A finance degree, a computer science diploma, and five years at a recognizable company used to tell employers quite a bit about a candidate. They still tell them something. But in fintech, they may no longer tell employers enough.
The reason is simple: the work is changing faster than many traditional credentials can keep up with.
A payments company may need engineers who understand artificial intelligence, fraud detection, APIs, and cloud security. A lending platform may need analysts who can work with large datasets while understanding credit risk and regulatory requirements. Compliance teams may need people who can interpret rules while also knowing how automated systems make decisions.
Hiring managers therefore face a practical question: Can this person actually perform the work the role requires today?
That question is pushing more employers toward demonstrated skills — assessments, work samples, portfolios, certifications, projects, and evidence of prior results — alongside traditional qualifications.
The shift is particularly relevant to fintech because technology and financial regulation meet in the same workplace. Employers need technical capability, financial knowledge, problem-solving ability, and judgment, often within the same team.
Why Fintech Job Requirements Are Changing
Fintech companies aren’t simply hiring people to perform established banking functions with newer software. They’re building products around artificial intelligence, digital payments, embedded finance, cybersecurity, automated compliance, data analysis, and other technologies that continue to change.
The World Economic Forum found that 97% of Financial Services employers surveyed expected AI and information-processing technologies to affect their businesses substantially by 2030.
The same research found that 95% of Financial Services and Capital Markets employers considered AI and big-data skills to be growing in importance. Technological literacy was identified by 84%, while 82% pointed to networks and cybersecurity.
Those findings create a hiring problem.
A candidate’s degree may have been earned several years ago. Their previous job title may also reveal little about whether they’ve worked with the tools, security risks, datasets, or AI systems a fintech employer uses now.
That’s one reason companies across sectors are shifting toward skills-based hiring. Instead of treating education and previous titles as the primary filters, employers can define the capabilities needed for a role and ask applicants to provide evidence that they possess them.
That doesn’t make education irrelevant. It changes the weight employers place on different signals.
Skills-Based Hiring Can Expand Fintech Talent Pools
Fintech companies compete for many of the same engineers, security professionals, data specialists, and AI practitioners sought by technology companies, banks, consulting firms, and other employers.
Restricting searches to people who already hold a narrow set of job titles can shrink the available pool considerably.
According to LinkedIn’s Skills-Based Hiring 2025 Report, matching Financial Services candidates by relevant skills rather than previous job titles could expand the potential talent pool by:
- 30.4 times in the United States
- 11.4 times in the United Kingdom
- 44.5 times in India
- 8.9 times globally
That doesn’t mean every person in the larger pool will be qualified. It means employers may be overlooking people who can perform the work because those candidates arrived through different career paths.
Someone who built fraud-detection systems for an e-commerce company, for example, may have valuable skills for a fintech fraud team even without previous employment at a bank.
A cybersecurity professional from healthcare may understand identity controls, incident response, encryption, and sensitive-data protection even if “financial services” never appeared on their résumé.
Skills-first screening gives hiring teams a way to find those adjacent candidates.
Which Skills Are Fintech Employers Looking For?
The exact combination depends on the role, but several competency areas are becoming particularly relevant.
AI and Data Fluency
AI capability is moving from a specialist requirement toward something relevant across a wider range of technology roles.
SHRM’s 2026 analysis of job postings across 27 countries found that the United States had the highest average share of IT and computer-science postings mentioning AI skills, at 28.5%.
Demand also varied considerably by occupation. Across the countries SHRM examined, the median share of postings mentioning AI ranged from 4.7% for network and systems support roles to 55.1% for data analysis and mathematics roles.
For fintech employers, AI competence can mean different things depending on the position. A machine-learning engineer may need to build and evaluate models. A product manager might need to understand where AI is useful, what its limitations are, and how model outputs affect customers. A risk professional may need enough AI literacy to evaluate automated decision systems.
Data fluency matters for similar reasons. Candidates may need to work with SQL, analytics platforms, reporting tools, experimentation frameworks, or large financial datasets.
Cybersecurity Skills
Financial companies hold valuable data and move money. That makes security knowledge relevant far beyond the security department itself.
Fintech employers may look for experience with:
- Secure application development
- Identity and access management
- Cloud security
- Encryption
- Threat detection
- Vulnerability management
- Incident response
- API security
- Security testing
A certificate can support a candidate’s case, but employers may also want examples of incidents handled, systems secured, vulnerabilities identified, or architectures designed.
Regulatory Knowledge
Fintech is technology operating inside a heavily regulated industry.
Depending on the company and product, roles may touch anti-money-laundering rules, know-your-customer requirements, consumer protection, data privacy, lending regulations, payments rules, securities regulations, or other obligations.
That means domain experience can sometimes outweigh a generic qualification.
An engineer who understands why certain transaction records must be retained, for example, may make better system-design decisions than someone with equivalent coding ability but no exposure to regulated financial products.
Problem-Solving and Communication
Technical proficiency alone doesn’t solve every fintech problem.
Developers work with compliance teams. Product managers coordinate with risk professionals. Data scientists may need to explain model behavior to people without technical backgrounds.
Employers can therefore look for evidence of reasoning and communication rather than assuming those abilities come automatically with a particular degree.
Case exercises and structured interviews can reveal how candidates investigate unclear problems, explain tradeoffs, respond to new information, and communicate recommendations.
How Employers Can Test Demonstrated Skills
Removing a degree requirement isn’t enough to create skills-based hiring. Companies need reliable ways to evaluate capability.
