IndusInd Bank - Head - Fraud Analytics - DSU

Fraud Analytics:

1. Develop Fraud Detection Models: Design and deploy predictive analytics models and machine learning algorithms to identify suspicious activities and patterns.

2. Enhance Systems: Continuously optimize fraud monitoring systems to improve detection accuracy and reduce false positives.

3. Data-Driven Insights: Analyze internal and external data sources to identify vulnerabilities and recommend proactive measures.

4. Performance Metrics: Define and track key performance indicators (KPIs) to measure the effectiveness of fraud detection strategies.

Market Intelligence:

1. Industry Monitoring: Stay updated on emerging fraud tactics, industry benchmarks, and regulatory changes.

2. Competitor Analysis: Analyze competitors' risk management strategies to identify opportunities for improvement.

3. Market Trends: Provide actionable insights from market intelligence to inform strategic decision-making.

Team Leadership:

1. Build and Lead: Recruit, develop, and mentor a high-performing team of data analysts, data scientists, and market intelligence specialists.

2. Stakeholder Collaboration: Work closely with risk management, compliance, IT, and other departments to ensure alignment and effective communication.

3. Training: Educate staff and leadership on emerging fraud trends and best practices.

Strategic Planning:

1. Policy Development: Establish and enforce fraud prevention policies, procedures, and standards.

2. Technology Partnerships: Evaluate and recommend advanced fraud detection technologies and vendors.

3. Reporting: Present findings and strategic recommendations to senior leadership and the board.