Identity verification: 8 levels of security - Which one to choose?

In an increasingly digitized world, the question of identity has become central. Frauds, usurpations, money laundering, etc. Banks and insurance companies face major challenges in ensuring the security of transactions while complying with regulations (KYC, AML, RGPD). Robust identity verification is therefore essential to ensure trust and compliance. Financial institutions must balance several imperatives:

• security : prevent fraud and protect sensitive data.
• Compliance : meet legal requirements and avoid sanctions.
• User experience : offer a smooth journey without excessive friction.

A process that's too strict can discourage customers, while overly flexible control exposes the business to risks. That is why it is crucial to choose a level of identity verification that is adapted to the specific needs of each institution. There are several verification methods. Each with its own levels of security and complexity. From in-person identification to automated data analysis, these solutions offer varying degrees of protection. In this article, we detail 8 levels of identity verification, their pros and cons, as well as their specific use cases. At the end of this reading, you will have all the keys to choosing the most suitable solution for your institution. Let's now discuss the 8 possible levels of identity verification. Starting with the most secure and most restrictive level for the user.

Level 1 - In-person identity verification

The historical standard

In-person identity verification is the oldest and most robust screening method. It is based on the client's physical presence in an agency or office, where an agent checks their documents and physical appearance.

Benefits of in-person identity verification

• Maximum security : It is extremely difficult to impersonate an identity when the authentication is done face-to-face.
• Strong legal value : Physical verification is often considered to be the gold standard for many administrative and banking procedures.
• Instant anomaly detection : A trained agent can spot fraud signals such as suspicious behavior or a forged document.

Potential drawbacks

• Binding for the user : Travelling to a branch is perceived as a major obstacle by customers used to 100% online services.
• High cost : Maintaining a physical network of agencies and training qualified personnel is a significant investment.
• Lack of scalability : This method does not allow you to manage a large number of checks simultaneously.

Use case: Border control

Border control is one of the few cases of in-person identity verification that is still common. And even it tends to be automated with the Parafe device. In the case of in-person identity verification, the agent can visualize specific checkpoints. It is also possible for him to pass the identity document under a UV lamp in order to distinguish complex security elements. Despite its constraints, in-person verification remains a solution of choice for institutions looking for maximum security. However, with the rise of digital technologies, more flexible alternatives are emerging to reduce friction while ensuring a high level of protection.

Level 2 - NFC verification of electronic documents

The reliability of contactless

With the rise of electronic identity documents (identity cards, biometric passports), NFC (Near Field Communication) technology allows fast and highly secure verification. This method is based on reading RFID chips embedded in identity documents to ensure their authenticity.

The benefits of NFC verification

• High reliability : The information stored on the chip is protected against forgery and can be verified instantly.
• Speed of the process : A simple reading with a smartphone or an NFC terminal is enough to retrieve and validate the data.
• Reducing document fraud : Electronic documents have cryptographic signatures that cannot be forged, guaranteeing their authenticity.

Inherent disadvantages of this method of identity verification

• Dependence on compatible equipment : Requires an NFC reader, which may limit accessibility for some institutions or users. However, this reader is integrated into the majority of new smartphones.
• Variable compatibility by country : Not all identity documents have an NFC chip yet. This can be a problem in an international context.
• Mixed user experience : We've noticed that some users are more wary when it comes to scanning their document. We recommend offering alternative methods to ensure a high conversion rate.

Use case: Opening an online bank account

Many online banks allow customers to scan their passport or electronic identity card for instant validation. This allows them to meet customer knowledge, fraud and money laundering obligations. Thanks to this method, banking institutions optimally secure their onboarding processes.

Level 3 - Expert Guided Video Identity Verification

A compromise between human and digital

With the rise of digital solutions, video verification assisted by a human expert has become an effective alternative to in-person verification. This process consists of guiding the user through a live or delayed video call, during which they must present their identity documents and perform certain actions to prove their authenticity.

