Credit Risk Analyst *** Direct end Client ***

Responsibilities

• Lead the development and optimization of credit risk and fraud strategy for frontend decisioning across consumer financial products.
• Perform credit risk analytics, fraud analysis, and portfolio monitoring to assess policy effectiveness, loss trends, and customer outcomes.
• Translate complex analyses into datadriven narratives and executiveready insights to influence stakeholders across product, risk, and operations.
• Build and validate analytical assumptions using SQLbased analysis, hypothesis testing, and structured problem solving.
• Own ongoing policy performance monitoring, KPI tracking, and reporting to support risk appetite alignment and growth objectives.
• Identify opportunities to improve customer segmentation, acquisition strategy, fraud detection, and credit policy optimization.
• Partner crossfunctionally with Product Management, Data Science, Engineering, Underwriting, Compliance, and Marketing to launch and scale risk initiatives.
• Collaborate with fraud operations and data science teams to enhance fraud rules, decisioning logic, and analytics frameworks.
• Analyze largescale transactional and behavioral datasets using big data platforms to uncover actionable insights.
• Design, implement, and evaluate A/B tests, challenger strategies, and experiments to measure impact on fraud rates, approval rates, and portfolio performance.
• Drive execution from strategy through implementation, ensuring ideas convert into measurable business outcomes.

Required Qualifications

• 5 7 years of experience in Fraud Analytics, Credit Risk, Risk Strategy, or Fraud Operations within fintech, banking, payments, or consumer lending.
• Strong knowledge of fraud typologies, including:
  • Synthetic identity fraud
  • Account takeover (ATO) / phishing
  • First and thirdparty fraud
  • ACH fraud and disputes
• Handson experience with fraud detection and identity verification platforms (e.g., Alloy, Sift, Ekata, Kount, or similar tools).
• Advanced analytical skills using SQL; strong Excel proficiency required.
• Experience with BI and reporting tools (Looker, Tableau, Power BI, or equivalent).
• Working knowledge of financial regulations and fraud frameworks, including NACHA/ACH rules, Regulation E, KYC, and AML fundamentals.
• Proven ability to perform rootcause analysis, investigations, and datadriven decisioning.
• Excellent communication skills with experience presenting findings to senior leadership and crossfunctional teams.

Nice to Have

• Experience supporting digital banking, challenger banks, fintech lending, or payments platforms.
• Exposure to fraud rules management, decision engines, or machinelearningbased fraud detection models.
• Familiarity with ACH return codes, dispute workflows, chargebacks, or payment operations.
• Experience working in highgrowth, datadriven environments with ambiguous problem spaces.

Credit Risk Analytics, Fraud Risk Management, Fintech, SQL, Payments Risk, Portfolio Analytics, Decisioning Strategy, ACH Fraud, Identity Fraud, Risk Segmentation, Data Science, BI Reporting, KYC, AML, Reg E