Data Analyst - Banking Analytics (Global Banking)

Role Summary

The role involves monitoring transactional and credit portfolios to identify fraud trends, delinquency risks, and exposure patterns. The analyst will support risk monitoring, investigation queues, portfolio tracking, and regulatory reporting using SQL and Python.

Key Responsibilities

Fraud Analytics

• Monitor transactional data to detect suspicious patterns and rule breaches
• Identify high-risk transactions (velocity checks, geo-mismatch, abnormal spend patterns)
• Analyze fraud trends by product, channel, and geography
• Support fraud investigation teams with structured data extracts
• Track chargebacks and fraud loss metrics
• Prepare daily/weekly fraud MIS dashboards

Credit Risk Analytics

• Monitor portfolio performance (DPD buckets: 30/60/90+)
• Identify delinquent accounts and high-risk segments
• Conduct vintage and cohort analysis
• Track exposure at default (EAD) and portfolio concentration
• Support credit policy impact analysis
• Prepare regulatory and management reports

Technical Skills Required

SQL (Mandatory)

• Joins (multiple tables: customer, transaction, loan, repayment)
• Aggregations & Grouping
• Window functions (ROW_NUMBER, SUM OVER)
• Data extraction and reconciliation

Python (Preferred)

• Data analysis using Pandas
• Data cleaning & validation
• Trend analysis and reporting automation
• Basic visualization (Matplotlib/Seaborn)

Tools

• Excel (Advanced Pivot, VLOOKUP/XLOOKUP)
• Power BI / Tableau (Preferred)

Experience

• 2-6 years in Fraud Analytics / Credit Risk Analytics / Banking Analytics
• Experience working with large transactional or loan datasets
• Exposure to global banking domain, retail banking portfolio