Lead Analyst - Portfolio Intelligence & Fraud Analytics

Key Responsibilities:

• Analyze customer, product, corridor, and market performance to identify emerging trends, risks, and growth opportunities.
• Develop and maintain portfolio monitoring dashboards and reporting frameworks and perform deep-dive analysis on customer segments, transaction behavior, payment flows, and portfolio concentration risks.
• Monitor key portfolio KPIs and KRIs, identifying significant changes and potential risk indicators.
• Conduct ad hoc analytical reviews to support business initiatives and strategic decision-making.
• Monitor fraud, chargeback, dispute, and payment abuse trends across products and geographies.
• Investigate fraud events to identify root causes, attack patterns, and control gaps.
• Support development and optimization of fraud rules, thresholds, and monitoring strategies.
• Assess fraud-control effectiveness and provide recommendations for improvement.
• Analyse customer friction metrics to balance fraud prevention objectives with customer experience.
• Support remediation activities for emerging fraud risks and elevated loss trends.
• Build and maintain dashboards using Looker, Power BI, Tableau, or similar analytics platforms.
• Develop and optimize SQL queries for fraud, portfolio, compliance, and operational reporting.
• Create recurring and ad hoc reports for Risk, Compliance, Product, and senior stakeholders.
• Develop data visualizations that communicate actionable insights effectively.
• Partner with Data Engineering teams to improve data quality, accessibility, and reporting capabilities.
• Monitor portfolio performance against internal risk thresholds and payment network requirements.
• Track fraud-to-sales ratios, chargeback ratios, dispute rates, approval rates, and transaction-risk indicators.
• Analyze emerging payment-risk trends and provide early warning indicators to stakeholders.
• Provide analytical support for fraud prevention, risk management, and portfolio optimization projects.

Requirements:

• 4-7 years of experience in Fraud Analytics, Portfolio Analytics, Payments Risk, Financial Crime Analytics, Business Intelligence, or Data Analytics.
• Strong analytical and problem-solving skills.
• Experience working with large datasets and identifying actionable insights.
• Advanced SQL skills and experience performing complex data analysis.
• Proficiency in Python for data analysis and automation.
• Experience building dashboards and visualizations using Looker, Power BI, Tableau, or similar tools.
• Understanding of payment ecosystems, fraud typologies, chargebacks, and transaction-risk indicators.
• Strong communication and stakeholder-management skills.
• Ability to present analytical findings to both technical and non-technical audiences.
• Preferred Qualifications
• Experience within fintech, payments, banking, card issuing, or acquiring environments.
• Knowledge of Visa and Mastercard fraud monitoring programs.
• Familiarity with AML, transaction monitoring, or financial crime controls.
• Experience with machine learning models or predictive analytics.
• Exposure to cloud data platforms such as Snowflake, Redshift, Databricks, or BigQuery.