Lead Fraud Control Unit

Responsibilities for the job:

• Lead end-to-end Fraud Analytics strategy across all digital lending products, covering onboarding, underwriting, disbursement, and repayment lifecycle
• Own fraud risk performance for the portfolio by monitoring key metrics such as FPD, EPD, fraud rates, and loss trends; ensure fraud losses remain within defined thresholds
• Design, develop, and continuously enhance fraud detection strategies leveraging rules engines, scorecards, statistical models, and advanced analytics techniques
• Lead the development and deployment of next-generation fraud detection capabilities using Generative AI, including LLM-powered workflows, Retrieval-Augmented Generation (RAG), and autonomous AI agents
• Conceptualize and implement agentic workflows to automate fraud investigation processes, anomaly detection, and signal generation at scale
• Drive innovation through use of synthetic data generation and simulation frameworks to identify emerging fraud typologies and stress-test fraud controls
• Define and implement fraud risk policies, underwriting rules, and approval frameworks to optimize customer experience while minimizing fraud risk
• Collaborate with business, partnerships, and external stakeholders to strengthen sourcing quality, improve underwriting frameworks, and enhance fraud prevention mechanisms
• Lead partnerships with external data providers (bureau, alternate data, device intelligence, telecom, etc.) and evaluate new data sources through pilots and POCs
• Drive deep-dive analytics on fraud incidents, emerging patterns, and channel-level risks; translate findings into actionable strategies, features, and rule enhancements
• Build and oversee scalable fraud data infrastructure, including feature marts and monitoring systems, in collaboration with Data Engineering, Tech, and DWH teams
• Define fraud KPIs, dashboards, and reporting frameworks for senior leadership, ensuring timely and actionable insights
• Lead cross-functional collaboration with FCU, Risk, Credit, Collections, Compliance, and Tech teams to drive fraud prevention initiatives and improve portfolio quality
• Present fraud insights, strategic recommendations, and performance updates to senior leadership and stakeholders
• Ensure compliance with regulatory guidelines, audit requirements, and internal risk governance standards
• Lead audit engagements, policy reviews, and hindsight analyses to continuously strengthen fraud frameworks
• Mentor and guide junior analytics team members, fostering analytical excellence and innovation within the team

Education:

Bachelors degree in engineering, Statistics, Mathematics, Economics, Computer Science, or related quantitative field.

(MBA / PGDM / Masters in Analytics, Data Science, or AI is a plus)

Work Experience:

6–10 years of experience in fraud analytics, risk analytics, or data science within BFSI / NBFC /

FinTech. Strong experience in unsecured digital lending products such as Personal Loans, Consumer Loans, BNPL, Credit Lines, or Cross-sell portfolios. Experience in leading fraud strategy, policy design, and data-driven risk decisioning is essential

Primary Skill:

Deep expertise in fraud analytics and strategy across digital lending portfolios, with proven ability to design and deploy data-driven fraud prevention frameworks. Strong understanding of fraud typologies, risk policies, and portfolio performance management

Technical Skills:

• Advanced proficiency in SQL and Python/PySpark for large-scale data analysis and automation
• Strong experience in machine learning techniques (supervised and unsupervised) for fraud detection and risk modelling.
• Hands-on experience with Generative AI technologies including LLMs, prompt engineering, vector databases, and agentic frameworks (LangChain, LlamaIndex, AutoGen, etc.).
• Experience working with big data platforms (e.g., Databricks) and building scalable data pipelines.
• Strong understanding of rule engines, decision systems, and automated underwriting frameworks
• Experience in data visualization tools such as Power BI for fraud monitoring dashboards.