Head - Fraud Data Analytics & Market Intelligence - Retail Assets

Key Responsibilities:

Fraud Analytics & Modeling:

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, fraud investigation team, 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.

Desired Behavioral / Functional Traits

- Deep expertise and experience in developing predictive models for proactive fraud management, prior experience of building anomaly detection models and fraud strategies

- Strong coding skills in Python, R, SQL, SAS and Machine learning algorithms and model development

- Achievements in delivering substantial business value through the development and implementation of advanced analytical tools and insights

- A passion for data, with a preference for more data and comfortable with model deployment process within compliant environment

- Senior analytics leader with experience in leading analytics teams and establishing teams from the ground up

- Strong domain knowledge across various variety of retail products, preferably, Credit Card, Personal Loan, Microfinance, Vehicle loan, Home Loan, Loan against property etc.

- Art of presenting data analytics as storytelling and command on presentation skill

- Skills in networking and influencing C-Level executives, and the ability to work collaboratively