Claims:We claim:
1) The recruitment system through artificial intelligence comprising of;
a. Registering the candidate in the database of the website;
b. Storing of the data in the internal database of the website;
c. Analyzing massive amounts of data for precise evaluation through analyzing unit;
d. Conducting online Screening questions and skill questions to the candidates;
e. Validating the answers of the candidates;
f. answering queries about the selection process by enhancing the selection experience for candidates through chatbots; and
g. filtering the candidates through filtering followed by ranking of the candidates.
2) The system as claimed in claim 1, wherein the said system enables to utilize video interviews in the selection process for in-person interviews.
3) The system as claimed in claim 1, wherein the said system enables recruiters to offer immediate support and help through interactive chatbots that can answer queries about the selection process or about specific assessments.
, Description:FIELD OF THE INVENTION:
The present invention relates to recruitment systems. The present invention more particularly relates to a system for recruitment through artificial intelligence.
BACKGROUND OF THE INVENTION:
Resume screening is the process of determining whether a candidate is qualified for a role based his or her education, experience, and other information captured on their resume. In a nutshell, it’s a form of pattern matching between a job’s requirements and the qualifications of a candidate based on their resume. The goal of screening resumes is to decide whether to move a candidate forward – usually onto an interview – or to reject them. Screening resumes usually involves a three-step process based on the role’s minimum and preferred qualifications. Both types of qualifications should be related to on-the-job performance and are ideally captured in the job description. These qualifications can include: work experience; education; skills and knowledge; personality traits and competencies.
Resume screening is still the most time-consuming part of recruiting: Screening resumes and shortlisting candidates to interview is estimated to take 23 hours of a recruiter’s time for a single hire. When a job opening receives 250 resumes on average and 75% to 88% of them are unqualified, it’s no wonder the majority of talent acquisition leaders still find the hardest part of recruitment is screening the right candidates from a large applicant pool.
Resumes are screened based on minimum qualifications. Minimum qualifications are the mandatory qualifications that a candidate must meet to be able to do the job. A simple example of a minimum qualification is whether the candidate is legally able to work in the country. These types of qualifications are often considered knockouts because either the candidate has it and can move forward or they don’t and gets screened out of the process. Candidates that meet the minimum qualifications move onto the second step of screening resumes. Second type of screening of candidates is based on preferred qualifications of the candidates. Preferred qualifications are non-mandatory characteristics that would make someone a stronger candidate for the job. A common example of a preferred qualification is whether the candidate has prior related work experience. Candidates that meet both the minimum and preferred qualifications move onto the shortlisting step of resume screening. Third type of screening is through Shortlisting candidates based on minimum and preferred qualifications. It helps to decide which candidates gets shortlisted for the interview phase depends on your recruiting needs.
For high volume recruitment, generally all candidates that meet the minimum qualifications move forward to the interview process. For low volume recruitment, generally only the top few candidates that meet both the minimum and preferred qualifications receive an interview. For every 100 candidates you screen, you need to shortlist 12 of them to interview, two of them will receive an offer, and one candidate will accept to result in one successful hire. Both quality of hire and time to fill are influenced by resume screening process. Recruiters face increased pressure to show quality of hire but lack tools to link their resume screening to post-hire metrics.
Advances in recruitment technology have added automation to candidate sourcing with recruitment marketing and to candidate interviewing with video interviews. However, technological innovations to address the biggest pain point in recruiting—screening resumes—has been frustratingly absent until recently. The time spent on screening resumes often takes up the largest portion of time-to-fill.
So there is a need for increasing quality of hire by reducing false positives through keyword searching and to reduce false negatives because candidates with good qualifications no longer slip through the keyword filters. The present invention learns how to predict quality of hire to analyze historical hiring decisions as well as learn from current hiring decisions. The present invention automates resume screening by using AI to learn from historical hiring decisions to improve quality of hire and reduce employee turnover.
The present invention helps in saving recruiters time by automating high-volume tasks. The present invention helps in hiring volume of the candidates and make recruiters more efficient with the help of artificial intelligence. The present invention helps in automating time-consuming, repetitive tasks such as screening resumes or scheduling interviews with candidates. The present invention helps in improving quality of hire lies in its ability to use data to standardize the matching between candidates’ experience, knowledge, and skills and the requirements of the job.
OBJECTS OF THE INVENTION:
An object of the present invention is to provide a recruitment system through artificial intelligence.
Yet another object of the invention is to help in improving quality of hire lies in its ability to use data to standardize the matching between candidates’ experience, knowledge, and skills and the requirements of the job.
Yet another object of the present invention is to automate time-consuming, repetitive tasks such as screening resumes or scheduling interviews with candidates.
Yet another object of the present invention is to store browsing habits can generate valuable data for email campaigns and to increase faster inventory turnover.
SUMMARY OF THE INVENTION:
Accordingly, a system for recruitment through artificial intelligence is disclosed. The recruitment system through artificial intelligence comprising of; Registering the candidate in the database of the website; Storing of the data in the internal database of the website; Analyzing massive amounts of data for precise evaluation through analyzing unit ;Conducting online Screening questions and skill questions to the candidates; Validating the answers of the candidates; answering queries about the selection process by enhancing the selection experience for candidates through chatbots; and filtering the candidates through filtering followed by ranking of the candidates.
DESCRIPTION OF THE DRAWINGS:
Fig 1 is the recruitment system through artificial intelligence.
DETAILED DESCRIPTION OF THE INVENTION WITH RESPECT TO DRAWINGS:
The present invention is applied in employee recruiting by using information extraction techniques that automate the process of résumé identification and extraction of relevant information. Information extraction refers to a process where knowledge and information is acquired by skimming a text. AI is also capable of recognizing personality and acquiring personality models for the by observing text and conversation through language cues. Personality traits affect many aspects of task-related individual behavior such as the general job performance, sales ability and academic ability and motivation. AI is able to interpret an applicant’s personality and compatibility to the job from an application letter. AI can analyses massive amounts of data, much more than any human could accomplish. More candidate data can now be precisely evaluated and can help make better selection decisions.
AI enables recruiters and talent teams to conduct consistent and objective assessments of job-relevant data at a much earlier stage in the selection process. AI enables to utilise video interviews far earlier in the selection process than is typically the case with in-person interviews. AI’s objectivity will help recruiters to eliminate conscious and unconscious bias in the selection process. AI can make selection decisions difficult to justify, because it’s almost impossible to understand how these algorithms reach the conclusions they deliver. If your selection decisions cannot be easily explained, they could be challenged by applicants in a court of law. Allowing AI to continuously learn by ‘observing’ the best practice of human raters offers the best and most legally-defensible approach for assessment. AI can improve the candidate experience in recruitment. It enables recruiters to offer immediate support and help, for example through interactive chatbots that can answer queries about the selection process or about specific assessments. AI can also optimize and enhance the selection experience for candidates. The present invention allows responses in personality questionnaires and situational judgement tests. AI can improve the whole selection experience for jobseekers by speeding up decision times, reducing bias, enhancing the assessments and making the process more candidate-centric.
In one embodiment, the recruitment system through artificial intelligence comprising of;
a. Registering the candidate in the database of the website;
b. Storing of the data in the internal database of the website;
c. Analyzing massive amounts of data for precise evaluation ;
d. Conducting online Screening questions and skill questions to the candidates;
a. Validating the answers of the candidates;
b. utilising video interviews in the selection process;
c. answering queries about the selection process by enhancing the selection experience for candidates through chatbots; and
d. filtering the candidates through filtering followed by ranking of the candidates.