AI-Based Resume Screening: Does Your Company Need It?
November 30, 2022
Recruiters are almost always evaluated on two metrics regarding KPIs: hiring time and hiring quality.
It’s time to find the answer to improving the hiring and selection process quickly. At Bragona Technologies, the answer is artificial intelligence (AI).
Bragona Technologies has implemented AI-based mechanisms in developing HR and recruiting solutions and can confidently say that AI is the real game changer for recruiters.
Major resume screening problems
When we talk about resume screening, we are referring to evaluating information about a candidate, such as education, skills, experience, and personality traits, to suggest whether an applicant is suitable for a particular position.
As one of the most critical steps in the hiring process, resume screening occurs after a job offer is posted and before an interview. If you think this process is pretty straightforward, it’s not. There are three main problems associated with resume screening:
Studies show that human resources professionals attract an average of z300 applications per specific position. Only 15% of them meet the requirements of the position.
Selecting many candidates is a monotonous and time-consuming part of the hiring process. Therefore, recruiters likely feel bored, make mistakes, miss information, or screen out qualified candidates based on unconscious bias.
Quality of hiring
Hiring quality is characterized by the value a new candidate can bring to your company in the long run.
There are many ways to measure hiring quality: new candidate performance, overall performance, 360-degree feedback, etc.
That’s fine when evaluating an employee, but how do you ensure at the selection stage that the new candidate will bring value to the company? That’s the million-dollar question. Some tools can help recruiters pre-screen candidates, but they only sometimes guarantee a person’s future success. For example, applicant Tracking Systems (ATS) needs to provide an early assessment of hiring quality, which prevents companies from improving their selection workflows.
If we know that, on average, there are about 250 applications per job opening, the selection process can take up to 23 hours. And that’s only for one employee. If a recruiter has multiple open positions, the process will need to be faster to be efficient.
70% of candidates admit to canceling an application when the process takes too long, so imagine how many perfect candidates a company could lose.
How does AI resume screening change recruiting for the better?
Artificial intelligence technology to screen resumes addresses the significant problems we discussed above, making this process more intelligent and efficient. For this purpose, companies most often use solutions called resume selection tools or resume parsers.
What is an AI-based resume parser?
Recruiters typically receive candidate resumes in PDF or Word formats. It’s easy to read the information presented this way, but it’s not easy to manage if the recruiter receives dozens of new resumes a day.
This is where a resume parsing tool comes to the rescue. Resume parsing solutions free the recruiter from the time-consuming manual processing of resumes.
The resume parser is an artificial intelligence tool that allows you to recognize and extract the necessary information from resumes in various formats and present this data in an organized and understandable way.
How does the resume parsing algorithm work?
The process begins by loading all relevant resumes of job seekers into the parsing tool. This can be done manually or automatically if the solution supports this functionality. The parser then goes through each document, extracting data relevant to both the recruiter’s needs and applications, including information about experience, skills, education, qualifications, and so on.
During this process, the resume parsing software screens out candidates with insufficient or missing information. It provides recruiters with a list of suitable candidates without having to spend long hours manually checking each resume.
In general, there are several resume parser methods on the market today:
A keyword-based resume parser detects specific text keywords, phrases, and patterns. This technique is the easiest, though less accurate. The accuracy rate is about 70% because of the ambiguity of keywords; the algorithm cannot always catch the meaning of a word and may misinterpret it.
The grammar-based resume parser supports complete logic. It works based on a list of predefined grammar rules. The algorithm breaks down the textual information in each resume by combining certain words and phrases to capture the meaning of each sentence. The accuracy of grammar-based resume parsers is close to 90%. In most cases, parsers can easily understand the different meanings of words and phrases, leading to more detailed results.
Statistical parsers are the most advanced technology. It applies numerical models to analyze information in summaries. In addition to distinguishing word meanings, it can recognize different structures, including addresses, timelines, etc. For a statistical parser to be accurate, it must be pre-trained on the data it is supposed to process.
The last smart resume parsing module we put in our projects was based on integration with Textkernel, a third-party resume parsing tool. Here’s what it looks like in practice – recruiters upload a list of resumes from job seekers to the system. Then, through integration with an external tool and predefined mechanisms, recruiters get structured candidate profiles inside the system. As a result, talent searchers can easily find suitable candidates in seconds using advanced search or an intelligent auto-selection algorithm.
AI can process vast amounts of data quickly, making it the most valuable tool for high-volume recruitment processes.
Intelligent resume selection improves hiring quality because it reduces human error and unconscious bias and can make predictions. Ideal, an intelligent talent analysis system, claims that organizations using AI-enabled automated recruiting have seen a 20% increase in productivity and a 35% decrease in turnover.
Automated screening saves the recruiter time, freeing up more time and resources for other valuable activities such as communicating with top candidates, evaluating, and so on.
The main problems in using resume parsers and how to solve them
Now that we’ve seen the benefits that an artificial intelligence-based parser can provide, let’s find out what difficulties you might encounter when using a resume parser to select resumes.
Automate your resume screening process with Bragona Technologies
The team at Bragona Technologies has years of experience in developing recruiting software. We have created various HR software for staffing agencies, HR management teams, and innovative startups.
In addition to solving pressing recruiting problems such as candidate tracking, interview scheduling, candidate evaluation, HR analytics, etc., we have automated the selection process.
Our team has also developed a skills verification algorithm that automatically clears your database of parsing errors. Check out our HR software development services to learn more about what we can offer.