Internal Fraud Risk Management Data Analyst
U.S. Bank (company site, opens in a new tab)5–7 yrsChennaiOn-siteDirect career pageEst. 12–20 LPA
Posted 2d ago
Reach people at U.S. Bank
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at U.S. Bank you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own creation and execution of recurring risk reporting for management and stakeholders to monitor and analyze internal fraud risk trends.
- Lead advanced analytical projects across enterprise business lines involving complex data mining and statistical techniques to identify emerging fraud risks.
- Collaborate cross-functionally to identify new risk monitoring opportunities, provide actionable recommendations, and support risk management policies and controls.
Minimum Requirements
- Bachelor’s degree in Business, Finance, Computer Science, Business Analytics, Statistics, or quantitative field.
- 5 to 7 years of total experience in Analytics, preferably in financial services.
- Proficiency with advanced analytics tools and languages such as SAS, SQL, R, Python, Azure, Databricks, Synapse, Power BI, Tableau.
- Work Mode: Work From Office (WFO) in Chennai with shift timing 1:30 PM to 10:30 PM.
Ideal Candidate Profile
- Experienced analytics professional with strong data mining and advanced statistical analysis skills in financial services internal fraud risk context.
- Proven ability to collaborate cross-functionally and influence leadership with actionable insights to strengthen fraud risk management.
- Comfortable working in a regulated environment with complex data sources and multiple stakeholders, able to manage multiple tasks under minimal supervision.