Financial services engineering leaders are tasked with modernizing legacy systems, shipping new digital products, and navigating an AI revolution – all while competing with big tech and fast-growing startups for increasingly scarce engineering talent. 

To better understand where FinServ engineering leaders are feeling the most pressure, Karat analyzed data from 303 CTOs, CIOs, VPs of Engineering, and other senior technology leaders across the industry. Respondents identified the hiring and technical interview challenges creating the most pressure for their organizations today.

Engineering Hiring Challenges in Financial Services at a Glance

What the Data Reveals About Engineering Talent Challenges in Financial Services

The results paint a clear picture of what’s actually getting in the way of building high‑performing engineering teams in financial services and where leaders are looking for help. 

Interview Quality and Process Consistency Are the Dominant Concerns

The top three pain points are all about how organizations evaluate candidates. Inconsistent interview quality, bandwidth constraints, and difficulty evaluating specialized roles together account for over half (58%) of responses. 

FinServ tech leaders are struggling with the reliability of their assessments. It’s difficult to confidently evaluate technical talent while maintaining hiring quality and protecting engineering productivity. 

AI Is Reshaping Hiring From Two Directions

AI-assisted cheating (14.9%) and difficulty evaluating specialized/advanced roles (17.2%) make up nearly a third of responses. This shows how AI is both a threat to interview integrity and a capability gap in organizations’ evaluation criteria. 

As AI changes how software engineers work, hiring teams must adapt how they assess technical talent. Traditional assessments such as take-home assignments and coding tests no longer provide enough confidence when evaluating modern engineering skills. 

Poor Candidate Experience Is Costing FinServ Firms Engineering Talent

The combination of ineffective assessments (13.9%) and candidate drop-off (7.6%) indicate that a meaningful portion of FinServ engineering teams is losing candidates to their own process, rather than competitors. In a market where FinServ firms already face headwinds against tech companies and fintechs, a friction-heavy interview process is a self-inflicted wound. 

These trends show that FinServ engineering leaders need better, more reliable signals and less friction in how they evaluate talent. Now let’s take a look at how each of these pain points can be addressed with targeted changes to your interview design, process, and infrastructure. 

Inconsistent Interview Quality Across Interviewers

Many organizations assume their interview process is standardized. In reality, candidates may receive entirely different experiences depending on who conducts the interview. 

Common Causes of Interview Inconsistency

Different interviewers may:

• Ask different technical questions.
• Evaluate different competencies.
• Apply Inconsistent hiring standards.
• Interpret candidate performance differently.

This inconsistency creates noisy hiring signals and makes it difficult to compare candidates fairly.

In FinServ, this problem is amplified by scale and urgency. Regulatory buildouts, AI initiatives, and modernization projects create sudden surges in hiring, and organizations respond by quickly pulling in more interviewers without providing the proper training or calibration needed for consistency. 

Recommended Strategies

• Design structured interviews and standardize criteria. Clearly define the competencies required and build interview content around them, so every candidate is evaluated against the same criteria.
• Use scoring rubrics. Rubrics make it clear to interviewers what it looks like when a candidate merely meets the bar or demonstrates true mastery. This reduces subjectivity and makes it easier for interviewers to score candidates consistently. 
• Leverage experienced interviewers at scale. By centralizing technical interviews with calibrated interviewers, FinServ organizations can ensure that every candidate meets the same bar. Karat’s standardized live interviews help enforce that consistency while freeing internal engineers to focus on core work. 

Limited Engineering Bandwidth to Conduct Technical Interviews

Every interview pulls an engineer out of their work. This is manageable when you have little to moderate hiring needs. At scale, it becomes a serious drag on engineering productivity.

Simultaneously, candidate experience and interview quality can slip when busy engineers are squeezing interviews in between their work. Interviewers may show up late to the interview or appear unfocused. 

Recommended Strategies

• Centralize interview infrastructure. By centralizing technical interviews with partners like Karat, FinServ organizations can reduce the burden on their engineers while maintaining or improving hiring rigor. Karat’s centralized model helps improve completion rates and consistency while freeing internal teams to focus on their work. 
• Improve signal quality earlier in the hiring process. When the assessment generates a strong, accurate hiring signal, you need fewer total interviews to make a confident decision. Replacing low‑value screenings and long take‑home tests with live interviews upfront lets hiring managers quickly identify strong candidates, so engineers spend their limited time only on those who have already been well‑vetted.

Difficulty Evaluating Candidates for Specialized/Advanced Roles

Evaluating a senior distributed systems engineer, AI/ML specialist, or financial systems architect requires domain expertise, and many FinServ organizations don’t have enough interviewers with that knowledge. As a result, interviews are conducted by whoever is available rather than who is qualified. 

Recommended Strategies

• Expand access to qualified interviewers. Some organizations are partnering with external interviewers who have niche skill sets or experience in specialized domains. Karat’s network of Interview Engineers includes specialists across a wide range of skillsets, enabling consistent, expert-level evaluation for advanced or niche roles.  
• Adopt AI readiness assessments. As AI reshapes what strong engineering looks like, evaluating specialized candidates means going beyond traditional technical benchmarks. Assessments that test AI-native skills help organizations set a consistent, future-facing bar for advanced roles where AI proficiency is increasingly a baseline expectation.

AI-Assisted Cheating and Candidate Authenticity Concerns

As generative AI becomes a standard part of engineering workflows, hiring teams now need to distinguish between candidates who can effectively use AI and candidates who rely on AI to compensate for skill gaps. This has led to growing concerns around AI-assisted cheating in technical interviews, where candidates use generative AI tools to complete coding assessments without demonstrating underlying technical understanding.

