In the ever-evolving world of recruitment, artificial intelligence (AI) is taking centre stage.
The adoption of AI is widespread throughout the hiring process, predominantly in the sourcing and screening of candidates, but increasingly in their assessment, selection and onboarding.
AI is being harnessed in a way that can drive efficiencies through automation and simplification to complete the more mundane and administrative tasks, leaving recruiters and hiring managers able to spend more time with candidates where it best benefits them.
One important, and growing, application of AI is its ability to combat fraud in the hiring process.
As organisations strive to maintain the credibility and integrity of recruitment procedures, the use of AI could offer a proactive, efficient approach to identifying and combatting fraud.
Analysing application data
AI plays a crucial role in identifying fraud during recruitment by analysing vast data sets with speed and precision.
For example, in the banking sector, AI detects fraudulent credit and account applications. Similarly, the UK government employs AI to assess benefit applications for potential fraud. These systems learn from historical and real-time fraudulent data to predict and flag suspicious new claims.
Applying this to candidate hiring, AI can swiftly review CVs, application forms, and online profiles, identifying inconsistencies. Hiring managers can then receive alerts about discrepancies, such as falsified qualifications or misrepresented work experiences.
Applicant tracking systems (ATS) streamline screening processes and are one of the most powerful data sources to detect fraud. ATS can scan CVs and covering letters, extracting key information and identifying red flags. For instance, language analysis can reveal proficiency discrepancies or instances of plagiarism.
Scrutinising candidate interactions
Artificial intelligence tools can contribute significantly to fraud detection through scrutiny of a candidate’s interactions and even via responses made during interviews and assessments.
In a similar way to how machine learning can help identify irregularities in application data, it can also learn how to analyse communications.
AI can track geolocation data across multiple interactions with an applicant. By analysing IP addresses, timestamps, and claimed locations over time, AI can look for consistency and detect any discrepancies compared with the candidate’s stated location. This ongoing monitoring helps identify potential fraud or misrepresentation throughout the hiring process.
Furthermore, and potentially a step into the realms of debatable ethics, is the ability for facial recognition technology to be integrated into virtual interview platforms to detect microexpressions that may indicate deceit or discomfort, and to provide objective behavioural insights. In the same way, voice analysis can discern fluctuations in tone, pitch, and speech patterns.
AI also facilitates the implementation of predictive analytics models to identify patterns and trends associated with fraudulent recruitment activities. The analysis of historical data, including previous cases of fraud, can provide predictive indicators and risk factors, leading to the identification of high-risk candidates. This proactive approach allows organisations to allocate resources effectively and prioritise thorough vetting procedures for candidates who exhibit fraud risk profiles.
Document verification
Finally, and probably most widely adopted into hiring today, is AI’s ability to analyse the documentation provided by a candidate and to verify its authenticity.
In hiring, vetting and compliance checks are imperative to preventing a fraudulent hire. Organisations may be concerned with compliance documents such as passport copies, proof of address, or other identity documents, or in evidence of qualifications for those roles which require professional credentials.
AI ensures document authenticity through linguistic analysis, identity verification, and pixel-level scrutiny. It combines language patterns, cross-references data, and detects signs of forgery, bolstering trust and security in a variety of contexts.
Conclusion
AI in the prevention of fraud in hiring holds promise, and numerous industries have already embraced its potential.
However, as we expand the role of AI, we must prioritise fairness, equality, and diversity particularly when dealing with a candidate’s access to employment.
While using AI technology to flag risks will become essential, final decisions should remain independently conducted by humans to ensure accuracy and fairness.
Lawful, ethical and transparent AI deployment is crucial, and continuous review and feedback are vital for ongoing development.
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