Business Leader, Fraud Investigation

Job Overview:
Responsible for analysis of data to support ad-hoc fraud and payment system integrity reporting requests with a secondary role as backup support for the Fraud Investigation team in the identification and investigation of Account Data Compromise (ADC) events.
Must be able to work with little supervision and be able to supplement training with self guided ongoing research and an 'own the data' mentality using sound statistical approaches and an intuitive ability for knowledge and data discovery. Demonstrated hands-on SAS and Unix skills with very large datasets are a must have as well as the ability to manage and triage requests for analyses.

Supporting role includes working with current staff in the identification and validation potential points of compromise and the production of excess risk or fraud statistics consisting of valid transactions on accounts with subsequent fraud in order to assess the potential risk to the reputation of MasterCard and its membership with extreme urgency and responsiveness. Analysis is accomplished by the use of applied programming and mathematical skills including capabilities in complex statistical methods and algorithms utilizing MasterCard Clearing, Authorization (Banknet and EPSNet) and SAFE data.

Demonstrated skill in leveraging the MasterCard Data Warehouse, MasterCard Debit Switch (MDS) using Unix/Aix command line, Korn shell, batch SAS as well as Postgresql. Identify and evaluate additional authorization, clearing and settlement related systems for inclusion in analysis. Responsible for articulating thought leading and global perspectives on the fraud character and pattern trends of account data compromise events as change occurs throughout the payments business, the environment, the economy, and competitive dynamics, and ensuring those perspectives are supported with research, data, expert opinion, and investigations.

Major Accountabilities:
1. Produce complex adhoc fraud reporting utilizing Unix based SAS and SQL.
2. Data cleansing algorithms as needed for normalization and aggregation of reported data.
3. Analyze and interpret fraud data to determine if fraud patterns and character are consistent with an ADC event.
4. Partner with the ADC investigation team on ADC events to determine fraud level and risk patterns as the forensic investigation continues to unfold with new case facts and theories.
5. Analyze and evaluate account data recovered in MasterCard investigations, forensic investigation or law enforcement investigations to determine if fraud patterns and character are consistent with an ADC event.
6. Evaluate issuer provided Common Point of compromise or other commonalities associated to the transaction to validate an entity as a potential point of compromise.
7. Identify common points of compromise and prepare potentially at risk accounts based on statistical analysis thereby allowing issuers to more effectively manage fraud risk.
8. Evaluate competing theories of ADC as necessary. For example, if multiple entities are suspected of having been compromised, study the comparative risk of accounts transacted by all. Determine if one alone is associated with significantly elevated fraud risk.
9. Prepare, maintain and archive data needed for analysis. Ensure that data is available for support of litigation and audit in a secure manner. Determine appropriate amounts of sample data based on statistical analysis. Manage retention and PCI compliance.
10. Develop repetitive/predictive models that can be used by the team for initial analysis.
11. Keep abreast of current industry standards and MasterCard data elements to fully utilize available data and techniques to determine potential points of compromise. Recommend innovative solutions and processes to support new trends in the criminal vector.
12. Perform routine analyses on sets of accounts and transactions related to counterfeit and card not present fraud. Produce fraud characteristics and periodic fraud timing tables along with conclusion of findings, leading to potential next steps in the investigation.
13. Coordinate and manage modeling projects with cross-functional teams including MasterCard Technologies Risk Systems, Data Warehouse and Global Network Products groups.
14. In coordination with the CS&RS team, interface with customers, both internal and external, regarding suspected account data compromise events, fraud findings, exposure of card data, establishment of at risk timeframe and developing a compelling case to pursue forensic investigations.
15. Prepare monthly management statistics, correspondence, memoranda, presentations and other assigned documentation relating to an ADC event.
16. Build and maintain a network of fraud/security contacts within the payment card industry and the MasterCard issuing community.
17. Provide conclusions and recommended next steps on the results of analysis to the ADC investigation team.

Education (preferred):

• Bachelor's Degree in Mathematics, Statistics or Computer Science. An advanced degree is desirable.

• Unix/Aix command line shell
• Netezza Postgresql or Oracle SQL*Plus
• Mainframe experience a plus
• Unix/SAS including SAS Macros, SAS ODS, and SQLConnect
• Statistical background
• Experience in building predictive models and statistical modeling
• Experience in security, fraud and/or account data compromise issues
• Knowledge of MasterCard payment systems preferred
• Advanced knowledge of leadership principles, theories and concepts
• Strong analytical, investigative, and detail-oriented skills
• Interpersonal skills and relationship building skills required, including strong presentation development and delivery experience to senior management, both internally and externally
• Requires a high level of tact and integrity due to the frequency of communicating with high-level internal and external contacts as well as regular exposure to confidential data
• This position requires a Bachelor's degree (Master's preferred) in mathematics, statistics or Computer Science and at least 5 to 10 years of progressively complex data analytics and fraud risk management
• Candidate should be able to integrate knowledge across multiple disciplines (i.e., data analysis, operations, fraud & risk, etc) and function in an unstructured environment with minimal day-to-day supervision.
• Clearly articulate, verbally and written, the results of statistical analysis.

• This position can be located in Purchase, New York or Saint Louis, Missouri