2026 July - TLIX Deep Dive

Recruiters focused on hiring tech talent are most definitely feeling the pain caused by fraudulent candidates.

This was the first topic discussed during our recent TLIX: Talent Leader Information Exchange Deep Dive Connect which focused on Technical/AI Talent Pool Recruiting. Participants shared current strategies for identifying fake candidates and everyone is doing their best to exclude them from consideration as early as possible in the recruiting process. Members with remote roles are experiencing the highest fraud volume. Several members shared tools they’re experimenting with to identify fraudulent resumes and authenticate candidates in first-round interviews. But recruiting teams’ ability to secure approval from internal legal and operations teams, plus the time it takes to train people on the new tech, still takes significantly longer than the pace of candidate innovation. We also discussed the issue of fraudulent company emails and LinkedIn profiles being used to dupe candidates into thinking they’re being considered for jobs or even hired, but so far, no solutions have surfaced.

Next, the discussion focused on preserving a positive candidate experience as processes become automated. Nicely, none of our members have received negative feedback from candidates yet. One member shared that they often need to tell candidates that their message was truly from a human and not a bot. She shared that this has been highly appreciated by candidates. Given that we’re all experiencing the mass use of AI throughout our daily lives now, identifying which candidate interactions benefit most from ‘white glove’ recruiter engagement is starting to show the value proposition promised from adopting AI for less strategic candidate touchpoints.

Finally, the group talked about the pro’s and con’s of tech candidates using AI during the interview process and technical screens. Given that many of the jobs they’re interviewing for will require the use of AI, many companies are including or requiring AI usage during technical interviews. They’ve also redesigned interview questions to be more complex, ambiguous, and realistic — challenges requiring genuine engineering judgment even with AI assistance. Participants shared tools they’re using to conduct live coding assessments, and we all agreed that the focus has shifted away from getting the right answer and more toward assessing how candidates think, whether they can prompt strategically, detecting when AI’s wrong, and refining AI output into production-quality code.