Fraud Data Analyst
AI summary
NALA is hiring a Fraud Data Analyst in Nairobi to bridge fraud investigation and rule development. You will investigate cases, classify fraud typologies, propose rule changes, and use AI tools to improve detection while protecting genuine customers. The role requires 3–5 years of fraud or payment risk experience and strong SQL skills.
- Own fraud cases end to end: investigation, typology classification, and rule change proposals
- Use AI tools to speed up triage and drafting while validating outputs against data
- Strong SQL and fraud typology knowledge required
- 3–5 years in fraud investigations, payment risk, or AML monitoring preferred
- Real autonomy influencing fraud rules across multiple markets
AI job guide
Use this guide to check salary signals, requirements, documents, application steps and safety before you apply.
AI salary guide
Not enough public dataNot enough public salary data is available for this exact role. Before applying, prepare to ask about gross pay, benefits, contract length, probation period, transport and any allowances.
Can you qualify for this role?
- Required3+ years of relevant experienceThe job post includes a minimum experience signal.
- RequiredEducation or certification mentioned in the postThe captured text mentions education, a diploma, certificate, or licence.
- PreferredPractical evidence in security, seguranca, technologyThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in NairobiThe vacancy is associated with this location.
- UnclearComfort with the Full Time contract termsConfirm hours, duration, probation and benefits at the original source.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- Role specificAcademic or professional certificates
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest Fraud Data Analyst evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to NALA and the role in Nairobi.
- Add concrete examples related to security, seguranca, technology, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from Opened Career Kenya; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
- Prepare a polite question about pay, benefits and contract terms for later interview stages.
Source and safety check
- Opened Career Kenya
- Original source link available
- Application method is clear
- Deadline not specified
- No major risk signal was detected in the captured text.
Never pay for interviews, shortlisting, medical checks, uniforms, or job placement. Confirm every application at the original source before sharing personal documents. Report suspicious listing.
Interview preparation
- What experience makes you a strong fit for this Fraud Data Analyst role in security, seguranca?
- How have you handled responsibilities similar to those in this job post?
- Are you available to work in Nairobi under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research NALA before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
- Fraud Data Analyst at NALA
- in
- Cyber Security & Information Security
- Full Time
- Nairobi
- ,
- KENYA
- 2026-08-04
- Job Overview
- Date Posted
- 2026-08-04
- Location
- Nairobi
- ,
- KENYA
- Expiration date
- 2026-10-04
Experience
3 Years
Gender
Both
Qualification
Bachelor Degree
Job Description
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
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
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
across multiple markets
Nice to have
working across multiple regulatory jurisdictions or in cross-border payments
Familiarity with AML/CFT frameworks and regulatory reporting
using AI/LLM-assisted tools in an investigative workflow
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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
- 3–5 years’
- Python/pandas for deeper, ad hoc analysis