VP Fraud Risk Business Analytics Lead

VP Fraud Risk Business Analytics Lead

As part of the Financial Crimes and Fraud Prevention organization, this role manages and executes the first party fraud analytics and strategies supporting Citi's North American and global credit card businesses. This includes leveraging data to identify fraud trends then designing and implementing strategies to prevent and mitigate fraud attacks across the full fraud lifecycle including application and synthetic ID fraud, and sophisticated new attack schemes.

This role partners closely with Credit Risk Policy, Fraud Policy, Operations, Technology and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.

The First Party Fraud Business Analytics Lead is a seasoned professional role who applies in-depth disciplinary knowledge, contributing to the development of new techniques and the improvement of processes for the area or function. Requires understanding of how credit cards work and integrate and contributes to the objectives of the function and overall business. Requires good analytical skills to filter, prioritize and validate potentially complex and dynamic material from multiple sources. Excellent communication and diplomacy skills are required. Regularly assumes informal/formal leadership role within teams.

Responsibilities:

• Lead implementation of fraud risk strategies for consumer and commercial card businesses with decision engine systems in accordance with defined change control procedures and controls.
• Own and manage fraud rules, scores, and detection strategies, risk appetite execution, POS interdiction strategies and defect analysis. Lead gathering of business requirements, testing, and implementation of projects impacting fraud decision systems as well as system incidents.
• Leverage analytics to identify enhancement opportunities and more granular insights that can be acted upon, while ensuring adherence to Fraud Policy.
• Build effective relationships within and outside the Fraud organization to help ensure successful and timely execution of key portfolio priorities.
• Collaborate with cross-functional teams to provide strategy recommendations based on data and trend analysis and implement mitigation strategies.
• Continuously improve processes and strategies by exploring and evaluating new data sources, tools, and capabilities
• Leverage customer data to build risk segmentation/ mitigation strategies and complete complex analyses to identify strategy and procedure gaps, manage implementation process across several systems to affect change.
• Lead key analytical projects within the card first party fraud analytics team and support the Card lines of business by utilizing advanced predictive analytical and statistical techniques.
• Translate data into consumer or customer behavioral insights to drive targeting and segmentation strategies, and communicate clearly and effectively to business partners and senior leaders all findings
• Work closely with internal and external business partners in building, implementing, tracking, and improving fraud capture decision strategies.
• Refine framework for change control risk assessment including evaluation of control gaps within existing processes.
• Supports Governance process, evaluating historical performance and emerging changes in the environment.
• Prioritize and provide a clear line of sight to the most critical work to partners and team members.
• Mentor and coach junior team members.

Recommended Qualifications:

• Bachelor's Degree required/Master's preferred in statistics, mathematics, physics, economics, or other (data) analytical or quantitative discipline.
• 7+ experience required
• 6+ years of experience in analytics, modeling, or relevant area.
• Extensive experience working with: Big Data environment with hands on coding within various traditional (SAS, SQL, etc.) and/or open source (i.e. Python, Impala, Hive, etc.) tools.
• Traditional and advanced machine learning techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.
• Data visualization tools, such as Tableau
• Excellent quantitative and analytic skills; ability to derive patterns, trends and insights, and perform risk/reward trade-off analysis.
• Experience analyzing large datasets; applying mathematical, statistical and quantitative/data analysis techniques to perform complex analyses and data mining.
• Ability to build effective presentations to communicate analytical findings to a wide array of audiences.
• Effective cross-functional project, resource, and stakeholder engagement and management, with ability to effectively drive collaboration across teams.
• Ability to make decisions independently with minimal guidance from management.
• Experience with a prior focus in financial services analytics preferred