Fraud Analyst

About Selina

At Selina Finance, our mission is to provide exceptional value with simple and flexible financial products. We’re achieving this by building a next-generation digital lender targeting homeowners across the UK. More than £2tn of equity value is locked up in UK homes, while homeowners still need to rely on high-interest-rate consumer loans, credit cards, or overdrafts. We believe this is unfair and unnecessary. We offer a financing product unlike anything else out there, one that is as flexible as a current account, as affordable as a mortgage, and as easy to apply for as a consumer loan.

We are building an incredible team and culture of high performance, development, reward and recognition, ready for the next phase of our adventure, and this is where you come in!

We have quite a few things to be proud of

🌏 We have 19 different nationalities across our London + Manchester and remote teams
🏆 Voted as ‘one of the fastest-growing businesses in the UK’ by Business Leader Growth 500 for 2025
🏆 Voted top 30 of ‘Tempo’s 100 best start-ups to work for’
🦾 Disability Confident Committed employer
🏆 BCorp Certified
✏️ Rated highly in our employee survey on:
✔Workplace Culture
✔Work Life/Balance
✔Employee Recognition

About the Role

This is a high-impact analytics role within our Risk & Compliance team. As a Fraud Analyst, you’ll use data to identify and prevent fraudulent activity, helping protect both our customers and the business, while ensuring genuine customers move through our products with minimal friction.

You’ll be part of a dynamic and collaborative team, and your insights will directly influence live fraud decisions and customer outcomes every day. Fraud is constantly evolving. As we scale our products and customer base, our ability to detect, prevent, and adapt to new fraud risks is critical.

What will you be doing?

• Monitoring fraud performance dashboards across products and channels

• Investigating anomalies, spikes, and unusual behavioural patterns

• Writing SQL queries and using Python to explore data and answer time-sensitive questions

• Designing, testing, and optimising fraud rules and controls

• Translating analytical findings into clear recommendations

• Proactively identify emerging fraud patterns

• Strengthen fraud controls across new onboarding journeys

• Reduce fraud losses while maintaining a seamless customer experience

• Continuously improve fraud decisioning through data-led insights