Fraud and Digital Acquisition Business Intelligence Analyst

WEX•Maine, NY

16d

About The Position

How you'll make an impact Data Analysis & Insight Generation: Connect business problems with data to produce insightful, data-driven analysis. Leverage analytics and risk models to drive operational improvements in areas such as fraud detection and customer onboarding. Analyze portfolio behavior to identify anomalies, emerging trends, drop-off points, and areas for improvement across customer funnels. Conduct root cause analysis on performance issues and fraud events to uncover underlying causes and continuously improve coverage and efficiency. Identify proxy variables or creative data substitutes when ideal data is not available. Reporting & Dashboard Development: Design and maintain dashboards and analytical tools that track overall business performance, including DCA metrics (e.g., approval rates, automation rates, conversion trends) and fraud performance (e.g., fraud loss performance, rule efficiency, alert-to-case conversion rates) over time. Monitor system KPIs, evaluate performance trends, and recommend improvements. Deliver insights to the organization that optimize revenue and risk. Strategy & System Optimization Support: Support the implementation of data-driven strategies that transform processes to enable efficiency and scale. Contribute to optimizing fraud detection systems to capture fraud and minimize customer disruptions. Collaborate with Data Scientists to support the building and operationalization of machine learning models to solve risk problems and enhance fraud detection. Assist in incorporating external threat intelligence and business context into strategic recommendations. Cross-Functional Collaboration & Communication: Partner across internal stakeholders—including Fraud, Risk, Product, Technology, Sales, Marketing, Legal, and Compliance—to align analytics with business goals and support cross-functional initiatives. Communicate complex analytical concepts and findings to non-technical stakeholders effectively. Build and maintain strong relationships with internal stakeholders. Early Success Expectations (First 100 Days): Improve capture rate of fraud or decrease false positive alerts by 3%. Establish working relationships with key stakeholders across various departments. Gain a comprehensive understanding of WEX’s products, customers, and key business systems, including the Digital Credit Application (DCA) and fraud prevention architecture.

Requirements

Bachelor’s degree in a quantitative field (e.g., Data Science, Economics, Computer Science, Statistics, Industrial Engineering, Data Analytics, Risk Management).
3-5 years of experience in business intelligence, fraud analytics, financial risk modeling, or digital operations, preferably in fintech, commercial payments, or customer onboarding environments.
Strong data skills: Proficient in SQL, Python, and data visualization tools (e.g., Tableau, Power BI, Snowflake, Data Lake).
Proven ability to contribute to building and managing fraud rules and detection policies.
Analytical thinker with excellent problem-solving abilities and a strong grasp of statistical and modeling techniques.
Highly collaborative and engaging, willing to drive execution.
Ability to excel at detailed execution and deliver results on-time in a fast-paced, complex environment.
Strong understanding of fraud typologies and detection principles (e.g., Account Takeover, synthetic identity, application fraud, transaction fraud).
Good project management, cross-functional communication, and analytical problem-solving skills.

Nice To Haves

Experience with Digital Credit Application platforms or similar customer onboarding analytics is preferred.
Experience with Artificial Intelligence and Machine Learning Solutions is desirable.
Familiarity with fraud decision platforms, third-party vendor tools, and fraud scoring techniques is a plus.

Responsibilities

Connect business problems with data to produce insightful, data-driven analysis.
Leverage analytics and risk models to drive operational improvements in areas such as fraud detection and customer onboarding.
Analyze portfolio behavior to identify anomalies, emerging trends, drop-off points, and areas for improvement across customer funnels.
Conduct root cause analysis on performance issues and fraud events to uncover underlying causes and continuously improve coverage and efficiency.
Identify proxy variables or creative data substitutes when ideal data is not available.
Design and maintain dashboards and analytical tools that track overall business performance, including DCA metrics (e.g., approval rates, automation rates, conversion trends) and fraud performance (e.g., fraud loss performance, rule efficiency, alert-to-case conversion rates) over time.
Monitor system KPIs, evaluate performance trends, and recommend improvements.
Deliver insights to the organization that optimize revenue and risk.
Support the implementation of data-driven strategies that transform processes to enable efficiency and scale.
Contribute to optimizing fraud detection systems to capture fraud and minimize customer disruptions.
Collaborate with Data Scientists to support the building and operationalization of machine learning models to solve risk problems and enhance fraud detection.
Assist in incorporating external threat intelligence and business context into strategic recommendations.
Partner across internal stakeholders—including Fraud, Risk, Product, Technology, Sales, Marketing, Legal, and Compliance—to align analytics with business goals and support cross-functional initiatives.
Communicate complex analytical concepts and findings to non-technical stakeholders effectively.
Build and maintain strong relationships with internal stakeholders.