Human Resource Information Systems - AI Modules

AI helps recruiters to Source, Screen, Shortlist

Recruiters have always leveraged technology in order to make their work easier, faster, and better. . If you had to ask Talent Acquisition Leaders, 52% of them consider the hardest part of recruitment to be the screening of potential candidates from large applicant pools.

How you ask?

Recruiters have always leveraged technology in order to make their work easier, faster, and better. If you had to ask Talent Acquisition Leaders, 52% of them consider the hardest part of recruitment to be the screening of potential candidates from large applicant pools. AI aids a recruiter’s ability to find top talent by intelligently automating the workflow. The application of artificial intelligence to the function of recruitment is known as AI for Recruiting. It includes problem-solving abilities or learning methods that computers can mimic in order to auto- screen candidates/conduct analysis/identify potential bias, etc. Screening, sourcing, and assessments – that depend on distinct inputs & outputs – are on the verge of becoming automated. So, we have for you a definitive guide to help you understand how AI helps automate your workflow.

Application of AI Innovations

Intelligent Screening Software: Automate Resume Screening

An intelligent screening software uses AI to learn from the existing resume database the success ratio of candidates on the basis of their performance, tenure, and turnover rates. It uses the knowledge to integrate existing Applicant Tracking System (ATS), without disrupting workflow all the while requiring minimal IT support.

How it helps

  • It learns the experiences & skills of existing employees.
  • Applies the knowledge to new applicants; enriches the resumes through public data sources such as their social media profiles and prior employer data.
  • Now, it ranks and grades them and shortlists the strongest candidates from the pool.

Digitised Interviews: Assess candidate capabilities

Though online interviews have now existed for a while, AI is a step ahead in the process.

How it helps

  • Technologies use AI to assess a candidate’s word choices, speech patterns, facial expressions, etc.
  • It uses the knowledge to assess if the candidate fits in with his role in the organization or is a mismatch with the company culture.

Recruiter Chatbots: Engage candidates real-time

58% of job seekers maintain negative impression of a company if they don’t hear back from them, while 67% get a positive impression if they receive consistent updates through the application process. Currently in testing stage, recruiter chatbots can provide real-time interaction and address the issue.

How it helps

  • It will ask candidates questions pertaining to job requirements.
  • Provide candidates feedback, updates, and next step suggestions.
  • Enhance candidate experience.

PROS of AI in Recruitment

Save on Time: Automate your high-volume tasks

Problem

  • Manual screening is a time-consuming process that eats up about 23 hours of a recruiter’s time for every single hire they make. 75-88% of all resumes received tend to be unqualified for the post applied and one has to screen through the multitude of resumes to shortlist candidates.
  • According to talent acquisition leaders, hiring volumes though are supposed to increase over the next few years (56%), without an equivalent increase in the recruiting teams which are expected to remain the same size or even contract (66%). Thus, recruiters are expected to do more with less.

Solution

  • AI automates repetitive tasks – thus cutting on time required previously. This includes tasks such as screening resumes, scheduling interviews, meeting candidates, etc.
  • AI-powered technologies are designed to seamlessly integrate with a company’s present recruiting attack without disrupting the workflow.
  • Recruiting through automation helps reduce time-to-hire. You no longer run the risk of losing the best talent to faster moving competitors.

AI Benefits

  • Save 23 hours/hire
  • Undisrupted workflow

Improve Quality of Hire: Adopt Standardised Job Matching

Problem

  • Over the years, Quality of Hire has become the top Key Performance Indicator, yet recruiters were often unable to measure the same once candidates got hired.

Solution

  • AI automates the collection and analysis of data by HR – thus improving the Quality of Hire. It has the ability to standardize the matching by comparing the experience, knowledge, and skills a candidate possesses with that of the requirements of the job.
  • Improved job matching leads to happier employees – more productive in their roles and less likely to turnover.

