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AI Redefining Compliance Verification for India’s Gig Economy

The small office in Bengaluru buzzed with activity, but the tension was palpable.

Rajesh, head of onboarding for a burgeoning quick-commerce platform, stared at a stack of paper documents piled high on his desk.

Each form represented a new gig worker, desperately needed to keep up with the city’s insatiable demand for instant deliveries.

But each form also represented a potential risk: an unverified identity, a fraudulent claim, a loophole in a fragmented regulatory landscape.

His team, already stretched thin, was drowning in manual background checks.

It felt like trying to fill a bathtub with a teaspoon while the tap was gushing.

He knew there had to be a better way to ensure trust, speed, and scale.

This challenge is not unique to Rajesh’s team.

India’s booming gig economy is experiencing unprecedented growth.

By fiscal year 2024–25, the sector employed nearly 12 million workers, a significant jump from 7.7 million in 2020–21, and is projected to double by 2030 (TOI Business Desk, 2024).

With blue-collar gig hiring alone soaring by 92 percent in 2024, companies are onboarding workers far faster than traditional compliance teams can manage (TOI Business Desk, 2024).

This rapid pace has raised critical concerns about safety, fraud, and identity-based risks, pushing businesses towards AI-led verification solutions.

In short: AI is rapidly transforming compliance verification in India’s booming gig economy.

It addresses critical challenges like fraud and identity risks stemming from rapid hiring, by automating checks and improving accuracy, despite existing barriers in cost and digital readiness.

The Gig Economy’s Rapid Ascent and Compliance Challenges

India’s hyperlocal economy is underpinned by this dynamic gig workforce.

From food delivery to ride-sharing, these platforms are transforming urban life.

However, the sheer volume and velocity of hiring cycles create a significant mismatch: compliance teams simply cannot keep pace with worker onboarding.

This creates a critical vulnerability, making businesses susceptible to fraud, safety breaches, and identity-related risks (TOI Business Desk, 2024).

It is a complex dance between speed, trust, and ensuring robust safeguards in a high-stakes environment.

The problem is not a lack of effort from compliance teams.

It is an issue of scale that human-only processes are inherently ill-equipped to handle.

The growing need for AI compliance India driven solutions is therefore not a luxury, but a necessity to maintain operational integrity and public trust.

The Blueprint: An Overwhelmed Compliance Department

Imagine Rajesh’s compliance department.

Every day, hundreds of new applications flood in.

Each requires manual document verification, cross-referencing, and risk assessment.

Emails fly, spreadsheets are updated by hand, and contracts are negotiated individually.

This manual, resource-intensive process becomes a bottleneck, slowing down onboarding and increasing the risk of errors.

In such an environment, the focus often shifts from thoroughness to simply keeping up.

The current digital maturity of many Indian companies, where internal processes still rely on email and spreadsheets, creates a gap where AI’s advanced capabilities struggle to integrate seamlessly (TOI Business Desk, 2024).

This operational readiness gap is India’s most significant compliance risk, particularly for small vendors and franchise operators within the gig economy.

AI as the New Frontier in Verification Workflows

The good news is that innovative companies are already leveraging AI to redefine compliance and verification.

Businesses like Melento (formerly SignDesk), Unstop, and Ongrid are showcasing the transformative power of AI in streamlining complex workflows and enhancing security.

Their experiences provide a valuable glimpse into the future of gig economy verification.

AI-led verification is crucial for India’s rapidly expanding gig economy to manage safety, fraud, and identity risks.

Manual processes are inadequate for the scale of gig hiring.

Companies must strategically adopt AI solutions to validate identities, detect anomalies, and monitor compliance in real-time, as manual processes cannot keep pace with rapid hiring volumes (TOI Business Desk, 2024).

Digital maturity and existing internal processes significantly impact AI adoption in compliance workflows.

Powerful AI tools require a compatible digital foundation.

Even powerful AI tools are underutilized if organizations still rely on manual methods like emails and spreadsheets, highlighting the need for broader digital transformation before full AI benefits can be realized (TOI Business Desk, 2024).

Bias prevention and ethical considerations are integral to AI-powered hiring and verification.

AI models must be fair to be trustworthy and effective.

Companies deploying AI in HR tech AI must prioritize training models on anonymized, balanced datasets across diverse demographics.

Implementing human-in-the-loop validation and regular audits ensures fairness and accuracy, building trust in the AI system and mitigating ethical risks (TOI Business Desk, 2024).

This focus on algorithmic accountability is paramount.

