fraud detection jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-10-28, fraud detection appears in 622 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning fraud detection, with demand share up 4.2% vs the prior 4 weeks.
Last updated · 90d ending 2026-10-28
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Frequently asked questions about fraud detection
+Is fraud detection in demand in 2026?
Yes. fraud detection appears in 622 job postings indexed by Skillenai over the 90 days ending 2026-10-28. Data Scientist accounts for the most postings mentioning fraud detection (14.6% of all postings mentioning fraud detection).
+What jobs require fraud detection?
According to the Skillenai jobs index over the 90 days ending 2026-10-28, among roles with at least 20 postings, the highest shares mentioning fraud detection are Fraud Data Analyst (45.0% of that role’s postings mention fraud detection), Data Scientist Intern (8.0% of that role’s postings mention fraud detection), Machine Learning Engineering Manager (6.6% of that role’s postings mention fraud detection).
+What skills are commonly paired with fraud detection?
Across job postings indexed by Skillenai (90 days ending 2026-10-28), fraud detection most often appears alongside SQL, machine learning, Python, Data analysis, anomaly detection.
+Where is fraud detection most in demand?
As of 2026-10-28, the metro areas posting the most jobs requiring fraud detection are San Francisco, New York City, London, Toronto, Bengaluru, according to the Skillenai jobs index.
+How can I keep up with new fraud detection content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning fraud detection alongside the jobs index. You can subscribe to a daily email digest of new fraud detection content from your Skillenai account.
+Which skills come before and after fraud detection?
The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.
Weekly indexed postings requiring fraud detection — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around fraud detection
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before fraud detection
Skills after fraud detection
How to read this chart · view counts
Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.
The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.
Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.
| Connection | Moves |
|---|---|
| Before: python | 18 |
| Before: Power BI | 13 |
| Before: Tableau | 13 |
| Before: sql | 12 |
| Before: spark | 7 |
| Before: docker | 7 |
| Before: SQL Server | 7 |
| Before: ci/cd | 6 |
| After: sql | 8 |
| After: ci/cd | 7 |
| After: python | 7 |
| After: ETL | 7 |
| After: Tableau | 7 |
| After: Azure Data Factory | 6 |
| After: HIPAA | 6 |
| After: PySpark | 5 |
Roles most likely to require fraud detection
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| Fraud Data Analyst | 9 | 45.0% |
| Data Scientist Intern | 7 | 8.0% |
| Machine Learning Engineering Manager | 4 | 6.6% |
| Deployment Engineer | 2 | 5.1% |
| Threat Analyst | 1 | 5.0% |
| Risk Analyst | 3 | 4.8% |
| Chief Information Security Officer (CISO) | 1 | 4.8% |
| Data Analytics Director | 1 | 4.5% |
| Java Engineer | 2 | 4.4% |
| Back-end Developer | 1 | 4.3% |
Roles with the most fraud detection postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Data Scientist | 91 | 14.6% |
| Product Manager | 83 | 13.3% |
| Software Engineer | 54 | 8.7% |
| Machine Learning Engineer | 29 | 4.7% |
| Data Analyst | 28 | 4.5% |
| Engineering Manager | 16 | 2.6% |
| Security Engineer | 13 | 2.1% |
| VP Engineering | 11 | 1.8% |
| Fraud Analyst | 9 | 1.4% |
| Fraud Data Analyst | 9 | 1.4% |
Top companies posting jobs requiring fraud detection
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Stripe | 30 |
| Shift Technology | 25 |
| Sezzle | 16 |
| Twilio | 14 |
| Tide | 12 |
| Mastercard | 11 |
| Barclays | 10 |
| Wise | 9 |
| Adyen | 8 |
| Tripledotstudios | 8 |
Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.
Top metros hiring for fraud detection
| Name | Postings | Share |
|---|---|---|
| San Francisco | 34 | 5.5% |
| New York City | 30 | 4.8% |
| London | 23 | 3.7% |
| Toronto | 19 | 3.1% |
| Bengaluru | 14 | 2.3% |
| Seattle | 13 | 2.1% |
| Singapore | 12 | 1.9% |
| Amsterdam | 10 | 1.6% |
| Paris | 10 | 1.6% |
Skills commonly paired with fraud detection
Explore related pages
How this was computed
Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-28. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning fraud detection by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s fraud detection postings by all fraud detection postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.3% to 0.3%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,482 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-06-30 to 2026-09-28 and 2026-07-28 to 2026-10-26 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.
- source
- Skillenai jobs index, deduplicated daily
- entity_id
- ef47b08d434d2ea0
- data_as_of
- 2026-10-28
- window_days
- 90