Senior Card Fraud Risk Analyst

1d1 day ago

Capital on Tap

London, GB · Full-time · £70,000 – £90,000

About this role

Capital on Tap is a financial platform offering a business credit card, spend management tools, and a savings product that reached £1 billion in its first year. The company serves over 200,000 customers with £20bn in annual card spend and maintains strong profitability and customer ratings.

As Senior Card Fraud Risk Analyst you will take direct ownership of fraud systems from onboarding through to card fraud. The role involves writing complex SQL queries against transaction and customer data to identify signals and emerging attack patterns while designing detection rules end-to-end.

You will work in a small, fast-moving hybrid team based in the London office three days per week. The environment requires comfort with messy, high-volume datasets and the ability to spot problems in the data and fix them without waiting for direction.

The position offers the chance to contribute to fraud strategy as the team expands and to shape ML-based detection capabilities for new products. You will own rules through deployment and monitor live performance to protect the platform.

Requirements

Strong data analyst background in a fintech or digital lending environment with experience handling messy, high-volume SMB or consumer transaction data.
Advanced SQL skills including CTEs, window functions, and the difference between WHERE and HAVING; tested at case study stage.
Genuine builder instinct: find problems in the data and fix them without waiting to be told.
Experience working end-to-end on analytical problems and owning logic through to deployment rather than handing off to a tech team.
Comfortable in a small, fast-moving team where answers are not always obvious and must be worked out independently.

Responsibilities

Write complex SQL queries to interrogate transaction and customer data, identify fraud signals, and surface emerging attack patterns.
Design and build fraud detection rules from scratch, set thresholds based on data, and iterate based on live performance.
Own rules end-to-end: spot the pattern, build the detection, and monitor the outcome.
Work across card and lending data to understand customer behaviour and where fraud risk sits in the product set.
Contribute to fraud strategy as the team grows, including input into ML-based detection.