Key Insights
- AI enables candidates to bypass screening, raising fraud and insider risk.
- CFIs face higher exposure due to limited HR and reliance on automation.
- Mitigate with layered hiring: verify identity, test skills, and monitor continuously.
Jackie Morris demonstrated the impact of artificial intelligence (AI) on hiring when she used it to help her land two separate jobs, in one case navigating three rounds of virtual interviews and beating out 261 other candidates. That’s because Jackie Morris doesn’t really exist. She is a phony persona created by cybersecurity advisor Jake Moore, who spent $50 on software to transform his appearance and voice into an Asian woman with a purely fictional professional identity. With the assistance of AI, Moore not only created a phony resume, but a fake LinkedIn profile, Instagram profile and even a passport – all of which managed to circumvent both the AI screenings of employers as well online interviews with humans.
AI is changing the hiring process, but not always for the better. Most major businesses, including those within the financial industry, employ some sort of AI to filter resumes and identify the most promising candidates. But AI is far from foolproof, especially when applicants employ it themselves. Failing to understand the weaknesses of AI assisted hiring is a strategic risk that community financial institutions (CFIs) can’t afford to take. For CFIs, the biggest concerns are operational, compliance, and reputational risk.
AI Hiring Risks CFIs Can’t Afford to Ignore
There has been a lot of talk about how AI has helped companies streamline the hiring process, but the flip side is that it is making it more difficult for organizations to find qualified candidates that are truly a good fit.
Studies have found that AI screeners can lead to demographic bias and tend to show preference for candidates who best mimic the parameters of their AI models. Since generative AI is widely available, most job applicants now use it to help craft resumes with key words and phrases that can help them circumvent employers’ AI filters. This can result in strong candidates who don’t rely on AI being overlooked, while people lacking legitimate qualifications may wrongly appear to be the strongest candidates.
Not only can generative AI help people spiff up resumes, it can also be used to craft phony skills and previous experience, and even to assist people with virtual interviews. In fact, Gartner predicts that one in four job candidates' profiles will be partially or completely fake by 2028.
Virtual interviews have become commonplace, particularly for initial interview rounds. There are specific risks that come to play in this kind of interview environment. Some candidates are using AI software, such as Parakeet and Final Round AI, to help mask the fact that they are unqualified for a position. The program provides the candidate real-time answer assistance for interview questions that appears solely on their screen, which is how they are able to circumvent many AI detection programs.
The risks of selecting candidates who are the best at using AI to navigate the hiring process extend to an employer’s long-term strategy. Hiring people without adequate skills can erode a company’s talent pool and can lead to unnecessary turnover, can impact productivity, and can be costly. According to SHRM’s 2025 Benchmarking Report, the average cost of hiring a non-executive is currently $5,475, increasing to $36,000 for executives. Gallup puts the cost of replacing employees at up to two times a person’s annual salary. In more extreme cases, such as the phony Jackie Morris, cybercriminals can use AI to land jobs and evade detection long enough to get hired, begin receiving paychecks, computer equipment and internal access to their new employer that could make it easy for them to hack into sensitive information within the organization. A fraudulent or AI-assisted hire can quickly become an insider threat, gaining access to customer data, payment systems, or internal networks. In an environment where institutions are already managing rising fraud and cybersecurity risks, weak hiring verification processes can create a new and largely unmonitored entry point.
How to Spot Red Flags for Fake Candidates
In a generative AI world, CFIs need to be aware of red flags to look out for during the hiring process, which may lead to altering their hiring approach. Hiring managers should be acutely aware of the reality that much of the content in the resumes they receive may be fake and verification initiatives need to be mindful of the ease with which people can falsify references and previous experience.
Here are some helpful precautions CFIs should consider throughout the hiring process:
- Resumes that match your company's job descriptions too well should be questioned, as should any credentials that can’t be verified or generic experience that lacks details.
- Keep in mind that candidates' previous work experience with extremely small organizations without a visible internet presence, or jobs at organizations that are so big that can be hard to verify employment, have a high likelihood of being phony. Look for independent third-party sources that can verify and corroborate what is on a person’s resume.
- Make sure to look beyond the resume to a person’s online presence. Beware of social media profiles that were recently created, that match the name of another person online with a similar work profile, or that do not have clear profile pictures.
- Consider using third-party verification firms, particularly for candidates that are in different geographic locations.
- Invest in AI detection and smart screening tools that can help identify phony applicants.
- Run candidates past multiple interviewers to look for consistency.
- During virtual interviews, make sure candidates are visible and clear. If they refuse to turn on their camera or have unnatural or delayed responses, those are good signs that they may be fake candidates.
- AI assistance programs tend to be better with helping people respond to standard and predictable interview questions, so deviating from these types of questions by asking candidates things that require reasoning can make it easier to spot when such programs are being used.
Addressing these precautions requires more than heightened awareness. It calls for a more structured and layered approach to hiring. Financial institutions should consider setting clear expectations around acceptable AI use in the application process, while also being transparent about the controls in place to detect fraudulent behavior and the consequences for those who attempt to circumvent them. Incorporating multiple forms of evaluation, including skills-based assessments and in-person or proctored interviews where appropriate, can help validate candidate capabilities beyond what appears on a resume. Strengthening post-offer safeguards is equally important, with enhanced background checks, identity verification measures, and ongoing monitoring of access and system activity helping to detect inconsistencies that may not surface during initial screening.
AI assistance can reduce the time it takes hiring managers to sift through countless applications, but it can also introduce new vulnerabilities that institutions can no longer afford to overlook. As candidates become more adept at using AI to manipulate resumes, interviews, and even identities, traditional hiring signals are becoming less reliable. For CFIs, this shifts hiring from a purely talent acquisition function to a critical component of risk management. Institutions that fail to evolve their screening and verification practices risk not only making poor hiring decisions, but also creating a new entry point for fraud, data exposure, and insider threats.
