The gig economy is no longer a niche labour market. With estimates ranging from 154 million to 435 million workers globally and a market generating over $582 billion in annual revenue, platform-mediated work has become a structural feature of the global economy. Workers in the gig economy deliver food, transport passengers, provide childcare, tutor students, offer home maintenance, write code, manage financial data, and deliver healthcare. They do so across national borders, often simultaneously for multiple platforms, and typically with minimal direct supervision.
The background verification frameworks applied to these workers, where they exist at all, were designed for traditional employment a model with defined start dates, linear career histories, institutional references, and jurisdictional clarity. That model has been breaking down for years. The gig economy has simply made the breakdown impossible to ignore.
The Scale of the Problem
The numbers paint a stark picture. In 2024, one in every twenty gig worker verification attempts was found to be fraudulent a 21% increase from the prior year. In the ride-share and mobility sector specifically, impersonation fraud accounted for over 90% of all fraud detected during onboarding.
Account sharing the practice of a verified individual allowing an unverified person to operate under their credentials remains the single most persistent form of fraud in gig platforms. A driver verified through a criminal check, identity confirmation, and licence validation logs in, then hands the account to someone who has passed none of those checks. The customer, the platform, and the regulator all believe they are dealing with a verified worker. They are not.
The consequences of these failures are not abstract. Passengers have been assaulted by drivers operating under shared accounts. Patients have received care from healthcare workers whose credentials were never verified. Children have been tutored by individuals with undisclosed criminal histories. In each case, the verification process existed on paper. It simply didn’t function in practice.
Why Traditional BGV Frameworks Break Down in the Gig Economy
Five structural tensions explain why traditional background verification models are inadequate for gig economy screening.
The speed-thoroughness tension. Traditional background verification operates on a timeline of days to weeks. The gig economy operates on a timeline of hours. When a food delivery platform is competing for driver sign-ups against three rival platforms, a verification process that takes five business days is not a minor inconvenience it is a competitive disadvantage that directly impacts supply-side growth. This creates constant pressure to accept faster, less comprehensive checks or to allow workers to begin operating before checks are complete.
The non-traditional work history problem. Traditional BGV relies on verifying linear employment histories: employer names, dates, job titles, references from managers. Gig workers frequently have fragmented, multi-platform work histories that do not fit this model. A delivery driver may have worked for four platforms simultaneously over a two-year period, with no single “employer” to contact for a reference. A freelance developer may have completed fifty projects across three marketplaces, none of which functions like a traditional employer. The standard employment verification call “Can you confirm that X worked for you between these dates?” has no meaningful equivalent in gig work.
Cross-border complexity. The gig economy is global by nature. A freelance platform may onboard workers from sixty countries in a single week. Each country has its own criminal record infrastructure, its own education verification system, its own data protection regime, and its own cultural norms around what information can and cannot be disclosed during a reference process. Building a verification programme that is genuinely comprehensive across this many jurisdictions rather than superficially global but actually dependent on incomplete databases is a challenge that most platforms have not adequately addressed.
Blurred liability. The traditional BGV model assumes a clear employer who bears responsibility for the quality and completeness of pre-employment checks. In the gig economy, the question of who is responsible is contested. Is the platform an employer? A marketplace? A technology intermediary? The answer determines the legal obligation to verify, and until recently, platforms have benefited from the ambiguity. That ambiguity is now being resolved in most cases, against the platforms.
The continuous verification gap. Traditional BGV is a point-in-time process. A check is conducted before hiring, and the result is assumed to remain valid. In the gig economy, this assumption is particularly dangerous. A driver whose licence was valid at onboarding may have it suspended six months later. A care worker whose DBS check was clean at sign-up may receive a conviction after starting work. Without continuous monitoring, these changes go undetected often until an incident forces the discovery.
The Regulatory Environment Is Shifting Fast
The regulatory landscape for gig economy verification is changing more rapidly than most platforms have internalised.
The EU Platform Work Directive, which took effect in December 2024 with member states required to transpose it into national law by December 2026, creates a rebuttable presumption that gig workers are employees. If this presumption holds, platforms will be subject to the same pre-employment screening obligations as any other employer. For platforms that have been operating with minimal verification or verification processes designed for independent contractors rather than employees this is a fundamental change.
India’s Digital Personal Data Protection Act (DPDPA) introduces new constraints on how personal data can be collected, processed, and stored during verification including for gig workers. Platforms operating in India will need to ensure that their screening processes comply with DPDPA requirements around consent, purpose limitation, and data retention.
In the United States, a patchwork of state-level AI transparency and employment screening laws is creating new obligations around the use of automated decision-making in hiring and verification. Several states now require disclosure when AI is used in employment decisions, and some require bias audits of automated screening tools requirements that extend to gig platform onboarding where those platforms are found to exercise employer-like control over workers.
The direction of travel is clear: gig platforms are being held to higher verification standards, and the regulatory frameworks are converging toward employer-equivalent obligations. Platforms that have not begun preparing for this shift will find themselves scrambling for compliance within tight timescales.
A Framework for Gig-Ready Background Verification
Addressing the gig economy’s verification challenges requires not incremental improvements to traditional BGV processes, but a fundamentally different framework. AMS Inform proposes five pillars of gig-ready background verification.
Pillar 1: Speed Through Automation, Not Shortcuts
The solution to the speed-thoroughness tension is not to accept lower-quality checks, but to automate high-quality checks so they complete faster. API-integrated identity verification that confirms a government-issued ID against a biometric selfie in under sixty seconds. Automated criminal record checks that access digital court and police databases in real time, rather than relying on postal requests. Education credential verification through digital certificate networks that can confirm a qualification in minutes rather than weeks.
