Fraud Data Analyst

👋 About Us NALA is building Payments for the Next Billion. Faster, smarter, and fairer transfers for everyone. Since 2022, we've grown our business 120x, grown the team from 9 to 150+, raised $50M+ from top-tier investors , and were named to the Forbes Fintech 50 in 2025 & 2026. We operate two core products: NALA , our consumer app makes cross-border payments cheaper, faster and more reliable for the global diaspora. Allowing users to send money from the UK, US and EU to Africa and Asia. Rafiki , our B2B payments infrastructure, is powering global payments for global giants like MoneyGram & Western Union. Our team includes alumni from Wise, Stripe, Monzo, Revolut, and CashApp operators who've scaled world-class products. We act with urgency, think deeply, and put our customers first always. At NALA, this isn't just a job. It's ownership, impact, and the chance to change global payments forever. Join us in building Payments for the Next Billion . 🙌 Your Mission This role sits between fraud investigation and rule development. You will review case outcomes, feed what you learn back into our fraud rules, and help keep the experience smooth for genuine customers. You will own it end to end: Investigating cases, classifying fraud typologies, proposing rule changes, and checking customer impact before anything ships. It's not a labelling job, you're the person who turns individual case judgment into changes that make our fraud rules better over time. As NALA grows into new markets and account types, this role grows with it. You will be working closely with the fraud and data teams, using AI tools to move faster on triage and drafting, while still applying your own judgment to every case that matters. 🎯 Your Responsibilities in this Role False-positive review: Investigate legitimate customers who were wrongly blocked or held up by extra verification steps, quantify the impact, and propose fixes that reduce friction without opening new fraud risk. True-positive typology & evidence: Classify confirmed fraud cases into typologies (ATO, card testing, first-party, APP scams, mule networks, and others) with structured, evidence-backed case packs, not just labels. Bridge to rule development: Turn your findings into clear rule change proposals for the team that implements them, and help keep our detection sharp over time. Incident response: During fraud spikes or new attack patterns, quickly investigate affected customers, find the root cause, and recommend both an immediate fix and a longer-term one. AI-augmented workflows: Use AI tools to speed up triage and drafting, while checking every output against the underlying data rather than taking it at face value. Requirements 🔥 Must-have requirements 3–5 years' experience in fraud investigations, payment risk, or AML transaction monitoring, ideally in a fast-growing fintech, remittance, or PSP environment Strong SQL skills, comfortable writing complex queries independently and validating data at scale, not just running pre-built reports Solid working knowledge of fraud typologies (ATO, card testing, mule networks, APP scams, first-party fraud) and how they connect to detection logic A track record of turning case-level findings into actual rule or policy changes, not just flagging issues and moving on Sharp attention to detail across timestamps, device/IP/card sequencing, and behavioural patterns Clear, structured written communication, able to produce a case pack or rule proposal that stands on its own without a follow-up meeting Comfortable working with real autonomy. This role has genuine influence over fraud rules and customer experience across multiple markets 💪 Nice to have requirements Python/pandas for deeper, ad hoc analysis Experience working across multiple regulatory jurisdictions or in cross-border payments Familiarity with AML/CFT frameworks and regulatory reporting Experience using AI/LLM-assisted tools in an investigative workflow ✅ Success in the role looks like 3-Month Metrics