Fraud Data Analyst

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.

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

1. 3–5 years’ experience in fraud investigations, payment risk, or AML transaction monitoring, ideally in a fast-growing fintech, remittance, or PSP environment
2. Strong SQL skills, comfortable writing complex queries independently and validating data at scale, not just running pre-built reports
3. Solid working knowledge of fraud typologies (ATO, card testing, mule networks, APP scams, first-party fraud) and how they connect to detection logic
4. A track record of turning case-level findings into actual rule or policy changes, not just flagging issues and moving on
5. Sharp attention to detail across timestamps, device/IP/card sequencing, and behavioural patterns
6. Clear, structured written communication, able to produce a case pack or rule proposal that stands on its own without a follow-up meeting
7. Comfortable working with real autonomy. This role has genuine influence over fraud rules and customer experience across multiple markets
8. Python/pandas for deeper, ad hoc analysis
9. Experience working across multiple regulatory jurisdictions or in cross-border payments
10. Familiarity with AML/CFT frameworks and regulatory reporting
11. Experience using AI/LLM-assisted tools in an investigative workflow