AI Benefits

  • Cost reduction/screen by 75%
  • 35% decrease in turnover
  • 20% increase in performance
  • 4% increase in revenue/employee
Standardised Job Matching is the ability to more objectively assess a candidate’s ability and skills without letting inherent biases creep in throughout the sourcing and selection process; AI makes this possible.

PROS of AI in Recruitment

Data-Driven

To accurately learn how to mimic human intelligence, AI requires a sea of data. Example: For an AI that used Machine Learning to screen resumes as accurately as a human recruiter, it would need at least several hundreds of resumes for specific roles before it can get it right.

Mimicking Human Biases

Though reducing unconscious bias in the recruiting process, AI can pick up on patterns of bias previously in use and replicate the same.

Technology Skeptics

HR professionals have already been bombarded with a myriad of trends that appear and reappear in the industry. It is not unnatural for HR professionals to be a sceptic of software that can make their role redundant. It is important for them to view the software as a tool for use and not their replacement.

In Conclusion

AI for recruiting allows for streamlining or automating repetitive high-volume tasks of the recruiting workflow with the help of artificial intelligence. It saves on recruiters’ time, improves the quality of hire through innovations such as intelligent screening software, chatbots, and digitized interviews. In spite of major challenges in the form of data requirements, potential to mimic human biases, and resistance to new technologies by the HR staff, AI is here to stay and augment the role of a recruiter – help them become more pro-active in hiring, assessing, and retaining capable candidates while also improving company culture via improved communication and use of KPIs such as quality of hire.

AI Face Recognition Attendance System

Smart Attendance using real-time face recognition is the latest innovation in the world of Automatic Face Recognition – a real-world solution that uses face biometrics-based on high-definition video and associated technologies. The system could be used to monitor attendance of all employees working in your organization. Machine Learning has made it possible for numerous algorithms in improving the performance of face recognition systems. Easy conversion of facial metrics helps reflect attendance on the database.

Existing Recognition Systems

Fingerprint-based Recognition System

Problem

  • Portable fingerprint device is needed; often time-consuming and expensive too.
  • Does not take into consideration physical changes or aberrations received from accidents or development changes.
  • Can lead to false rejections or false acceptance.

Voice Authentication

Problem

  • Not as accurate as facial recognition
  • Requires liveness detection to distinguish sample between live speakers and recordings

Iris based Recognition System

Problem

  • Requires IR light source, sensor, with minimal visible light.
  • Requires close proximity to the camera
  • Possesses less value for any future criminal investigation

Radio Frequency Identification based Recognition System (RFID)

Problem

  • Possible fraudulent access, misusing others’ cards, RFID Raeder Collision/ Tag Collision
  • Different developmental standards of different companies making the cards.
  • Delays and inconvenience from similar electromagnetic spectrum in use (Wi-Fi network/cell phones)

How Face Recognition Works

Facial recognition works by means of using biometrics that map facial features from a photograph or video to compare the information with a database of known faces- thus find a match. The basic steps include:

First Step

Employee pictures are captured from photographs or video, in solitary or from amongst a crowd. Usually, a profile image is used.

Thirst Step

The Facial Signature – a mathematical formula – is compared with an existing database of all known faces in the directory.

Second Step

The AI Face Recognition Attendance System reads the entire geometry of the face – factoring in key aspects such as distance between the eyes or distance from forehead to chin.

Last Step

A match is determined when the faceprint used matches an image withing the facial recognition system database.

AI Face Recognition enabled Attendance System

Make the most of an AI face recognition system that is equipped with cloud-based softwares to help capture time-in and time-out of all employees through Face Recognition Readers. The attendance data then syncs with the Time Attendance Software. Attendance data can often be linked to payroll softwares or exported to other document managers like Excel, CSV, etc. Companies can opt to choose from a variety of options to capture the employees’ Attendance Data through facial recognition:

Employees can now use their own smartphones or tablet apps to clock in and out from any location as the system uses GPS information to match each employee with their designated place of work. AI tools are now built-in mobile attendance apps to verify employee face – to deter fake clocking.

