Develop and implement effective fraud authorization suppression strategies to mitigate false positives from fraud loss mitigation efforts for the branded credit card business while ensuring an appropriate balance between risk, operational cost, and customer experience. These may be simple strategies based on decision trees, to more complex analyses involving text mining, clustering , or other advanced techniques.
Analysis of customer transaction data to develop and improve transactional or authentication fraud prevention strategies for diverse card present and card not present scenarios.
Identify patterns to detect false positive trends by evaluating combinations or sequence of events from different data sources.
Managing a group of fraud analysts.
Support large scale projects including testing and deployment of new vendor scores or tools , as well as development of business cases for new technology
Develop and review MIS reports and communicate the fraud results to the business
Share best practices with partners
Partner in root cause analysis and decision quality / defect reviews to identify strategy opportunities to improve performance
Work within a business end to end agile framework where teams are cross - functional and empowered , activities are time boxed around specific outcomes , work is iterative and incremental and problems are solved in a modular and adaptive manner.
"Lump Sum Payment Provided "
Qualifications
Bachelors degree in Statistics , Economics , Finance , Mathematics , Computer Science , or a related quantitative field , is required . An advance degree is highly desirable
A minimum of 5 years of experience in applied analytics is required preferably in a risk management context
People leadership experience
Experience in the Banking / financial industry
Experience in the agile methodology of iterative project management
Experience in the fraud risk discipline in Credit cards or other consumer Banking products is desired
Experience in implementing strategies into real time fraud prevention system is desired
Strong analytical skills. Familiar with dataset environments
Experience in statistical and data analysis in a at least one of the following statistical software packages or languages : SAS(required), SQL , R or Python.
Experience with Unix desired
Excellent organizational skills
Excellent communication and presentation skills are required
Ability to be flexible and work in a rapidly changing environment
Must be extremely motivated with a desire to continuously learn and refine our processes