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We are seeking an individual who can own the fraud analytics charter end-to-end, from defining frameworks and identifying key data sources to operationalising insights into measurable fraud prevention strategies. This person will not only lead the analytics but also shape the fraud vision, working closely with data science, product, and business leadership teams.
Responsibilities:
Define and execute the fraud analytics strategy for the organisation, covering detection, prevention, and intelligence.
Design the fraud framework integrating AI/ML, rules-based engines, and behavioural analytics.
Bring a cross-industry perspective and apply best practices from payments, mobility, fintech, or e-commerce into the insurance context.
Identify and integrate data sources (internal and external) to enhance fraud models and detection accuracy.
Develop measurable fraud detection KPIs and continuously optimise performance through data-driven insights.
Collaborate with product, engineering, and client teams to embed fraud intelligence across decision workflows.
Build and mentor a high-performing team of analysts and data scientists focused on fraud risk and analytics.
Stay ahead of the evolving fraud and threat landscape, ensuring frameworks adapt to new risk vectors.
Requirements:
7-12 years of experience in fraud analytics, risk strategy, or data science, with at least 3 years in a strategic or leadership capacity.
Proven experience in building or leading fraud/risk analytics functions in industries such as insurance, fintech, payments, or e-commerce.
Strong understanding of fraud typologies, anomaly detection, and modern fraud prevention technologies.
Hands-on proficiency in SQL, Python, and data visualisation tools like Power BI, Tableau, or Looker.
Experience operationalising analytics into real-world fraud detection or prevention initiatives.
Exceptional communication and stakeholder management skills and the ability to partner across functions and influence at senior levels.
Preferred Background:
Experience in insurance analytics (claims, underwriting, or policy lifecycle) or risk intelligence in adjacent domains.
Exposure to graph/network analytics or AI/ML-based fraud detection systems.
Previous experience in a startup or high-growth tech environment, building teams or functions from scratch.
Experience
8-12 yrs
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