Sr. Analyst - Fraud Analytics

Proclink; Hyderabad; On-site; INR 600,000 - 900,000; Full time

Job summary

Keka Technologies Private Limited in Hyderabad is seeking a detail-oriented Fraud Analyst to join our Credit Card Risk team. You will monitor dashboards daily, investigate transaction and account data to identify fraud patterns, and contribute to fraud strategy development.

The role requires strong SQL and Advanced Excel skills, familiarity with fraud typologies, and ability to build reports. Python knowledge is a plus, and experience with BI tools like Tableau or Power BI will help you spot

Qualifications

• Strong SQL and Advanced Excel skills; data analysis focus.
• Experience in BFSI credit card fraud domain is preferred.

Responsibilities

• Monitor fraud dashboards daily and flag anomalies to stakeholders.
• Investigate flagged transactions to distinguish genuine fraud from false positives.
• Develop and test fraud detection rules and dashboards with risk teams.
• Collaborate with Risk, Operations, Compliance, and Product on trends and remediations.
• Build regular and adhoc reports to track fraud rates and loss trends.

Skills

SQL

Advanced Excel

Analytical reasoning

Education

Bachelor's or Master's in a quantitative field

Tools

Python

Tableau

Power BI

Looker

Job description

We are looking for a detail-oriented Fraud Analyst to join our Credit Card Risk team. The ideal candidate will monitor fraud dashboards daily, investigate transaction and account level data to identify fraud patterns, and support fraud strategy development to strengthen our detection and prevention capabilities within the BFSI credit card business.

Key Responsibilities:

• Monitor fraud/risk dashboards daily, proactively identify anomalies, spikes or unusual trends and flagging them to stakeholders at the earliest.
• Dep-dive into flagged anomalies to determine root cause - distinguishing genuine fraud trends, data/reporting issues, or one-off outliers.
• Investigate flagged accounts/transactions to distinguish genuine fraud from false positives, applying knowledge of common fraud typologies (e.g., account takeover, first party fraud, synthetic identity fraud, third party fraud etc.)
• Support the design, testing, and monitoring of fraud detection rules and strategies in partnership with seniors and risk stakeholders.
• Collaborate cross functionally with Risk, Operations, Compliance, and Product teams on fraud trends and remediations.
• Manage multiple investigations and monitoring tasks independently, prioritizing bases on risk and business impact.
• Analysis & Reporting-
• Analyze large volumes of transaction, account and customer data using SQL and Excel to identify unusual patterns and emerging fraud trends beyond what dashboards surface.
• Build and maintain regular and adhoc reports/dashboards to track fraud rates, loss trends, and rule/model performance.
• Document findings and investigation outcomes clearly and communicate anomalies and results promptly to stakeholders and escalating urgent issues without delay.

Required Skills & Experience:

• Prior experience in the fraud domain is a must, with a working understanding of BFSI credit card fraud typologies and lifecycle.
• Strong hands-on proficiency in SQL and Advanced Excel skills - mandatory.
• Basic understanding of Python for data analysis - good to have.
• Comfort working with dashboards/reporting tools and reading trend data to spot deviations quickly.
• Strong analytical and problem-solving skills, with high attention to detail.
• Excellent written and verbal communication skills, with the ability to explain findings clearly and promptly to stakeholders at different levels.
• Strong organizational skills and ability to manage daily monitoring alongside deeper investigations, independently and with minimal supervision.
• A quick learner with genuine enthusiasm for picking up new tools, domains, and analytical techniques.

Nice to Have:

• Familiarity with fraud monitoring tools or case management systems.
• Exposure to BI/reporting tools (Tableau, Power BI, Looker).

Education

• Bachelor's or Master's degree in a quantitative, business, engineering, or related field, with relevant practical experience.