Features : Face recognition software, Accurate GPS capture ability, GPS lock with clock-in allowed at specified location, Available on all your iOS and Android devices, Real-time monitoring of every employee and Past location history tracker

Biometric Machines

Mobile App

Tablet App

What iStudio Gives you

Our HRIS software is equipped with updated tools to provide you with:

Daily Attendance Report

Your company/organization will be provided with all staff details of time-in and time-out, along with location and live photo on a day-to-day basis

Individual Attendance Report

Your company/organization will be provided with every individual attendance report of time-in and time-out of a whole month, along with location and live photo on a day-to-day basis

Map View

You can now view all your employees actually clocking in, with the help of the Map View. An AI Face Recognition Attendance system with multi-platform versatility will enable Live Monitoring and GPS Tracking of all your employees.

Predict Attrition using AI

Give a rest to the never-ending cycle you have to endure any time an employee leaves – advertise, interview, screen, and eventually hire. Managers are stressed, HR is overworked, and you have at your hand a long training session for the new hire to understand how things work – even more, time training and onboarding. Finally, the new member must get acclimatised to the company culture, understand his job role, and function effectively to be able to match up to his predecessor’s productivity. All the while, as an HR manager, you have the added responsibility of managing the disengagement in the team. A million questions remain to be answered before they can understand this shuffle – why their colleague left, if they are at risk of losing the position too, how the new person will sync in, and much more. A lot of money and even more time is lost for you and your company. This is the cost of attrition – the money spent to replace an employee. On average, the average cost to replace an employee tends to go up to 20% of the employee’s average salary. But now AI has come to your rescue. Adapted as a revolutionary tool for task prediction, it helps you avoid the high cost of attrition and high attrition rates – which combine to form one of the most expensive problems for companies across all sectors.

Challenges & Solution

In the road to predict attrition using artificial intelligence and data science, a couple of challenges first need to be overcome.

Challenges

Collection of feature-rich attrition data – Companies might fumble to collect enough data about their employee’s history of previous attrition

Being able to afford the research – The financial investment necessary to hire quality data scientists and domain experts needed to collect the data and understand attrition factors might not be possible for some MSMEs or start-ups.

Lack of Time – Without automated tools to aid you, manual collection of data necessitates interviews, forms, questionnaires that only increase the cycle time of prediction, are time-consuming and affect accuracy.

Plus, not all employees will be so forthcoming with their data – leads to inconsistent data sets and impact overall data volume.

Solution

AI Softwares are keen to provide you with performance data on an ongoing basis, which coupled with employee characteristics would form a robust attrition predicting model. Performance data contains loads of features such as employee sentiment, employee workload, etc. which helps understand attrition as also engagement & burnout levels.
With Small Data Amount – Ensemble models like Random Forest and XGBoost work better with smaller, more diverse data to give correct predictions, albeit harder to interpret.
With Large Data Amount – Deep Learning Models, using large data sets give results that you can easily interpret.

How AI predicts Attrition

Post collection, it’s time for Data Analysis via one of the two:

AI Tools

The ease availability of AI tools makes it convenient for anyone to build a predictive model using these AI tools like Ludwig, H20,Google Cloud AutoML etc., without having had to code

AI-powered HR platforms

HR tools with in-built AI collect data from employee performance and constantly analyse the same using the AI engine. This results in predictions whenever the system achieves high enough accuracy.
First, you select your problem statement which then gets used as a variable by the system. If your problem statement is “whether an employee is going to leave the company’, the platform collects similar data of all employees to predict future attrition.

In conclusion

Robust tools built on AI predictive models are extremely useful in accurately predicting Attrition Rates through feature-rich data. Several companies are giving it a try to help reduce their attrition rates and cost involved. This rapidly evolving technology surely has a lot in store for the future.