Industry Leaders Paving the Way: Case Studies

The examples from leading Indian companies illustrate AI’s practical application in compliance.

Melento, a compliance-focused platform that processes over 50 million documents annually across banks, NBFCs, and large corporations, has profoundly reshaped its contract management using AI.

According to its founder and CEO, Krupesh Bhat, AI now handles the initial layer of contract reviews.

It helps identify the template and runs the first review based on the playbook that’s created.

It does redlining, highlights risks in contracts, and even tracks milestones and deliverables (TOI Business Desk, 2024).

Bhat adds that AI can automate complex actions, such as alerting teams, processing payments, or generating legal notices.

He notes that clients do not fear AI; they simply want clarity on its purpose, boundaries, and oversight (TOI Business Desk, 2024).

This demonstrates the potential of contract management AI to boost efficiency.

At Unstop, a platform specializing in campus and early-talent recruitment, AI now powers nearly 80 percent of the screening workflow, from assessments to document verification and fraud detection.

Ankit Agarwal, Founder and CEO of Unstop, reports that AI flags anomalies in resumes and identity mismatches with over 92 percent accuracy.

This automation of the first verification layer has cut manual review time by 70 percent, ensuring a uniform, skills-first evaluation for every candidate (TOI Business Desk, 2024).

To strengthen fairness, Unstop trains its models on anonymized, balanced datasets across various demographics and income groups.

Agarwal states that bias prevention starts with data hygiene.

The platform uses human-in-the-loop validation for every risk flag, conducts monthly audits, and routes a case for manual review if AI confidence drops below 85 percent (TOI Business Desk, 2024).

This meticulous approach embodies the principles of human-in-the-loop AI.

Ongrid, a background verification company, applies AI more selectively, often to complement human oversight.

Ajay Rao, its Chief Technology Officer, explains that AI accelerates parts of the process by flagging potential discrepancies and speeding up verification.

However, human review is still critical, especially when dealing with sensitive PII and legal compliance.

Rao adds that generative AI models are increasingly useful for extracting structured data from documents, analyzing risk patterns, and identifying missing information (TOI Business Desk, 2024).

This highlights the nuanced role of fraud detection AI.

Navigating the Regulatory Landscape: Informal Principles and Oversight

The regulatory landscape for gig-worker compliance in India is still evolving.

There is no consistent rulebook, and state-wise enforcement remains fragmented.

In this vacuum, the sector is informally guided by emerging principles.

These include algorithmic accountability, ensuring fairness in worker classification, promoting transparent decision-making, and advocating for minimal data use (TOI Business Desk, 2024).

Companies like Melento are actively reorienting themselves for this shift.

Krupesh Bhat confirms, We are transforming into more of an AI-native product company (TOI Business Desk, 2024).

This strategic pivot aims to leverage AI to launch new products and features quickly, increasing service offerings without necessarily reducing headcount.

Melento’s internal AI playbook is built on explainability, human oversight, and auditability—logging and reviewing every AI decision and reviewer action.

This proactive approach addresses the requirement that many clients insist on before giving a go-ahead for AI deployment (TOI Business Desk, 2024).

Overcoming Barriers: Cost, Trust, and Digital Maturity

Despite AI’s clear advantages, several significant barriers hinder widespread adoption.

Krupesh Bhat of Melento identifies trust as a major hurdle.

He notes that even though Melento does not use customer data to train its models, some clients worry about data privacy (TOI Business Desk, 2024).

Another substantial challenge is the cost of AI systems.

Bhat explains, AI is expensive, usage-based and still requires complementary digital tools.

For many Indian companies, especially those operating on thin margins, hiring entry-level staff is often perceived as simpler and cheaper than investing in these advanced AI tools (TOI Business Desk, 2024).

Advanced AI systems, particularly those driven by Large Language Models (LLMs), demand robust infrastructure, reliable data flows, and dedicated compliance governance.

This mismatch between AI capability and the current operational readiness of many organizations, particularly small vendors and franchise operators with limited digital infrastructure in the gig economy, remains India’s most significant compliance risk (TOI Business Desk, 2024).

This digital transformation gap is a crucial hurdle for broader adoption.

The Future of Compliance: Human Expertise Enhanced by AI

India’s identity verification and background-check industry is experiencing rapid expansion, fueled by the growth of gig platforms, BFSI (Banking, Financial Services, and Insurance), and shared-services firms.

This market reached $451.1 million in 2024 and is projected to surge to $1.72 billion by 2033, with a robust Compound Annual Growth Rate (CAGR) of 16 percent from 2025-2033 (IMARC Group, 2024).

