Risk Analyst (SQL), Card Payment Fraud

Responsibilities:

Real-time transaction monitoring: Monitor card authorization flows and flag suspicious activity across debit/credit card portfolios.
Fraud rule strategy: Design, test, tune, and deploy fraud detection rules and risk scoring strategies at the authorization (auth) decision point — balancing fraud capture rate against false-positive / decline rates.
Investigation & case management: Investigate suspected fraud cases (account takeover, counterfeit/cloned cards, stolen card, CNP fraud, first-party/friendly fraud), determine root cause, and take action (block, step-up, decline).
Loss & performance analysis: Track fraud losses, chargeback rates, approval rates, and key risk KPIs; produce dashboards and reporting for management.
Chargeback & dispute support: Work with dispute/chargeback teams on fraud-related chargebacks and network (Visa/Mastercard) reason codes.
Cross-functional collaboration: Partner with product, engineering, data science, and compliance to improve fraud models, tooling, and controls.
Emerging threat detection: Stay ahead of evolving fraud typologies, BIN attacks, enumeration/testing attacks, and organized fraud rings.

Requirements:

3+ years in card payment fraud risk, ideally on the issuer or card processor / issuer-processor side.
Solid understanding of the card payment lifecycle: authorization, clearing, settlement; the roles of issuer, acquirer, card network (Visa/Mastercard), and processor.
Familiarity with authorization message flows (ISO 8583), auth/decline codes, AVS, CVV, and 3-D Secure (3DS) / SCA.
Working knowledge of fraud typologies: card-not-present (CNP) fraud, account takeover (ATO), counterfeit/cloned cards, lost/stolen, BIN attacks, card testing/enumeration.
Experience with fraud detection / rules engines (e.g., Falcon, SAS, Feedzai, Featurespace, or in-house platforms).
Strong data analysis skills: SQL required; ability to interrogate transaction data and quantify fraud/risk trade-offs.
Understanding of chargeback lifecycle and network dispute reason codes.

Nice to have:

• Experience tuning real-time authorization rules and measuring fraud vs. false-positive trade-offs.
• Exposure to machine learning fraud models and feature engineering for risk scoring.
• Crypto card experience: familiarity with crypto-linked cards and crypto-to-fiat settlement at auth.
• Knowledge of PCI-DSS and relevant regulatory/compliance requirements.
• Industry certifications (e.g., CFE — Certified Fraud Examiner).