Blog

India’s gig economy is on a rapid rise. According to the NITI Aayog, 7.7 million workers were engaged in the gig economy in 2020-21 and the workforce is expected to expand to 23.5 million workers by 2029-30.

There has been an increase in demand for gig employees in the global capability centres of MNCs in the last six months—up by 20-25% from a year earlier—as they look for a balance of cost-effectiveness, scalability, and faster access to skills.

Given the fast-evolving gig economy, the stakes for hiring the right gig workers have never been higher. Companies rely heavily on background verification to ensure the safety and reliability of their workforce.

AI has become a game-changer in this arena, especially in enhancing background checks through swift analysis of vast datasets to identify discrepancies and validate credentials.

Unleashing AI: Scaling up court record checks

One of the groundbreaking applications of AI in the gig economy is conducting comprehensive court record checks. Hiring individuals with criminal records without proper vetting could expose a company to legal liability and waver confidence in the quality and integrity of the services provided. Traditional methods of background checks are often time-consuming, labour-intensive, and susceptible to human errors, especially when dealing with large volumes of data.

AI, however, transforms this process by introducing unprecedented efficiency and accuracy. What usually takes days or weeks can now be accomplished in hours without unnecessary delays.

Addressing bias for trustworthy checks

AI bias can result from several factors, including skewed/limited training data, cultural or regional misunderstandings, and flawed algorithms leading to unfair rejections or incorrect validations, undermining trust in the system.

Addressing these biases is crucial for maintaining the integrity and reliability of background verifications. This can be achieved via extensive training in AI models, geography-wise and use case-wise, specifically for the diverse Indian context. And we prioritise this aspect. Our systems are trained to manage the unique and region-specific name, address and other background verification checks required for gig workers.

Such extensive and unbiased training increases the accuracy and speed of background checks and helps companies seamlessly interpret the complexities of Indian names and addresses, which often include variances like middle names referring to fathers’ names or local dialect variations in addresses.

@media (max-width: 769px) {
.thumbnailWrapper{
width:6.62rem !important;
}
.alsoReadTitleImage{
min-width: 81px !important;
min-height: 81px !important;
}

.alsoReadMainTitleText{
font-size: 14px !important;
line-height: 20px !important;
}

.alsoReadHeadText{
font-size: 24px !important;
line-height: 20px !important;
}
}

Also Read

Financial wellness in the gig economy: Strategies for freelancers, independent contractors

AI in broader onboarding processes

When onboarding gig workers, the process involves a wide range of documents—from identity proofs to educational certificates, each with its format and structure. AI systems must accurately classify and extract information from these diverse documents. Utilising advanced OCR (Optical Character Recognition) technology combined with machine learning models trained on diverse document types and continuous training of these models can improve accuracy and adaptability to new document formats.

Building state-of-the-art AI models for document classification, detail extraction, and deep analysis of varied document types is important to simplify the onboarding process of employees, vendors, or customers while minimising the risk of fraudulent activities.

Ensuring data privacy and protection

With data abundance, data breaches are becoming a common occurrence. Like every other high data throughput industry, background verification companies need to be careful about how they handle and store client and candidate data.

It is crucial to prioritise the responsible handling of Personally Identifiable Information (PII) for both clients and candidates. This commitment translates into robust security measures. It’s crucial to ensure consistently meeting the highest global standards for data protection and adhere to certifications like SOC 2 Type 2, ISO-9001:2015, and ISO-27001:2013.

Conclusion

Much like other aspects of the economy and our lives, AI is leaving its mark in the background verification industry as well. From processing and analysing huge data sets to continuously learning and improving via unbiased datasets and human intervention, artificial intelligence-led technology is the way forward. However, whether it becomes a boom or a bane will depend on how businesses leverage it while overcoming its caveats.

As AI continues to evolve, its role in enhancing trust and security in the gig economy will only become more critical, paving the way for a more reliable and trustworthy gig workforce.