategic thinker with strong analytical and investigative mindset
- Deep understanding of fraud ecosystems, scam networks, and digital threats
- Excellent stakeholder management and communication skills
- Proficiency in data analytics tools (e.g., Python, SQL, Power BI, SAS) and knowledge of AI/ML model governance
*Only shortlisted candidate will be notified.
