Retail Bank Mule and First Party Fraud Fraud Risk Officer Retail Bank Mule and First Party Fraud Fraud Risk  …

in Wilmington, DE, United States
Permanent, Full time
Be the first to apply
in Wilmington, DE, United States
Permanent, Full time
Be the first to apply
Retail Bank Mule and First Party Fraud Fraud Risk Officer
The Retail Bank Mule and First Party Fraud Fraud Risk Officer is an individual contributor role focused on the development and deployment of fraud strategies and projects with an objective to reduce customer pain points and enhance fraud detection.
This role will research known and unknown pain points, research root cause, and develop rules, projects, or tools to reduce Mule activity within the Retail Bank line of business as well as focus reducing early month on book Deposit Risk. Report creation and monitoring will be required periodically, automation of existing or future reporting is a required skill set. This individual will need strong Oracle and SQL skills to quickly get up to speed on existing task or projects and should be prepared with existing or willing to gain knowledge of data science methodology as the organization begins from our current rules based approach to a more advanced analytics mindset. Works closely with internal fraud partners, business partners and external vendors to ensure optimal strategic use of fraud tools. Works with external vendors on various aspects of fraud prevention including fraud model development, performance monitoring and strategy implementation. Works directly with teams of analysts within the United States and other offshore locations. Requires a comprehensive understanding of multiple areas within Retail Bank and how they interact in order to achieve the objectives of the function.
Preferred competencies:
  • Experience in unsupervised learning methods, network link analysis, and anomaly detection.
  • Operational experience or previous analytical interaction leading to volume reduction in alerts.
  • Hands on experience with Python; or Machine Learning techniques.

  • Individual Contributor in support of Retail Banking Mule and First Party Fraud prevention function. Further, this role also requires execution of strategic framework in establishing Mule and First Party roadmap for the Retail Banking portfolio.
  • Responsible for discovering threat landscape and customer insights in identifying opportunities through newest data and analytical methods (e.g. statistical, algorithmic, mining and visualization techniques, machine learning among others).
  • Act as a creative thinker to the organization and propose new ways to look at problems by using data and available information, presenting back their findings to the business by sharing their assumptions and validation work in a pragmatic / simple ways that can be easily understood by their business non-analytics counterparts.
  • Responsible for automating work with predictive and prescriptive analytics.
  • At times, lead and develop an internal capability to distill information and results from various data science models into something that is simple and pragmatic and that everyone can understand (e.g. storytelling to key stakeholders based on analysis and experiments).
  • Provides subject matter expertise including mathematical risk modeling on developing the Independent Fraud Risk Team's Fraud Risk Assessment application (proprietary software).
  • Builds, tests, analyzes data, and models to enhance user experience, customer service, and operational expense reduction.
  • Work closely with clients from the organization to turn data into critical information and knowledge that can be used to solve key use cases
  • Build effective working relationships within own department and across department, functional, and geographic reporting lines to execute against key portfolio priorities.
  • Provide other analytical support in managing Retail Bank fraud prevention function

  • 6-10 years Project Management experience and exposure to Financial Services industry
  • Experience working in Big Data environment with hands on coding experience within various traditional (SAS, SQL, etc.) and open source (i.e. Python, Impala, Hive, etc.) tools.
  • Proficiency in various quantitative, optimization and predictive analytics techniques using various statistical techniques
  • Understanding and hands on working experience of traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.
  • Hands on experience with data visualization tools, such as Tableau, Excel, etc.
  • Preference given for a person with strong understanding of various Fraud systems such as SAS Raptor, Actimize IFM/IFM-X, FDR DefenseEdge, PinDrop, etc., spanning across authentication, detection and resolution domains
  • Excellent verbal and written communication skills required in order to communicate effectively, internally and externally, often at a senior level.
  • Require Bachelors' degree in Mathematics, Computer Science, Analytics, Economics, or Statistics. H igher-level education such as a Master's degree, PhD, etc. in a quantitative discipline, preferred.
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.

Job Family Group:
Risk Management
Job Family:
Fraud Risk
Time Type:
Full time
Citi is an equal opportunity and affirmative action employer.
Qualified applicants will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
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