The evolution of identity verification and what’s next

In the rapidly evolving digital age, the way we verify our identities has undergone significant transformation. From the traditional methods of in-person verification and physical documents to the sophisticated use of biometric technologies, the identity verification industry has seen remarkable advancements.

With over 20 years of experience in this field, I’ve had the privilege of witnessing and influencing these changes first-hand. As I look at the next stage of its evolution, I find it just as important to reflect on how it’s developed and why. In this piece I’ve explored the pivotal moments that have shaped this industry, offering insights into its current state and a glimpse of what lies ahead.

Data sparks the creation of the identity market

The early 2000s marked the beginning of a significant transformation in how individuals verified their identities. During this period, the internet was rapidly expanding, and seeing an opportunity for growth, more businesses moved online. This change quickly meant traditional methods of verifying an identity, such as in-person verification and physical documents, were no longer sufficient. All of sudden, businesses needed a new way to confirm if a customer was really who they said were and this had to take place within the digital sphere.

So, the identity market was born.

Initially the market worked by leveraging offline data to verify customers online. Companies began to access and analyze vast amounts of data to establish the identities of their users, including names, addresses and regional identity data. By cross-referencing this information with existing databases, businesses could verify the authenticity of their customers more efficiently and accurately.

One of the key developments during this period was the rise of credit bureaus and data aggregators. This enabled companies to quickly and easily verify the identities of their customers by comparing the information provided with the data held by these bureaus.

However, this approach was not without its challenges. The reliance on static data, such as social security numbers and addresses, made it vulnerable to fraudsters who got their hands on stolen PII (Personally Identifiable Information). Privacy concerns also began to emerge as consumers became increasingly aware of the amount of personal data being collected and shared.

Despite this, the use of data to verify customers laid the foundation for the modern identity verification market we see today. It demonstrated the potential of leveraging technology and data to enhance security and streamline the verification process, setting the stage for innovations in the years to come.

The rise of biometrics to supplement data-driven decisions

From 2014 onwards, the identity market saw significant advancements with the increased usage of document-based verification and biometric technologies. These innovations were driven by the need to address the limitations of data-driven verification methods as some of the market’s biggest fears came to bear with significant data breaches spilling mass amounts of individuals’ personal data into the hands of fraudsters. The market needed to enhance the overall security and accuracy of the verification process.

Document-based verification emerged as a powerful tool to supplement data-driven decisions. This method involved the use of government-issued identification documents, such as passports, driver's licenses, and national ID cards, to verify the identity of customers. By requiring users to submit scanned copies or photos of their documents, businesses could cross-check the information against official records, adding an extra layer of security.

In parallel, biometric technologies began to gain traction as a means of verifying identities. Biometrics, such as fingerprint scanning, facial recognition, and iris scanning, offered a more secure and reliable way to authenticate users. Unlike static data, biometric traits are unique to each individual and difficult to replicate, making them highly effective in preventing fraud.

The integration of biometrics into identity verification systems provided a multi-factor authentication approach, combining something the user knows (data) with something the user has (documents) and something the user is (biometrics). This layered approach significantly enhanced the security of the verification process and reduced the risk of identity theft and fraud.

Regulatory frameworks also evolved to keep pace with technological advancements. Regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) imposed stricter requirements on businesses to protect consumer data and ensure the privacy of personal information. Rightly so, these regulations further drove the adoption of secure and compliant identity verification methods.

How much can you trust an identity?

In the last five years, the identity verification industry has evolved to place more focus on the trustworthiness of an identity. Confirming if an identity is real or not became only one part of the equation. Equally critical was whether this identity could be trusted or not.

By combining identity and sophisticated fraud signals and analyzing data from multiple sources, a comprehensive view of an individual's identity and risk in real-time became possible.

Machine learning (ML) and artificial intelligence (AI) also increased the accuracy and speed of identity verification. With the support of this technology, identity platforms could analyze more data than ever before, helping them to continuously learn, adapt to new fraud patterns and make better and faster risk assessments.

A key step forward during this time was the use of a wide range of identity signals, layered together, including behavioral biometrics, device intelligence, and geolocation data. With growing levels of fraud and, specifically identity fraud, impacting businesses, fraud signals, such as transaction history, account activity, and known fraud patterns, combined with identity data, document verification and biometric authentication, enabled platforms to not only confirm if an identity was real but whether they could be trusted.

What’s next?

In 2025 and beyond, identity verification will continue to evolve to address threats and improve the identification of legitimate customers.

More cross-sector and industry collaboration will become an important part of identity verification strategies for global businesses as they place more resource and focus on stopping fraud before onboarding. Afterall, the most efficient way to stop fraud is to never let a fraudster into your system.

Shared cross-sector identity intelligence brings together hundreds of global businesses and billions of identity insights for a strong first line of defense against identity fraud. As a result, suspicious identity data anomalies and high-velocity submissions across multiple organizations can be detected in real time, enabling businesses to block fraud before it’s onboarded. On the other side, with access to millions of identity insights through a global network, businesses can find positive matches and onboard more good customers at the very first contact, even if their identity footprint is small.

As a result, shared identity intelligence networks will grow in use and become central to combating identity fraud and helping to increase identity inclusion for traditionally hard-to-reach customers such as new-to-country or thin-file customers.

This is already happening but not at the scale needed. However, there has been a notable shift in how some regulators now approach data sharing. In late 2024, the UK Information Commissioner's Office (ICO) clarified data protection laws do not prevent organizations from sharing personal information to combat fraud. It advised organizations to share data in a "responsible, fair, and proportionate" manner to help individuals avoid the economic and financial harm associated with fraud. We’ll likely see this debate continue to evolve as businesses navigate the complexities of shared intelligence and compliance.

In parallel, the use of AI will accelerate both in identity verification and in identity fraud. Already Generative AI has enabled fraudsters to create highly realistic images, videos, and audio, which can be used to disguise digital appearances – known as deepfakes. Popular face swap apps have increased the risk of fake personas and fraud, making it more challenging to trust online identities. Additionally, voice manipulation and deepfake videos have become common methods used in fraud attacks.

While AI has enabled deepfakes and synthetic identities, it will also enhance identity fraud protection. Advancements in biometric technologies are expected to improve the security of digital identities. Smart identity proofing systems are developing a multi-layered defense against identity fraud involving deepfake documents. These systems combine biometric authentication with video injection attack detection and AI-powered data mining to quickly analyze large volumes of data and identify anomalies in identity documents. 

The journey of identity verification has come a long way from its early stages of basic documentation to a sophisticated layered approach that combines advanced technologies, cross-sector collaboration and human intelligence to determine if an identity is real and can be trusted. Now more than ever, an identity is a form of currency that provides people with access to the digital economy, and the identity verification industry will need to continuously evolve, to both outpace fraudsters and protect the integrity of genuine digital identities.

While the path forward will undoubtedly present new and exciting challenges, as we look forward to the future, it is evident that the fight against identity fraud will require a multifaceted approach that combines behavioral biometrics, identity data, AI-driven insights, and shared identity intelligence networks.