Fraud Data Analyst

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

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