Data Analyst (Fraud Analytics)

Hiring: Data Analyst Fraud Analytics Location: Bangalore Experience: 4 - 7 Years Employment Type: Full-Time Job Summary We are seeking a Data Analyst Fraud Analytics with strong analytical capabilities and hands-on expertise in SQL and SAS to support fraud detection, fraud strategy optimization, and data-driven decision-making. The ideal candidate will have experience in the Banking, Financial Services, FinTech, or Payments domain, with a focus on identifying fraud patterns, analyzing large datasets, and improving fraud prevention strategies. If you're passionate about transforming data into actionable insights and contributing to fraud risk mitigation, we'd love to hear from you. Key Responsibilities Analyze large volumes of transactional and customer data to identify fraud trends, anomalies, and emerging risks. Perform data mining, data validation, and exploratory data analysis using SQL and SAS. Support the development, testing, and optimization of fraud detection rules and strategies. Generate actionable insights and recommendations to improve fraud prevention performance. Develop reports, dashboards, and presentations for business stakeholders. Perform quantitative and qualitative analysis to support fraud investigations. Collaborate with Fraud Risk, Business, Product, and Technology teams to improve fraud controls. Present analytical findings and recommendations to senior stakeholders. Participate in process improvement, automation, and organizational initiatives. Required Technical Skills Must Have Strong hands-on experience in SQL. Strong hands-on experience in SAS. Excellent data analysis, data mining, and pattern recognition skills. Strong analytical thinking and problem-solving abilities. Experience working with large datasets and transforming data into business insights. Good to Have Exposure to Machine Learning models for fraud analytics. Experience with Python and PySpark. Exposure to Big Data technologies (Spark, Hadoop, Hive, Databricks). Experience with Cloud platforms (AWS, Azure, or GCP). Hands-on experience with Shell Scripting. Advanced proficiency in MS Excel, VBA, and PowerPoint. Familiarity with Jira, Confluence, or similar workflow management tools. Domain Experience 47 years of experience in Data Analytics, preferably in Fraud Analytics, Fraud Risk, or Financial Crime Analytics. Experience in Banking, Financial Services, FinTech, Credit Cards, Payments, or Digital Banking. Good understanding of fraud detection methodologies, fraud strategy, and rule optimization. Experience analyzing customer behavior and transaction data to detect suspicious activities. Preferred Qualifications Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, Engineering, or a related quantitative field. Certifications in SQL, SAS, Data Analytics, or Fraud/Risk Management are an added advantage. Soft Skills Strong verbal and written communication skills. Ability to present insights to senior business stakeholders. Excellent stakeholder management and collaboration skills. Self-driven with strong attention to detail. Ability to work in a fast-paced, dynamic environment. A proactive problem solver and strong team player. .