How to Assess Engineering Skills in Financial Services

To assess engineering skills in financial services, organizations should define the competencies needed for each role, develop interview content that measures both foundational engineering and AI skills, evaluate candidates in realistic environments, and standardize their interviews and scoring. By taking these steps, you’ll be able to reliably identify software engineers who can build secure products and thrive in the age of AI.

Financial services (FinServ) engineering teams operate in a high-stakes environment. Strict regulations and security requirements mean an engineering mistake has significant consequences. At the same time, organizations are often looking for skills to support legacy modernization, specialized architecture, and AI development, which can be difficult to find. 

In Karat’s survey of 303 CTOs, CIOs, VPs of Engineering, and other senior technology leaders in FinServ, respondents identified the top challenges that make it hard to hire right now. 

These findings indicate where technical assessments for financial services fall short and what effective assessments must measure and achieve. Ultimately, FinServ companies need a more consistent way to identify whether candidates have the skills needed to succeed in the AI era. 

Why Are Financial Services Leaders Struggling to Assess Engineers?

Our data points to four main reasons why FinServ leaders are having difficulties hiring.

• Lack of standardization: Different interviewers ask different questions, evaluate different competencies, and apply inconsistent scoring criteria. This produces noisy hiring signals and makes it difficult to fairly compare candidates. 
• Limited engineering bandwidth: Every interview pulls an engineer away from their work. When organizations hire at scale, that adds up and becomes a serious drag on engineering productivity.
• Lack of interviewers with specialized/advanced knowledge: Evaluating a senior distributed systems engineer or a financial systems architect requires domain expertise that organizations may not have enough of in-house, so interviews often get conducted by whoever is available rather than who is qualified. 
• Interviews haven’t been updated for the AI era: Many companies still use traditional assessment formats like coding tests and take-home assessments, which are more susceptible to AI-assisted cheating. Nearly two-thirds of companies still prohibit AI use in interviews, even though most engineers already use AI in their day-to-day work. This means most assessments don’t reflect today’s engineering work. 

How Should Financial Services Companies Assess Engineering Skills?

An effective engineering skills assessment is role-specific, structured, realistic, and designed to evaluate both technical capability and judgment. The following framework can help organizations develop structured technical interviews that produce a stronger, more consistent hiring signal. 

1. Define the Competencies That Matter for the Role

Start by listing the competencies for the role and level. While foundational engineering capabilities like coding still matter, FinServ roles usually require additional skills that reflect the complexity of the work and the risk involved. This can include:

• Security- and compliance-aware coding practices: Since FinServ organizations handle sensitive financial and customer data, engineers should be able to write secure code, handle data responsibly, implement appropriate access controls, and identify and mitigate risk. This skill has become particularly important as engineers increasingly work with AI tools, as 45% of AI-generated code has security flaws. 
• System design for regulated environments: Engineers need to understand the constraints that come with working in a regulated industry, not just general system design principles. They may need to design systems that balance performance and scalability with reliability, resilience, access control, data protection, and auditability requirements. 
• Legacy modernization: Modernizing legacy systems is a priority for many FinServ companies, and it requires a different skillset than building a new application. Engineers need to carefully make changes in systems with complex dependencies, business-critical workflows, and limited tolerance for downtime. Useful skills include the ability to understand unfamiliar codebases, map dependencies, prioritize risks, propose incremental migrations, and protect production stability throughout the process. 
• AI proficiency: An effective AI skills assessment for engineers shouldn’t just look at whether a candidate can use an AI coding assistant or write effective prompts. Engineers must also know how to evaluate AI-generated output, refine that output, and recognize when AI shouldn’t be used at all. A study found that developers with access to an AI assistant wrote significantly less secure code than those without access, and they were more likely to believe they wrote secure code. This illustrates why judgment is so important when working with AI.

2. Develop Relevant Interview Content

Once you’ve defined the competencies that matter, design interview content that allows candidates to demonstrate them. This is the best way to reveal whether a candidate can perform the work once hired. 

