Payments & Fraud Data Analyst

B2spin

5d

About The Position

B2Spin is seeking a Payments & Fraud Data Analyst to join its Business Intelligence team. The role uses transactional and player-behaviour data to improve payment performance, prevent fraud and abuse, and protect the experience of genuine players. The analyst works closely with Payments, Fraud, Compliance and Operations to provide insights that support faster, sharper decisions.

Requirements

• Hands-on data analyst background in payments, fraud, risk or AML, ideally in gaming, iGaming, fintech or another high-volume digital environment.
• Strong SQL skills on a cloud data warehouse; BigQuery is preferred.
• Practical experience with Looker, Tableau or Power BI.
• Demonstrated experience analysing fraud and abuse patterns in transactional and behavioural data, with an understanding of the trade-off between fraud capture and friction for genuine players.
• Ability to take analytical projects from ambiguity to delivery, including scoping unclear questions, building and documenting solutions, and monitoring their impact over time.
• Clear, confident communication across Operations, Compliance and senior leadership, including the ability to challenge assumptions with data in a remote-team setting.
• Investigative instinct and sharp attention to detail.

Nice To Haves

• Python for deeper analysis, anomaly detection or modelling.
• Familiarity with fraud-scoring tools, device or behavioural signals, social casino mechanics, or machine-learning approaches to fraud detection.

Responsibilities

• Own end-to-end payment performance analytics, including success rates, declines, payment methods and provider performance, and identify ways to reduce friction and cost.
• Identify fraud and abuse patterns in transactional and behavioural data, including promo abuse, multi-accounting, chargebacks and money cycling, and translate findings into actionable rules, thresholds and segments.
• Design, back-test and monitor automated risk decisions, balancing fraud detection with friction for genuine players and measuring real-world impact after changes go live.
• Build dashboards in Looker or Tableau and alerts for business-critical anomalies, such as provider volume drops or risk spikes.
• Partner with Compliance on AML work by providing investigation queries, data analysis and audit-ready reporting.
• Use analytics to quantify Operations and Customer Service review workloads, case outcomes and player-facing friction.
• Document data sources, metric definitions and automation logic so insights are trusted, reusable and easy to build on.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed