Job Title: Fraud Control Unit (FCU) Analyst – AI & Emerging Fraud Risk
Role Overview
We are looking for a proactive and analytical FCU Analyst to identify, investigate, and mitigate fraud risks arising from AI-driven and technology-enabled activities. The role involves monitoring suspicious patterns, leveraging data analytics, and strengthening fraud prevention frameworks in a rapidly evolving digital ecosystem.
Key Responsibilities:-
- Monitor and detect fraud patterns, including AI-generated frauds such as synthetic identities, deepfake-based verification bypass, and automated bot attacks.
- Investigate suspicious transactions, onboarding anomalies, and behavioral deviations using data-driven approaches.
- Collaborate with data science and engineering teams to enhance fraud detection models and rule engines.
- Analyze large datasets using SQL/Python to identify emerging fraud trends and vulnerabilities.
- Conduct root cause analysis of fraud incidents and recommend preventive controls
- Develop and refine fraud risk policies, especially for digital lending, onboarding, and partner integrations.
- Work closely with product, risk, and compliance teams to implement real-time fraud mitigation strategies.
- Stay updated on emerging AI fraud techniques and proactively design countermeasures.
Key Skills & Requirements:-
- 2–4 years of experience in Fraud Risk, FCU, Risk Analytics, or similar roles.
- Strong understanding of fraud typologies, especially in digital platforms and fintech ecosystems.
- Hands-on experience with SQL and/or Python for data analysis.
- Familiarity with AI/ML concepts and how they are used in fraud detection (or exploited for fraud.
- Experience with transaction monitoring systems, rule engines, or risk scoring models.
- Strong analytical thinking and problem-solving skills.
- Excellent communication skills for cross-functional collaboration.
Preferred Qualifications:-
- Experience in digital lending, banking, fintech, or payments domain.
- Exposure to AI-based fraud scenarios (deepfakes, synthetic IDs, bot fraud, etc.
- Knowledge of regulatory and compliance requirements related to fraud risk.
- Prior experience working with machine learning-based fraud models or scorecards.
What Success Looks Like
- Reduction in non-starter rates of portfolio and improved detection rates.
- Early identification of emerging AI-driven fraud patterns.
- Strong collaboration with tech teams to enhance fraud prevention systems.
