Senior Manager- Analytics & AI Driven Strategy

Job Purpose
The Senior Manager – Analytics & AI Driven Strategy (FRMU) is responsible for leading advanced fraud analytics, AI driven insights, and automation initiatives to enhance proactive fraud detection across Issuing and Acquiring businesses. The role serves as the analytical backbone of FRMU by conducting deep dive analyses, optimizing fraud rules, improving detection models, and driving adoption of AI/Gen AI capabilities to strengthen the bank’s fraud risk posture.

The position further oversees MIS governance, regulatory reporting, dashboard development, and data stewardship to ensure timely, accurate, and secure information delivery. The incumbent is expected to lead automation, streamline processes, and collaborate with cross functional teams to provide strategic, data driven intelligence for decision making
 

Key Result Areas

1. Fraud Analytics, Rule Optimization & AI Driven Strategy
   •    Lead advanced analytics using SQL, SAS, Python, and AI/Gen AI techniques to detect emerging fraud patterns.
   •    Conduct in depth evaluations of VRM/FraudGuard rules; recommend enhancements to improve detection accuracy and reduce false positives.
   •    Develop analytical models, scenario analyses, and predictive approaches to strengthen fraud surveillance.
   •    Champion the integration of AI/Gen AI tools in fraud detection workflows.
2. Fraud Detection Performance Management
   •    Oversee productivity, SLA adherence, and performance analytics of fraud detection teams across Issuing and Acquiring.
   •    Conduct root cause and gap analysis to highlight inefficiencies and recommend actionable improvements.
   •    Develop performance dashboards and scorecards using Power BI and Excel.
3. Advanced MIS, Dashboards & Governance
   •    Ensure timely, accurate, and automated MIS for senior leadership, committees, regulators, and other stakeholders. 
   •    Deliver comprehensive, data driven presentations with clear and structured analytical insights. 
   •    Maintain high quality governance standards in data reporting and visualization.
4. Automation, Digitization & Data Engineering
   •    Lead automation of manual data processes through SQL scripts, macros, workflows, and BI tools.
   •    Maintain secure, scalable fraud databases and ensure data governance and integrity.
   •    Introduce AI/Gen AI automation capabilities to enhance efficiency, accuracy, and processing speed.
5. Stakeholder Management & Cross Functional Collaboration
   •    Collaborate with IT, Business, Product, Compliance, and Operations for fraud-related initiatives and system enhancements.
   •    Provide analytical support, project insights, and business cases to senior management.
   •    Support strategic planning, forecasting, and committee reporting through strong analytical and presentation capabilities.

Knowledge, Skills and Experience

• Bachelor’s/Master’s in Finance, Data Analytics, Computer Science, Information Systems, Business, or related fields. 
• Certifications in Data Analytics, SQL/SAS/Python, BI Tools, or Fraud Risk (preferred). 
• CFE certification is preferable.
• 7–10 years of progressive experience in Fraud Risk Management, Advanced Analytics, MIS, Data Engineering, or Risk Strategy.
• Demonstrated experience leading analytics, automation, or MIS initiatives in a financial institution, payments business, or risk function.
• Prior experience in rule analytics, fraud systems optimization, or advanced data modelling is highly desirable.
• Advanced expertise in SQL, SAS, Python, Power BI, Excel, and data visualization tools.
• In‑depth knowledge of fraud detection platforms (VRM, FraudGuard, or similar rule engines) with capability to analyze, tune, and optimize rules.
• Hands‑on experience in process automation, data pipelines, workflow digitization, ETL concepts, and data warehousing environments.
• Strong command over data governance, data quality control, and secure database management.
• Ability to translate analytical output into strategic recommendations for senior management.
• Strong verbal and written communication skills with the ability to present technical findings in a professional, concise manner. 
• Excellent stakeholder management; ability to influence senior and cross‑functional stakeholders. 
• High attention to detail and strong analytical thinking. 
   

Company