Fraud Data Analyst at NALA
- @TrendyJobbers | HR Outsourcing
- Full-time
- Posted 21 hours ago
- Apply Before: August 15, 2026
- 0 Click(s)
- View(s) 10
Job Detail
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Job ID 1020679
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Experience 3 Years
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Qualifications Degree Bachelor
Job Description
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
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