fraud prevention jobs in 2026 — demand, top roles hiring, and related skills
As of 2026-09-30, fraud prevention appears in 316 job postings indexed by Skillenai over the past 90 days — Product Manager has the most postings mentioning fraud prevention, with demand share up 5.2% vs the prior 4 weeks.
Last updated · 90d ending 2026-09-30
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Frequently asked questions about fraud prevention
+Is fraud prevention in demand in 2026?
Yes. fraud prevention appears in 316 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Product Manager accounts for the most postings mentioning fraud prevention (32.0% of all postings mentioning fraud prevention).
+What jobs require fraud prevention?
According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning fraud prevention are AI Applied Software Engineer (25.0% of that role’s postings mention fraud prevention), AI Data Annotator (4.5% of that role’s postings mention fraud prevention), Digital Product Owner (4.5% of that role’s postings mention fraud prevention).
+What skills are commonly paired with fraud prevention?
Across job postings indexed by Skillenai (90 days ending 2026-09-30), fraud prevention most often appears alongside SQL, Product Management, Python, Data analysis, Risk Management.
+Where is fraud prevention most in demand?
As of 2026-09-30, the metro areas posting the most jobs requiring fraud prevention are San Francisco, New York City, London, Chicago, Toronto, according to the Skillenai jobs index.
+How can I keep up with new fraud prevention content and jobs?
Skillenai indexes news, blog posts, and research papers mentioning fraud prevention alongside the jobs index. You can subscribe to a daily email digest of new fraud prevention content from your Skillenai account.
+Which skills come before and after fraud prevention?
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 prevention — last 90 days
Salary distribution
Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized
Career paths around fraud prevention
Skills documented before and after this skill across employer changes.
Historical career profiles · all locations
Skills before fraud prevention
Skills after fraud prevention
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: convolutional neural networks (CNNs) | 1 |
| Before: Recurrent neural networks (RNNs) | 1 |
| Before: python | 1 |
| Before: web servers | 1 |
| Before: Spring Boot | 1 |
| Before: SciPy | 1 |
| Before: Cucumber | 1 |
| Before: sql | 1 |
| After: Vulnerability Management | 1 |
| After: data protection | 1 |
| After: streaming pipelines | 1 |
| After: network security | 1 |
| After: risk management software | 1 |
| After: third-party | 1 |
| After: content delivery | 1 |
| After: financial modeling | 1 |
Roles most likely to require fraud prevention
Among roles with at least 20 postings in the same period.
| Role | Postings mentioning skill | % of role postings mentioning skill |
|---|---|---|
| AI Applied Software Engineer | 5 | 25.0% |
| AI Data Annotator | 1 | 4.5% |
| Digital Product Owner | 1 | 4.5% |
| Product Director | 1 | 2.2% |
| Implementation Engineer | 1 | 2.0% |
| Engineering Lead | 2 | 1.7% |
| Group Product Manager | 2 | 1.5% |
| Principal Product Manager | 3 | 1.2% |
| Associate Product Manager | 1 | 1.2% |
| Product Manager | 101 | 0.8% |
Roles with the most fraud prevention postings
| Role | Postings mentioning skill | Share of skill postings |
|---|---|---|
| Product Manager | 101 | 32.0% |
| Software Engineer | 25 | 7.9% |
| Data Scientist | 14 | 4.4% |
| Backend Engineer | 11 | 3.5% |
| Engineering Manager | 10 | 3.2% |
| Business Analyst | 7 | 2.2% |
| Product Owner | 7 | 2.2% |
| Affiliate Manager | 6 | 1.9% |
| AI Applied Software Engineer | 5 | 1.6% |
| Technical Program Manager | 5 | 1.6% |
Top companies posting jobs requiring fraud prevention
Employers ranked by indexed job postings in the last 90 days.
| Company | Postings · 90 days |
|---|---|
| Stripe | 17 |
| Airwallex | 8 |
| Capco | 8 |
| Vanguard | 7 |
| Socure | 6 |
| Wise | 6 |
| EverAI | 6 |
| Barclays | 5 |
| Melio | 5 |
| USAA | 5 |
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 prevention
| Name | Postings | Share |
|---|---|---|
| San Francisco | 19 | 6.0% |
| New York City | 15 | 4.7% |
| London | 13 | 4.1% |
| Chicago | 12 | 3.8% |
| Toronto | 11 | 3.5% |
| São Paulo | 9 | 2.8% |
| Seattle | 8 | 2.5% |
| Tel Aviv | 8 | 2.5% |
| Singapore | 6 | 1.9% |
Skills commonly paired with fraud prevention
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How this was computed
Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. 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 prevention by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s fraud prevention postings by all fraud prevention 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: 0.1% to 0.2%. 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 2,595 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (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
- 61911c74005665d0
- data_as_of
- 2026-09-30
- window_days
- 90
The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.
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