From Resumes to Algorithms: The Role of automation in Screening Candidates

Candidate screening is one of the most important stages of the recruitment process. Nevertheless, it has been the proverbial Achilles heel for most recruiters. Take a look at these numbers, for instance. Out of 250 resumes received for an open position, nearly 220 are from unqualified candidates, which is a whopping 88%. Recruiters need to spend significant hours just filtering out qualified candidates before they can proceed with further hiring processes. Even though Applicant Tracking Systems (ATS) are used extensively, they still do not solve other problems associated with manual or semi-automated screening. For instance, quality of hire is not a guarantee even if you have a qualified candidate. Sometimes, resume formats may be rejected by the ATS since they do not “fit in” the generic format expected by the tool. Unconscious hiring bias can come into play when human recruiters screen resumes manually. Apart from that, much of manual screening involves recruiter’s expertise and experience, and making sure that hiring criteria are met.

Clearly, manual screening of candidates may result in qualified candidates getting filtered out due to inadvertent errors by humans. Automated candidate screening with artificial intelligence (AI) and machine learning (ML) can transform candidate screening in several ways.

How AI powers candidate screening

Automated screening tools that leverage AI or ML offer tremendous improvements to the candidate screening process.

• Parsing and keyword matching:

  Resume parsers can extract information from resumes and organise them into a structured format that is more useful. Resume parsers leverage deep learning and ML algorithms and natural language processing (NLP) to break down unstructured resumes into useful structured data. For example, resume parsers would be able to extract personal information, work experience, education and certifications from resumes. Unlike traditional parsing that was rule-based, resume parsers that are based on AI or ML are flexible and scalable. They can handle a wide variety of formats and are useful for high-volume recruitment. They can even understand the semantics, where they are able to determine the relevance of experience or skill for a specific job description.
  Similarly, AI-based keyword matching is contextual, which is unlike traditional keyword matching in ATS software. The ability to process natural language means that keyword matching is done in the context of candidate experience and the job posting, resulting in far more accurate and relevant results.

• Automated scheduling and workflows:

  Automated interview scheduling can save recruiters precious hours during the initial screening process. With the AI tool checking on interviewer and candidate dates and setting the interview at a mutually convenient time, recruiters are free to just do a final check and confirm the interview. AI-based software can scan thousands of databases and repositories for potential candidates who have been passive so far. Automated messages can be sent to such candidates, and they can further be engaged via chatbots or email.

• Talent assessments:

  With skill-based hiring taking the lead, talent assessments have become the de facto standard to evaluate candidate proficiency. AI-powered skills and behavioural assessments can save companies resources, time and money while giving candidates the flexibility to take the assessments at a time and place of their choice. With automated identity detection and online proctoring, there is the assurance of no unfair practices. Test scores can be calculated and sent to applicant tracking systems (ATS) in the recruitment workflow, thus ensuring that there is minimal manual intervention.

• Virtual interviews:

  Virtual interviews became common in post-pandemic times. They continue to be used, especially for initial candidate screening. AI adds a new dimension to virtual interviews. AI-based software can detect facial expressions, body language and speech patterns that can be further used to make data-driven decisions regarding candidate suitability for a profile.
  

There are several advantages to using AI in candidate screening. AI-based screening encourages diversity hiring since there is minimal human bias during the hiring process. With automated scheduling and virtual interviews, candidates can be easily accessed and assessed, which in turn leads to improved candidate engagement and a positive experience. By automating repetitive tasks, recruiters experience increased productivity and efficiency and can focus on other activities that require human intervention. The use of AI in candidate screening leads to data-driven decision-making rather than relying just on gut instinct. Predictive analytics can help determine the likelihood of candidate success, which will result in improved and more efficient hiring.

AI-driven candidate screening has the potential to transform recruitment. Companies must be mindful of ethical AI practices, maintain transparency with candidates and adhere to data privacy regulations. Companies also need to invest in continuous improvement to enhance the effectiveness of AI-driven automation and fine-tune the algorithms. By striking the right balance between automation and human oversight, companies can undoubtedly achieve excellence in talent acquisition.