Fraud Data Analyst

At NALA, we have decided to prioritise two goals: reducing the cost and increasing reliability for sending money. We partner with governments to acquire licences, which in turn enable us to build innovative products and services, unlocking faster, safer, and more affordable cross-border payments.

We are looking for a Fraud Data Analyst to join our team. This is a full-time position requiring a BA/BSc/HND qualification, with experience in Data, Business Analysis, AI, ICT, or Computer fields.

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.

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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.

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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: Fully ramped on our case review process and rule ticketing workflow, independently handling a full caseload of false-positive and true-positive reviews. Shipped your first rule change proposals, each backed by clear before-and-after data. Built strong working relationships with the wider fraud and data teams.

6-Month Metrics: Measurable improvement in how accurately genuine customers are treated, without a rise in fraud losses. Owning incident response for fraud spikes end to end: triage, root cause, and fix, with minimal oversight. Recognized as the go-to person for turning case findings into rule changes across more than one market.

Method of Application

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