Industry analyses also point to rising identity-related discrepancies in logistics and delivery-led sectors, further intensifying the demand for AI-assisted verification in high-velocity environments where human teams struggle to keep pace with scale (TOI Business Desk, 2024).

Despite the barriers, the undeniable advantages of AI are driving its adoption.

Ajay Rao of Ongrid articulates this synergy: AI is transformative when combined with human expertise.

It allows our teams to focus on higher-value tasks while AI handles routine data processing (TOI Business Desk, 2024).

This perspective clarifies that AI will not replace compliance teams but rather become their most critical tool, empowering human experts to manage scale and focus on nuanced legal and personal data compliance issues.

This blend of human judgment with AI efficiency is crucial for the future of work India.

Regulatory technology solutions are expected to grow significantly.

Conclusion: Trust, Speed, and Scale in India’s Evolving Workforce

The narrative of compliance verification in India’s gig economy is a story of immense growth met with unprecedented challenges, where the rhythm of human processing can no longer match the beat of rapid expansion.

The journey from manual spreadsheets to intelligent algorithms is complex, marked by hurdles of trust, cost, and digital readiness.

Yet, as companies like Melento, Unstop, and Ongrid demonstrate, AI is rapidly proving its value, not as a replacement for human oversight, but as an essential partner.

As India’s gig economy continues its relentless expansion, the pressure to build faster, safer, and more transparent verification systems will only intensify.

AI will become the cornerstone of this evolution, helping build a resilient infrastructure where trust, speed, and scale are not just aspirations, but integrated realities.

FAQ

How big is India’s gig economy and why is AI crucial for its compliance?

India’s gig and quick-commerce sectors have nearly 12 million workers in FY 2024–25 and are expected to double by 2030.

AI is crucial because rapid hiring cycles and a 92 percent rise in blue-collar gig hiring in 2024 overwhelm manual compliance, creating risks like fraud and identity issues.

This information is from the Main Content Article (TOI Business Desk, 2024).

What specific tasks can AI perform in compliance verification?

AI can perform first-layer contract reviews, redlining, risk flagging, milestone tracking, and automate actions like alerts and payments (Melento).

In hiring, it powers 80 percent of screening workflows, including assessments, document verification, and fraud detection with over 92 percent accuracy (Unstop).

It also identifies discrepancies and extracts structured data from documents (Ongrid).

This is detailed in the Main Content Article (TOI Business Desk, 2024).

What are the main challenges hindering AI adoption in India’s gig economy compliance?

Key challenges include client concerns about data privacy and trust in AI, the high cost and usage-based nature of AI systems, and the lack of digital maturity in many organizations and small vendors who still rely on manual processes like email and spreadsheets.

This is outlined in the Main Content Article (TOI Business Desk, 2024).

How do companies ensure fairness and prevent bias in AI-powered verification?

Companies like Unstop train their AI models on anonymized, balanced datasets across diverse demographics.

They also implement human-in-the-loop validation for risk flags, conduct monthly audits, and route cases for manual review if AI confidence drops below 85 percent.

This approach is described in the Main Content Article (TOI Business Desk, 2024).

Will AI replace human compliance teams?

AI is unlikely to replace human compliance teams.

Instead, it is becoming their most critical tool, enhancing human expertise by automating routine data processing and enabling teams to focus on higher-value tasks, complex cases, and nuanced legal and personal data compliance.

This perspective is shared in the Main Content Article (TOI Business Desk, 2024).

Glossary

Algorithmic Accountability

The principle that algorithms should be transparent, understandable, and their decisions explainable, especially in critical applications.

Digital Maturity

The extent to which an organization has adopted and integrated digital technologies across its operations, culture, and strategy.

Fraud Detection AI

Artificial intelligence systems designed to identify and flag suspicious patterns or anomalies that may indicate fraudulent activity.

Human-in-the-Loop AI

An approach where human intervention is integrated into AI systems to review, correct, or validate AI-generated decisions, enhancing accuracy and fairness.

Identity Verification

The process of confirming that a person is who they claim to be.

Regulatory Technology (RegTech)

Technology solutions designed to help companies comply with regulatory requirements more efficiently and effectively.

Workforce Management Solutions

Software and systems that help organizations manage various aspects of their workforce, including hiring, scheduling, and compliance.

References

• IMARC Group.

  (2024).

  IMARC Group.

• TOI Business Desk.

  (2024).

  Main Content Article.

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