Engineering leaders have a good reason to be concerned about this, as we’ve seen a 5x increase in suspicious candidate behavior over the past two years. 

Recommended Strategies

• Evolve interviews to assess reasoning and AI use. Interviews that ask candidates to reason through a problem and communicate their thinking help separate those who understand the underlying concepts from those who are simply pasting in AI-generated code. They also give you a clearer view into how candidates would use AI in your environment, not just whether they know the right prompts.
• Lean into live interviews. They’re more difficult to game compared to take-home assessments or assessments that only look at the candidate’s output. 
• Define a consistent standard for AI-assisted cheating. Rather than relying on individual judgment, create clear guidelines for acceptable AI use and train interviewers to apply them consistently. 
• Partner with expert interviewers who test AI-native skills. Karat interviews measure foundational engineering skills and AI‑native abilities like validating AI‑generated code, prompt engineering, debugging, and technical reasoning in structured live environments. This helps organizations assess how candidates will actually work in an AI‑augmented world. 

Ineffective Assessments Causing Candidate Drop-Off

Take-home assessments were originally designed to improve hiring quality. However, they may be creating friction for both candidates and employers.

Strong candidates often have multiple opportunities and may be unwilling to spend hours completing unpaid assignments on top of their current job. Requiring candidates to complete a time-consuming assessment can push them to drop out of the process or choose other job opportunities. 

Additionally, take-home assessments produce a weak hiring signal since it’s difficult to verify whether the candidate’s work is truly theirs. 

Recommended Strategies

• Replace take-home assessments with structured live interviews. The most effective interviews are designed to measure competencies in live, realistic environments. What this looks like today is giving candidates a production-grade, multifile codebase and an integrated development environment (IDE) with a built-in AI assistant. Live interviews are also harder to game and generate a stronger, more predictive signal. 

High Candidate Drop-Off During Interviews

Our data shows that only 61% of U.S. candidates complete their technical interview after receiving an invitation. High drop-off is a signal that something in the interview process is creating a lot of friction. 

Common causes include:

• Too many stages
• Take-home assignments that are time-consuming
• Poor communication throughout the process
• Interviewers who show up unprepared

Recommended Strategies

• Diagnose where drop-off happens. Track completion and drop‑off rates at each stage of the interview process and survey candidates who opt out. This reveals what is causing candidates to drop off.
• Fix bloated or misaligned assessments. Only 22% of organizations in the U.S. still use online code tests and take-home projects that don’t include human interviewers. Companies are increasingly moving away from this format and adopting live interviews that combine human interviewers with AI tools, as human-led and AI-enabled interviews are more predictive. 
• Shorten timelines and improve communication. Setting clear SLAs for scheduling and feedback and communicating expectations up front are now table stakes. Partnering with a company like Karat can help maintain momentum with candidates even when your internal bandwidth is constrained. Karat customers see a much higher completion rate compared to the industry average. 

Inability to Verify Contractor/Vendor Quality

As FinServ organizations increasingly rely on contractors and IT service providers to augment engineering teams, evaluating external talent has become just as important as evaluating full-time candidates. 

The challenge is that hiring leaders often have limited visibility into how contractors are assessed. This can create uncertainty around technical capabilities, increase onboarding time, and introduce risk. 

Additionally, the hiring bar that’s applied to full-time employees often isn’t applied to contractors. In fact, we’ve found that the majority of contractors fall below the Tier-1 bank talent bar. 

Recommended Strategies

• Introduce a single, standardized bar across vendors. Define a clear technical bar and require that all contractors and vendor placements pass a common assessment, regardless of which provider they come from. 
• Centralize interviews for internal and external hires. Routing all candidates, whether they’re full-time, part-time, or a contractor, through a centralized technical interview allows hiring managers to see the same kind of scores for every engineer.

FAQ: Engineering Hiring Challenges in Financial Services

What is the biggest engineering hiring challenge in financial services?

According to Karat’s analysis of 303 financial services technology leaders, inconsistent interview quality was the most commonly cited challenge.

Why are financial services firms struggling to hire engineers?

Financial services firms face competition from big tech and startups while also navigating AI adoption, legacy modernization, and increasing demand for specialized engineering talent.

How is AI affecting technical interviews?

AI is changing technical interviews by increasing concerns around AI-assisted cheating while also requiring companies to evaluate AI-native engineering skills such as debugging AI-generated code and prompt engineering.

Why are candidates dropping out of technical interviews?

Common causes include long interview processes, time-consuming take-home assignments, poor communication, and inconsistent interview experiences.

Are take-home coding assignments still effective?

Many companies are moving away from take-home coding assignments because they create candidate friction and provide weaker hiring signals compared to structured live interviews.

From Hiring Challenges to Hiring Confidence

Evaluating candidates consistently, efficiently, and at scale remains a huge challenge. Inconsistent interviews, overloaded engineering teams, AI integrity concerns, and friction‑heavy assessments are all eroding hiring confidence and driving away talent. 

If you’re facing any of these challenges, it’s time to rethink your technical interviews. Karat partners with FinServ firms to provide standardized, human-led, and AI-enabled interviews at scale. With Karat, you can generate a stronger hiring signal, shorten time-to-hire, and raise the bar for every engineer you bring on. Request a demo to learn more about how we can help you tackle your hiring pain points.