Artificial Intelligence and ID Verification: How does it work?

Artificial intelligence is a simulation paradigm that enables computers and machines to perform human-like decisions and automate tasks and process flows. Aside from a few initial parameters, there is little need for human intervention. These applications can be leveraged very successfully by the cybersecurity gateways of institutions to prevent identity theft, as computer algorithms can easily analyse data and train detection models based on their results. This makes it possible to check, verify and authenticate identities at the scale required by large financial institutions. 

When bank accounts are opened, clients usually present driver’s licenses or other authorized ID documents that are scanned for registration and verification. Document verification is used to check the originality of the document through OCR (Optical Character Recognition). OCR technology can recognize text in scanned documents and images. At this stage, technology can confirm genuine microprint text and utilize facial recognition to match identities.

By adding machine learning and automation techniques to the identification process, verification can become faster and more accurate than existing online and even manual checking could.

The Impact of AI on ID Verification

Employing AI in ID verification can increase the security of commercial portals significantly and provide businesses with a real competitive advantage. Here are just a few of the ways AI will improve workflows and facilitate growth for many industries, including the retail and banking sectors: 

Data Accuracy

New clients are required to submit a number of documents during onboarding as part of KYC compliance checks, e.g. passports, government IDs and proof of residence. These documents must be checked by human beings, which requires a lot of resources and may lead to human error. AI automates the verification process so that documents are uploaded and reviewed by technology in seconds. This not only reduces the possibility of errors but can onboard clients far more rapidly. 

Risk Profiling

Many companies adopt a risk-based approach to onboarding. Once customers are onboarded, their activities and risk status are evaluated to determine whether they pose a risk for fraud or money laundering (or other criminal activities). This risk profile is periodically assessed during the customers’ lifetime. It’s a complex process and cannot be done manually. AI, however, can process huge volumes of unstructured data over time and can spot anomalies that may raise red flags very quickly. AI technology is often deployed for PEP and sanctions screening purposes for this very reason. It would take a human being considerable time and effort to access and analyse data contained in sanctions or PEP lists, or government and corporate registries, in a manual manner. Using technology to perform these searches and amass the necessary information will offboard compliance teams from the burden of analysing these lists while fully meeting compliance requirements. 


ID verification and KYC processes require time, effort and resources. Many customers may lose interest or become impatient during this process. AI will make it faster and easier for customers to onboard by creating a seamless workflow that takes users from one step to the next in seconds without compromising the required due diligence checks. 

Cutting Costs 

Manually performing ID verification checks is extremely expensive. Numerous workers need to be hired to check and review documents, while inefficiencies may even lead customers to quit halfway through the process. It also opens up the company to regulatory fines or reputational damage if errors creep in that compromise personal information. AI reduces the need for those resources (and the salaries required to keep them working on ID verification).

What Does It Take To Introduce AI to ID Verification?

Clean data is required to design, test, model and implement any machine learning algorithm, including facial recognition or recommenders. Next, a data expert that understands the industry and what success or approval looks like must define critical threshold parameters at the onset. Machine learning models require training in order to enhance their decision-making capabilities. In some instances, automated authentication checks may fail (e.g. due to wear and tear on ID documents or poor lighting conditions). 

Human intervention and manual checking will be required to address those issues, through human ID validation experts, especially in the early training stages. This will not only identify key problems with the system but improve it. 


AI can improve the entire verification process from start to finish by making verification faster, smoother and more accurate. However, it requires real expertise to “train” the algorithm and generate the results you want. 

Make sure that you partner with ID verification companies that have the skills, technology and depth of knowledge you need for your business.