The Fraud Analytics Manager translates fraud policies and procedures into testable decision logic, analyzes transactional and fraud data, validates business rules, and identifies process or data gaps. The role coordinates Fraud Operations, Business, Technology, Analytics, client stakeholders, and India-based delivery teams. Responsibilities include testing, reconciliation, evaluation framework design, sensitive-data controls, client presentations, documentation, and tracking risks and decisions. SQL, Python, and advanced Excel are used for analysis and validation, with preferred exposure to AI and LLM evaluation.
Job Location: Jersey City, NJ (Hybrid)
Salary Range: $100K - $130K/Yr Base plus Bonus and Benefits
We are seeking an experienced Fraud Analytics Manager to design and evaluate fraud-focused decisioning and conversational AI solutions for a leading banking client. The ideal candidate combines deep fraud-domain expertise with hands-on analytical skills and the ability to translate fraud policies, operating procedures, and data into clear, accurate, and testable decision logic.
The role calls for sound judgment in handling banking and transactional data, and the ability to coordinate across Fraud Operations, Business, Technology, and Analytics teams. The candidate will serve as the key US-based point of coordination between client stakeholders and India-based delivery teams.
Responsibilities- Partner with Fraud Operations, Business, Analytics, and Technology stakeholders to understand fraud processes, policies, data, and business requirements.
- Interpret and simplify fraud standard operating procedures (SOPs), translating them into clear, accurate, and testable decision logic.
- Review and validate decision logic against documented policies, operational scenarios, and expected business outcomes.
- Analyze transactional and fraud-related data, including disputes, unauthorized transactions, card activity, and investigation outcomes.
- Apply appropriate controls when handling personally identifiable information (PII) and other sensitive banking data.
- Use SQL, Python, and/or advanced Excel to perform data analysis, testing, reconciliation, and validation.
- Identify gaps, inconsistencies, and ambiguous scenarios across fraud procedures, data, and decision logic.
- Coordinate client discussions and drive alignment across Fraud Operations, Business, Technology, and other stakeholder groups.
- Prepare and deliver client-ready presentations, evaluation summaries, process documentation, and progress updates.
- Manage on-site client interactions and coordinate requirements, feedback, and delivery with India-based teams.
- Track key decisions, open questions, risks, dependencies, and validation outcomes through to completion.
REQUIRED SKILLS & QUALIFICATIONS
- Bachelor's or master's degree in Business, Finance, Economics, Statistics, Data Science, Computer Science, or a related field.
- 4+ years of experience in fraud analytics, fraud operations, risk analytics, or a related banking domain.
- Strong understanding of banking fraud, preferably in retail banking, consumer cards, payments, disputes, or fraud operations.
- Experience with transactional data, unauthorized transaction data, disputes, fraud cases, or investigation outcomes.
- Ability to interpret complex business policies and procedures and convert them into structured business rules or decision logic.
- Experience designing test cases, evaluation criteria, validation frameworks, or quality-control processes.
- Hands-on proficiency in SQL and working knowledge of Python and/or advanced Excel.
- Strong analytical judgment and attention to detail when reviewing data and business logic.
- Strong client coordination, stakeholder management, presentation, and documentation skills.
- Ability to explain complex fraud and analytical concepts clearly to both business and technical audiences.
- Experience working in cross-functional environments with geographically distributed teams.
PREFERRED QUALIFICATIONS
- Experience in consumer cards, retail banking, payments, or digital fraud.
- Basic understanding of AI and large language model (LLM) concepts, including prompting, fine-tuning, deterministic workflows, evaluation, and ground truth.
- Experience evaluating AI-generated outputs or recommendations against established business rules and expected outcomes.
- Familiarity with data-labeling or annotation processes and tools used to build golden datasets.
- Prior experience working directly with banking clients in an analytics, consulting, risk, or technology-delivery environment.
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