The technology to conduct comprehensive verification at gig-economy speed already exists. The barrier is not capability it is adoption. Checkr, for instance, reports that 89% of its national criminal record checks complete within one hour through automation. The platforms that have invested in this technology have proven that speed and thoroughness are not inherently in conflict. They require investment, integration, and a provider with genuine technical capability but they are not mutually exclusive.
Pillar 2: Risk-Tiered Screening
Not every gig role carries the same risk profile, and verification programmes should reflect this. A food delivery driver who never enters a customer’s home requires a different verification scope than a care worker who provides intimate personal care to elderly people in their bedrooms. A freelance graphic designer working on brand assets requires different checks than a freelance accountant with access to client financial systems.
Risk-tiered screening means defining verification requirements by the risk profile of the role access to vulnerable people, access to financial assets, access to physical premises, access to sensitive data rather than applying a uniform process across all workers. This allows platforms to allocate their verification resources where risk is highest, without over-screening low-risk roles in a way that adds friction without proportionate benefit.
A practical tiered model for gig platforms:
Tier 1 Standard (all workers): Identity verification with biometric matching, criminal record check (jurisdiction of residence), right to work confirmation.
Tier 2 Enhanced (customer-facing, in-home, transport): Tier 1 plus driving record check (where applicable), enhanced criminal check including sex offender registries, address verification.
Tier 3 Regulated (healthcare, finance, childcare, education): Tier 2 plus professional licence verification with issuing authority, education credential verification, sanctions and exclusions screening, reference checks from previous engagements.
Pillar 3: Continuous Monitoring
A background check conducted at onboarding tells you about a worker’s history up to that point. It says nothing about what happens afterwards. Continuous monitoring addresses this gap by providing ongoing alerts when a worker’s status changes a new criminal conviction, a licence suspension, a regulatory sanction, an insolvency filing.
For gig platforms, continuous monitoring is not optional. The nature of gig work irregular engagement, minimal supervision, direct contact with customers means that the period between checks is exactly the period when undetected changes can cause harm.
Continuous monitoring operates through automated scanning of criminal record databases, licence registries, court filings, sanctions lists, and adverse media sources. When a relevant change is detected, an alert is generated for review. The worker’s access to the platform can be suspended pending investigation a capability that is technically straightforward for any platform that manages worker access through a digital system.
Pillar 4: Jurisdiction-Specific Compliance
A verification programme that applies identically across every market is not a global programme it is a domestic programme applied globally, which is a very different thing. Genuine jurisdiction-specific compliance means configuring checks differently for different markets based on what is legally required, legally permitted, and operationally possible in each jurisdiction.
In Germany, criminal record checks can only be requested by the individual and provided voluntarily. In India, education verification requires direct contact with awarding institutions, many of which operate manual processes. In the UAE, certain biometric and criminal record checks are only available through government channels. In the UK, enhanced DBS checks are available for roles involving vulnerable populations but standard checks are used for other roles.
A gig-ready verification programme must be designed with these jurisdictional differences built in not bolt on which requires a provider with genuine in-country operational capability across the markets where the platform operates.
Pillar 5: Candidate-Friendly Experience
Verification processes that are slow, opaque, or frustrating will cause platforms to lose the workers they are trying to onboard particularly in competitive markets where workers can simply sign up with a rival platform that has a smoother process.
A candidate-friendly verification experience means mobile-first document submission, real-time status tracking, clear communication about what is being checked and why, and completion timelines that are measured in hours rather than days. It also means designing the consent process to be transparent and proportionate workers should understand what data is being collected, how it will be used, and how long it will be retained.
The best verification programmes achieve both security and candidate experience. They are not in tension they are complementary aspects of a process that works.
Sector-by-Sector: What Adequate Verification Looks Like
Ride-share and mobility platforms require, at minimum, identity verification with biometric liveness detection (to prevent account sharing), criminal record checks, driving licence validation, and continuous monitoring of driving records. Platforms operating across borders need these checks configured for each jurisdiction’s specific requirements and data access pathways.
On-demand healthcare requires full professional registration verification with the relevant regulatory body (GMC, NMC, GPhC, and HCPC in the UK; equivalent bodies in other jurisdictions), enhanced criminal checks appropriate for work with vulnerable populations, health and occupational clearances, and employment history verification with gap checks. This is the highest-risk gig category and demands verification standards equivalent to or exceeding traditional healthcare employment.
Home services (cleaning, maintenance, repairs) require identity verification, criminal record checks including sex offender registries, and, where workers will have unsupervised access to premises, address verification and reference checks. The risk profile is driven by access to private homes and potentially vulnerable occupants.
Freelance finance and legal services require identity verification, professional qualification verification with issuing bodies, sanctions and PEP screening, financial background checks where appropriate, and employment history verification. The risk profile is driven by access to confidential financial and legal information.
EdTech and tutoring platforms require identity verification, enhanced criminal checks appropriate for work with children, education credential verification, and professional qualification checks where applicable. In many jurisdictions, anyone working with children even in a remote, digital capacity is subject to enhanced screening requirements.
Conclusion
The gig economy is not going to shrink. It is not going to revert to traditional employment models. And it is not going to become safer without deliberate investment in verification infrastructure that matches the way gig work actually operates.
The platforms that lead in verification will have a competitive advantage not just in compliance, but in trust. Consumer trust, regulatory trust, and investor trust all flow from the ability to demonstrate that the workers operating under your brand have been properly verified and are continuously monitored.
The platforms that treat verification as a cost centre to be minimised or a competitive bottleneck to be circumvented will eventually discover the cost of getting it wrong. And in the gig economy, where brand and trust are indistinguishable, that cost will be existential.
