The Fraud Data Analytics Officer must be able to analyze transaction patterns and fraud trends to develop effective and dynamic anti-fraud strategies based on fraud types and customer segments. Will also manage rule life cycle including performance analysis and reporting.
- Examine past and current fraud trends, running data reports and analyzing results in order to implement effective fraud mitigation strategies.
- Responsible for applying validation processes to fraud rules.
- Perform trend analytics using valid transaction and fraud data to identify patterns and make recommendation for improved controls.
- Responsible for creation statistical reporting based upon a large volume of data and identify fraud trends based on specific transaction attributes and historical transaction data.
- Identify, analyze and resolve complex issues with limited supervision using various fraud tools, applications and systems.
- Develop, implement and improve comprehensive fraud detection strategies and authorization decision rules that balance fraud loss reduction, costs and customer experience.
- Designs experiments, test hypotheses, and build models.
- Conducts advanced data analysis and highly complex designs algorithm.
- Applies advanced statistical and predictive modeling techniques to build, maintain, and improve fraud reduction controls.
- Leads discovery processes with stakeholders to identify the business requirements and the expected outcome always aligned with the reduction of fraud.
- Models and frames scenarios that are meaningful in the fraud reduction arena.
- Provides on-going tracking and monitoring of performance of the rules behaviors and fraud models.
- Leads the design and deployment of enhancements and fixes to the fraud model as needed.
- Prepare monthly, quarterly and annual reports as it relates to changes made to strategies, their impact & performance indicator results.
- Ongoing personal development and understanding of evolving analytical tools and technologies available that may be implemented to enhance work performance.
- Maintain open and ongoing collaboration and communication with front-line team; obtaining feedback and incorporating their day to day experiences into proposed solutions to mitigate negative impact to customers.
- Work with product owners and business functional areas in developing fraud prevention initiatives for plastics that balance risk and customer service.
- To identify, evaluate, monitor and make any recommendation deemed necessary to their respective Risk Management Committee in order to assess, reduce, eliminate or control any current or prospective risks to earnings or capital arising from violations of, or nonconformance with, laws, rules regulations, prescribed practices, internal policies and procedures or ethical standards.
- Maintain knowledge of bank fraud trends and card association rule and regulations.
Minimum Work Experience Requirements:
Minimum five years experience in the financial industry working in the fraud prevention arena. Proven analytic experience and capability preferably in risk related products and risk strategies. Knowledge of MasterCard & Visa Risk Management Policies & Procedures preferable.
Minimum Education and/or Certifications Requirements:
Bachelor's Degree in Business/MIS/Quantitative areas such as Mathematics or Statistics, or equivalent combination of education, related course work and hands-on experience with risk analytics systems.
Technical and/or Other Essential Knowledge:
Proficiency in SAS and SQL programming, Excel and Access. Predictive modeling experience. Strong background in statistics and mathematical optimization, forecasting and simulation. Strong analytical and problem solving skills. Ability to analyze fraud trends and recommend effective mitigation strategies. In addition to advanced analytic skills, this role is also proficient at integrating and preparing large, varied datasets, architecting specialized database and computing environments, and communicating results. Make sound decisions based on precedent, data analytics, judgement and experience. Working knowledge of data analysis software and tools. Knowledge of relational databases.