Senior Credit Risk Analyst

Overview & Responsibilities

• Develop and manage fraud risk and digital portfolio strategies by designing and monitoring key performance indicators (KPIs) to minimize fraud losses, optimize portfolio performance, maintain acceptable false positive rates, and limit impact to legitimate customers.

• Build, maintain, and monitor reporting infrastructure, including dashboards and analytics, to track fraud trends, digital portfolio performance, and emerging risks across the business.

• Analyze the financial impact of fraud and digital portfolio performance on overall profitability to establish and maintain risk tolerance thresholds, ensuring performance remains within acceptable limits.

• Investigate suspected fraudulent activity by partnering with branch representatives, providing guidance on application reviews, and supporting resolution of fraud cases.

• Ensure fraud risk policies and digital risk practices comply with all applicable federal, state, and regulatory requirements.

• Identify, assess, and communicate fraud risks, incidents, and portfolio performance trends to cross-functional stakeholders and executive leadership, providing actionable insights and recommendations.

• Collaborate with credit, marketing, and other business units to evaluate how upstream strategies impact fraud risk and digital portfolio performance, and communicate the downstream effects of strategy changes.

• Partner with Data Science teams to design, enhance, and validate machine learning models, including defining input data, target variables, and evaluating model performance for fraud detection and risk assessment.

• Design and implement fraud detection strategies across the full product suite using machine learning models, third-party fraud prevention tools, and internal data assets.

• Develop advanced fraud detection tools and methodologies using techniques such as graph databases, segmentation, and statistical inference.

• Analyze and classify default events to distinguish between fraud and non-fraud behavior, and further categorize fraud types (e.g., first-party, third-party, synthetic identity, bust-out, chargeback).

• Develop and deploy automated scripts and processes to screen and detect fraudulent applications in real time and at scale. EEOE

Requirements

• Bachelor’ degree (or foreign equivalent) in Statistic, Data Science, Business Analytics, or related and 3 years of experience as a data analyst, risk analyst, or a related role.

• Experience must include: Working with R programming to build decision tree and regression models, completing customer segmentation, formulating marketing strategies.

• Utilizing SAS statistical tools to analyze customer demographic information and credit history datasets for product portfolio optimization and sustainable user base expansion.

• Extracting and screening multi-dimensional data using SQL and developing underwriting frameworks for risk judgment and management capacity.

• Developing interactive self-service business intelligence dashboards using Tableau.