How Artificial Intelligence And Machine Learning Can Address Recruitment Frauds

Any investment in an AI system is always worthwhile because it saves time, filters out fraud, and provides candidates with trustworthy opportunities without being scammed.

There are numerous machine learning algorithms and classifiers that can help distinguish between real and fake job postings.

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Technology has advanced manifold in the last decade. The use of technology in almost all domains has seemingly made us increasingly dependent on it. Consequently, Artificial Intelligence (AI) and Machine Learning (ML) have significantly increased in popularity and are being used in numerous fields. These technologies have simplified our work, allowing us to simultaneously perform many tasks. Regardless of these technological advancements, there has been a significant increase in fraudulent activities. 

AI and ML are used in the modern recruiting process to easily filter data and recommend the most qualified candidates for the job. However, many recruitment scams are also prevalent in the industry. The victims of these scams are usually job seekers who are desperate for work. People are usually duped into giving up their money in exchange for their data and personal information when they work for a fraudulent company. This information can then be used for other fraudulent online activities.

AI and ML can enable us to get rid of recruitment fraud. Let us explore and learn the methods to grant us immunity against such fraud.

How does AI address recruitment fraud?

Most businesses now post their job openings online, where job seekers can easily apply. However, many fraudulent companies are using this medium to defraud candidates by posting fake jobs. There are numerous machine learning algorithms and classifiers that can help distinguish between real and fake job postings. These classifiers and algorithms are intended to predict unknown cases and correctly identify a fake job.

How do classifiers help in the detection of fake jobs?

The classifiers use the training data and input the variables into the target class. This data then helps determine whether the target class is fake or legitimate. There are two classifiers: 

Single Classifier-Based Prediction  

These classifiers predict unknown tests and are very helpful in detecting fake jobs. This category uses the classifiers below:

Naive Bayes

This classifier employs the Bayes theorem of conditional probability. This method's estimates are accurate, and the decisions it makes are promising and effective. It accounts for some missing information the availability of which could provide an even more accurate assumption, but its accuracy is not dependent on features.

K-Nearest Neighbor

This classification technique is very effective because it uses the closest data from the training examples. The classifier determines the class and focuses on the k number of objects as the nearest object. It is important to determine an appropriate value for k since the whole classifier technique depends on it. 

Decision Tree Classifier

As the name suggests, this technique uses a tree-like structure to do the classification and obtain the result. This tree structure consists of branches, leaf nodes, and root nodes. The target classes are denoted by the leaf node and the other non-leaf nodes are used to indicate tests. This method is used for spam filtering in emails. 

Ensemble Approach-Based Classifier

This classifier makes it possible to run different classifier algorithms together and obtain an accurate result. There are various classifier techniques under ensemble-based classifiers, such as: 

Random Forest

This method is best for the classification-based problem, it goes through the different tree-like classifiers. These classifiers are applied to the data set where each data set is acquired, the method selects the best one out of all and chooses it as the appropriate one.

Boosting

This classifier method uses weak data sets and combines them to form a strong classifier. This process selects random data from the data set and applies the classifier method to train it. This data set overcomes all the problems of its predecessor.

Boosting method is the best to solve spam filters; adaptive and gradient boosting are some boosting techniques used to improve the output and make it stronger.

Use of AI to minimise fraud

Fraud in recruitment has adverse effects on recruiting organisations and job seekers alike. Hence, AI will prove to be even more useful after further advancements. AI’s ability to accurately detect fraud is primarily owed to its ability to detect the patterns, actions, and habits in the transaction. It's not based on assumptions and prior experience only. There are numerous reasons for AI’s widespread adoption in the hr tech industry:

> It uses the behavior pattern of fraudulent activities and makes an algorithm to prevent such activities in the future.
> It can identify any suspicious transaction faster than humans and identify the location of the activity. 
> It helps detect social media verifications and performs qualification checks. 
> It enables us to find contradictory data and can also analyze facial expressions to detect fraud. 

Artificial intelligence is a tool that aids in the detection of fraud and scams. It employs the face detection technique, analyses the application, and collects data to create an algorithm capable of detecting fraud and saving countless candidates from being duped. It employs machine learning to detect any unusual activity and then compares it to other similar data. Any investment in an AI system is always worthwhile because it saves time, filters out fraud, and provides candidates with trustworthy opportunities without being scammed. Job portals and companies seeking candidates should use AI detection techniques to remove all fraudulent job postings.

(The author is the Global Co-Founder and CEO of Hirect India.)

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