The OECD reports that about half of surveyed employers expect to use pre-employment skills tests between 2025 and 2030, while roughly one-third expect to use psychometric assessments.
For fintech roles, several approaches can work.
Coding Assessments
Engineering candidates can complete exercises based on the type of work they’ll actually encounter.
The best tests generally resemble the role rather than asking candidates to memorize obscure syntax. An API engineer could review faulty integration code. A security candidate could analyze a vulnerability. A data engineer could clean or query a sample dataset.
Portfolio Reviews
Candidates may already have proof of their abilities.
GitHub repositories, software projects, dashboards, published research, technical writing, open-source contributions, financial models, security projects, or product case studies can all provide useful evidence.
Portfolio work is particularly valuable for candidates entering fintech from another sector because it gives employers something concrete to evaluate when the applicant lacks the expected job title.
Work-Sample Exercises
A compliance candidate might review a hypothetical transaction-monitoring scenario. A product candidate could analyze a feature proposal involving payments. A data analyst might interpret customer-behavior data and explain the conclusions.
The exercise doesn’t need to be lengthy. It needs to reflect the abilities the job actually requires.
Structured Interviews
Structured questions can make comparisons between candidates more consistent.
Instead of asking broad prompts such as “Tell me about yourself,” employers can ask every candidate to explain how they handled a particular type of problem, what decisions they made, what evidence they used, and what happened afterward.
The OECD specifically recommends structured assessments and competency-based interviews as tools for evaluating demonstrated abilities.
Skills-Based Hiring Doesn’t Mean Credentials Are Disappearing
There is an important distinction between reducing unnecessary credential requirements and abandoning qualifications entirely.
More than 40% of employers surveyed in data cited by the OECD still expected university degrees to remain part of their hiring assessments between 2025 and 2030. Around 80% expected work experience to remain relevant.
Some fintech positions also operate under regulatory, licensing, professional, or technical requirements that make formal qualifications particularly useful or mandatory.
Accountants, lawyers, compliance specialists, actuaries, certain investment professionals, and highly specialized security practitioners may need specific licenses, certifications, or educational backgrounds.
Credentials can also provide evidence of foundational knowledge. The point isn’t to automatically remove them. It’s to ask whether each requirement has a clear relationship to the work.
Employers have already been questioning unnecessary degree screens for years. Research from Harvard Kennedy School and the Burning Glass Institute found that 46% of middle-skill occupations and 31% of high-skill occupations experienced meaningful reductions in degree requirements between 2017 and 2019.
The researchers estimated that continued adoption of such practices could make about 1.4 million additional jobs accessible to workers without college degrees over five years.
Employers Need Better Hiring Processes, Not Simply Fewer Requirements
A fintech company could remove degree requirements from every job description tomorrow and still fail to hire based on skills.
Hiring teams have to define what proficiency actually looks like.
Start by asking:
- Which skills does someone need on their first day?
- Which abilities can reasonably be learned after hiring?
- What evidence would show that a candidate can perform each major task?
- Are previous job titles being used as shortcuts for abilities that could be assessed directly?
- Are degree requirements tied to the actual work or simply inherited from an old job description?
- Which regulatory or professional requirements genuinely apply?
Companies hiring at scale may also need outside recruiting expertise, particularly when entering unfamiliar technical specialties. When choosing a staffing agency, employers can examine whether recruiters understand the skills behind the role rather than simply matching résumés by title and keyword.
That distinction becomes more important for positions combining several disciplines, such as AI and risk, cybersecurity and payments, or software engineering and regulatory compliance.
How Fintech Candidates Can Prove What They Can Do
Skills-based hiring also changes how candidates should present themselves.
Listing “Python,” “machine learning,” or “payments” on a résumé isn’t particularly persuasive by itself. Candidates can strengthen those claims with evidence.
LinkedIn’s Skills Signal report found that workers matched to jobs according to their skills qualified for roughly three times as many positions. Adding 10 skills to a LinkedIn profile was also associated with an employment gap about one month shorter.
For fintech candidates, demonstrating capability could mean:
- Publishing code samples or technical projects
- Building a small payment, lending, analytics, or fraud-detection application
- Completing relevant cybersecurity or cloud certifications
- Creating data-analysis projects using public financial datasets
- Showing measurable results from previous work
- Documenting AI projects and explaining their limitations
- Demonstrating knowledge of applicable financial regulations
- Writing about technical or regulatory problems in plain language
- Contributing to open-source projects
- Explaining how previous experience transfers into fintech
Candidates coming from banking, insurance, technology, cybersecurity, consulting, or other industries shouldn’t assume an unfamiliar job title automatically disqualifies them. They can make the connection explicit.
Show the employer which problems you’ve solved and how those experiences relate to the job they’re trying to fill.
Conclusion
Fintech hiring is moving toward demonstrated capability because employers need better evidence that candidates can handle work shaped by AI, cybersecurity, data, regulation, and rapidly changing financial technology.
Traditional credentials still have a place. Degrees can demonstrate foundational education. Certifications can verify specialized knowledge. Licenses remain required for certain regulated positions. Previous experience also continues to provide useful information.
But none of those signals answers every hiring question.
Skills-based approaches give employers another way to evaluate talent: define the work, identify the capabilities behind it, and ask candidates to demonstrate those capabilities through assessments, portfolios, projects, structured interviews, and documented results.
For employers, that approach can reveal qualified candidates whose titles or educational paths might otherwise cause them to be overlooked. For candidates, it puts greater value on tangible evidence of what they know and what they can do.
As fintech technology continues to change, that evidence may become one of the strongest signals available.
