Lead Fraud Analyst
Job Description
REQUIREMENTS
● Professional Experience: 3–5+ years of hands-on experience in transactional payment fraud prevention, transaction monitoring rules development, or database analytics within a fintech neobank, credit card issuer, or processor.
● Mandatory SQL Expertise: Elite, active SQL capabilities for querying transaction databases and structural data extraction. Python or R proficiency is a strong advantage.
● Rules Engine Experience: Practical, hands-on experience configuring and tuning transactional rules and ongoing account monitoring alerts.
● Domain Mastery: Deep, granular understanding of payment card risk, merchant category codes (MCCs), authorization messages, digital wallet risk, account takeover (ATO) vectors, and card-not-present (CNP) fraud.
● Identity & Onboarding Awareness: Strong conceptual awareness of digital onboarding risk platforms (Alloy, Socure, SentiLink) is preferred but not required.
● Agile Execution: High enthusiasm for a startup environment, comfortable balancing executive-ready data-modeling with rolling up your sleeves to manage manual transactional review queues.
● Quantitative Background: B.S. in Mathematics, Statistics, Computer Science, Finance, Management Information Systems, or a highly quantitative field.
RESPONSIBILITES
Post-Onboarding Rules Architecture & Ongoing Account Monitoring
● Rule Development & Integration: Design, test, and deploy real-time post-onboarding transaction rules to block active fraud patterns and authorization exploits.
● Velocity & Behavioral Limits: Build, simulate, and configure dynamic transaction velocity limits, behavioral profiling rules, and risk thresholds to catch card-not-present (CNP) fraud, credit line bust-outs, and digital wallet (Apple Pay/Google Pay) provisioning abuse.
● Ongoing Account Monitoring: Design, configure, and maintain real-time monitoring rules for non-transactional account activities (such as profile edits, changes to PII, email/phone updates, password resets, and linking/unlinking funding sources) to detect and prevent Account Takeover (ATO) or first-party abuse before unauthorized transactions occur.
● Behavioral Account Profiling: Establish customer-level usage baselines and ongoing behavior models to identify anomalies in login frequencies, geo-location hops (impossible travel), and device-fingerprint shifts to ensure account health and detect compromised sessions.
● Friction & Approval Optimization: Continually analyze and adjust rules based on risk-to-friction cost models, optimizing approval ratios to keep net fraud losses below targets while minimizing false-positive declines for legitimate customers.
Hands-On Queue Management & Rapid Incident Response
● Transaction Alert Triage: Actively monitor and work the live, high-risk transactional alert queue, executing swift, defensible block or release actions.
● Real-Time Incident Mitigation: Translate frontline alert queue insights into immediate rules adjustments, creating localized merchant blocks or temporary velocity limits to mitigate active fraud attacks in real time.
● SLA Compliance: Strictly manage operational processing windows for held alerts, maintaining a high throughput rate to minimize manual queue build-ups and meet cardholder expectations.
ACH Payment Fraud & Hold Strategies
● ACH Risk Architecture: Design and deploy automated monitoring rules specifically for ACH transactions, focusing on detecting return codes (e.g., R01, R03, R05), unauthorized debit patterns, and high-velocity funding anomalies.
● Dynamic ACH Holds: Configure risk-based ACH hold strategies that leverage account tenure, verified balance history, and velocity metrics to effectively mitigate return risk without negatively impacting the customer experience.
● Exception Management: Monitor ACH exception queues and manage return-to-originator processes to minimize financial exposure while maintaining strict regulatory compliance.
Advanced SQL Data Mining & Loss Diagnostics
● Forensic Database Querying: Write daily, complex SQL queries on raw transactional tables to conduct post-mortem audits of cardholder fraud events and identify rules vulnerabilities.
● Risk Reporting & BI: Build and maintain interactive dashboards to track rule efficiency, transaction risk metrics, and gross and net loss curves for executive-level reporting.
● Loss Attribution Modeling: Partner with Credit Risk to perform detailed root-cause reviews on write-offs, distinguishing transactional fraud occurrences from credit risk defaults.
Customer Journey Alignment & Lifecycle Collaboration (Preferred)
● Onboarding Risk Literacy: Maintain familiarity with digital onboarding identity verification (IDV) platforms (Alloy, Socure, SentiLink) to seamlessly link application-stage risk signals with post-onboarding transaction rules.
● Product & Payment Security: Partner directly with Product and Engineering to design and configure transactional limits, secure provisioning holds, and authorization parameters for new card feature releases.
Are you interested in this position?
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