4-8 years of total experience in Fraud Analytics or Credit Risk Analytics within the banking/financial services domain.
Proven track record in strategy creation, model development, rule writing, and data deepdives.
Exposure to fraud / credit risk management platforms, transaction monitoring systems, or credit decision engines preferred.
Technical Skills:
Strong SQL skills for querying and manipulating large datasets.
Proficiency in at least one of: Python, Spark, or SAS.
Hands-on experience with data visualization tools (Tableau, Power BI, etc.).
Key Responsibilities in the role:
Fraud & Risk Strategy Development
Design, implement, and optimize fraud detection and prevention strategies across banking products and channels.
Partner with operations, product, and technology teams to refine risk controls while balancing fraud losses, customer experience, and operational efficiency.
Collaborate with modeling teams to develop predictive and machine learning models for fraud detection.
Author, test, and deploy fraud detection rules and scoring logic.
Conduct deep-dive analyses on fraud trends, customer behavior, and transaction patterns to identify emerging threats.
Provide actionable recommendations based on analytical findings.
Develop dashboards and reports in Tableau or similar tools to track KPIs, monitor strategy performance, and communicate insights to stakeholders.
Ensure reporting accuracy, timeliness, and usability for business partners.
Partner with internal teams such as Technology, Data Science, Operations, Compliance, and Legal to deliver risk management initiatives.
Support risk reviews and prepare presentations for senior management and governance forums.