Job Title

Roles and Responsibilities: Conceptualize and implement early warning triggers & strategies from fraud perspective in both assets and liabilities Develop analytics to identify suspected frauds in both credit and non-credit areas Monitor efficacy of the analytical / predictive models implemented and advise updations/modifications Monitor effectiveness of triggers and rules set for fraud identification and suggest refinements/additional rules to improve efficacy and accuracy Design fraud monitoring framework through data analytics and collaboration with first line of defense Analyze historical data on EWS, RFA and credit frauds across industry to develop and implement EWS triggers Interpret data and present actionable insights to key stakeholders Lead collaboration between stakeholders – RCU, product, analytics, IT, BSG & business to design/implement solutions w.r.t. Fraud identification and monitoring Monitor & maintain fraud analysis solutions / models to improve efficiency & effectiveness Collaborate with FinTechs / external vendors for exploring on new ideas / innovations and implement the same Presentation to relevant committees and senior management: Job Requirements: Candidate should have minimum 5-6 years’ experience in analytical/statistical modelling and data analytics in Fraud Risk with overall experience of at least 8 years. Prior experience in Analytics on Python/SAS is must. Candidate should have excellent logical thinking and solution building skills. Above average to advanced excel and presentation skills Good written and verbal communication skills Professional Qualifications - MSc Statistics/ MTech/BTech/MBA from Tier1 colleges or universities with CFE certification preferred