• Evaluate core competencies and AI skills. Interview content should assess the core competencies required for the specific role. It should also evaluate the AI skills candidates will need on the job. This includes AI fluency, output validation, responsible use of AI, and risk judgment. Although AI readiness is becoming expected, our data shows that organizations are behind in updating their technical assessments for the AI era. Only 33% of U.S. organizations use human + AI assessments, where candidates can use AI tools in a way that’s similar to day-to-day engineering work. 
• Assess technical judgment, not just output. Since AI is capable of producing working code in seconds, correctness and speed are no longer reliable signals on their own. Interview content needs to surface how a candidate reasons, makes decisions, and weighs trade-offs. 

3. Provide a Realistic Assessment Environment

The most predictive assessments put candidates in an environment that resembles their actual working environment. That’s why whiteboard exercises and isolated algorithm questions provide limited insight into how candidates navigate a real codebase, use development tools, or solve a problem when faced with ambiguity.

A realistic assessment environment gives candidates access to:

• A production-style, multi-file codebase
• A functional Integrated Development Environment 
• AI tools they would use on the job

4. Standardize Interviews and Scoring

Strong interview content can still produce inconsistent results if every interviewer conducts interviews and scores candidates differently. Creating a reliable, predictive assessment process requires standardization.

Aside from training interviewers to give all candidates the same experience, interviewers should use scoring rubrics. Rubrics define what it looks like when a candidate meets the bar versus demonstrating true mastery. This ensures scoring isn’t based on an interviewer’s gut feeling, and they protect against bias. 

How Karat Helps Financial Services Companies Assess Engineering Talent

FinServ engineering roles require a specific set of skills as organizations adopt AI, modernize legacy systems, and build increasingly sophisticated technology. Finding engineers who have them and can make sound decisions in a regulated industry is only half the challenge. FinServ organizations also need to be able to reliably assess whether a candidate has those skills. 

The cost of a bad hiring decision is particularly high. Engineering quality impacts security, reliability, operational resilience, and data protection. An engineering mistake that leads to a security incident or a breach of customer data is far more costly than a slower hiring process. 

Getting it right relies on clearly defined competencies, interview content that’s built around them, a realistic assessment environment, and standardized interviews and scoring. By following these steps, your technical interviews become reliable predictors of a candidate’s on-the-job performance. 

You can also partner with Karat to implement these steps, get help, and leverage our tools. Karat helps FinServ organizations improve the hiring signal that their technical assessments produce, resulting in a more effective and predictive hiring process.

With Karat NextGen, the first human-led, AI-enabled evaluation solution designed to identify AI-ready talent, teams can assess both engineering fundamentals and AI proficiency in a realistic environment. Karat also offers experienced Interview Engineers, standardized rubrics, and hiring insights to help you build a strong engineering organization with the right skills. 

Request a demo to see how Karat can help your team evaluate engineering talent more consistently and confidently.  

FAQs

What engineering skills should financial services companies assess?

Financial services companies should evaluate foundational engineering skills along with skills that are particularly important in complex financial environments, including security- and compliance-aware coding practices, system design for regulated environments, legacy modernization skills, and AI proficiency. 

How is AI changing engineering interviews in financial services?

AI makes it easier for candidates to generate working solutions and correct answers, which means output-based and asynchronous formats, like code tests and take-home assessments, provide a weaker signal of true engineering ability. To combat this, interviews should be conducted live by a human interviewer and show how candidates use AI, validate its output, reason through problems, and make decisions.

Why is it harder to assess engineering skills in financial services?

Financial services organizations often need talent with specialized skills in areas like legacy modernization, cybersecurity, regulated system design, and AI. They also operate under tight security and compliance requirements that raise the stakes of technical decisions. At the same time, FinServ leaders are struggling with inconsistent interview quality and limited interviewer bandwidth.

How can financial services companies create more consistent technical interviews?

Companies can make financial services technical interviews more consistent by clearly defining the competencies for each role, building interview content and scoring rubrics around those competencies, and applying the same technical bar to every candidate.