Why choose this level?

• Enhanced security : The presence of a human operator allows a thorough analysis of documents and user behavior. This significantly reduces the risk of fraud.
• Enhanced user experience : Less restrictive than a trip to an agency. This solution allows remote verification while benefiting from human support.
• Real-time anomaly detection : An expert can spot fraud signals such as nervous behavior, hesitation, or forged documents.

Operational disadvantages

• High cost : Requires trained and available agents to carry out the checks. This incurs greater costs than a 100% automated solution.
• Variable treatment time : Depending on the availability of agents and the volume of requests. The process can take longer than a fully automated audit and can be complex to scale up.
• Variable availability : Accompanied verification cannot be implemented at any time of the day or night. Registering for late hours can be complicated.

Use case: Subscribing to sensitive insurance

The insurance product is permanent over time and may only be activated once the identity verification has been validated. When taking out insurance, the user can enter all their information and then be called back by an agent (even a few minutes or hours later). Accompanied verification is therefore a good way to protect yourself from fraud while establishing a relationship of trust with the customer. Video verification assisted by an expert represents a good compromise between security and convenience. Especially for institutions that want human validation without requiring their customers to physically travel. However, it still has a high cost.

Level 4 - Biometric Verification

Authentication through the unique traits of the user

Biometric verification involves analyzing an individual's unique physical or behavioral characteristics to confirm their identity. This technology, which is increasingly used in banks and insurance companies, ensures a high level of security while improving the user experience.

Different types of biometrics

Face recognition

Facial recognition compares an image of the user's face with an identity photo (identity card, passport or residence permit). Advanced algorithms analyze key points on the face to verify the match.

• Advantages:
  • Fast and smooth: a simple image capture is enough.
  • Can be combined with “proof of life” tests (blinking, head movements) to avoid photo or video fraud.
  • Integrable with existing smartphones and cameras.
• Disadvantages :
  • Vulnerability to deepfake attacks if anti-fraud tests are not robust.
  • Dependence on camera quality and lighting conditions.
  • May ask ethical and regulatory questions about data protection.

Voice recognition

Speech recognition analyzes the unique characteristics of a user's voice to authenticate them. This technology is particularly used in bank customer service to secure access to accounts via telephone.

• Benefits :
  • Seamless user experience with no manual action required.
  • Can be used in environments without screens
  • Secured thanks to the analysis of voice frequencies and speech rhythm.
• Disadvantages :
  • Sensitive to voice variations (diseases, noisy environment).
  • Exposure to the risks of usurpation by imitation or registration.

Examples of use: Secure banking customer service using voice recognition

The use of voice recognition in banking services makes it possible to limit identity theft attempts. Thanks to this technology, financial institutions are able to improve the security of their customers. This comparison requires no action on the part of the customer and can be completely transparent for the customer.

Level 5 - Hybrid identity verification

The best of both worlds

Hybrid verification combines the benefits of artificial intelligence and human intervention. It thus offers a high level of security while optimizing the user experience. The AI performs an initial analysis of documents and biometric information. Secondly, a human agent intervenes in case of doubt or anomaly detected.

The multiple benefits of hybrid identity verification

• Enhanced security : The AI filters the majority of requests and sends suspicious documents or files to an agent. Human control helps avoid sophisticated mistakes and fraud.
• Optimized user experience : Automation guarantees a quick response in the majority of cases. Human assistance avoids unnecessary bottlenecks and ensures customer satisfaction.
• Cost reduction : Less human intervention than 100% manual verification, while limiting false rejections and fraud.
• Flexibility and adaptation : Allows you to adjust the level of control according to the level of risk detected (dynamic risk analysis).

Disadvantages of hybrid identity verification

• Requires advanced infrastructure : Integrating efficient algorithms and a human verification team requires resources and technical know-how.
• Variable treatment time : In the case of a manual check, the response time may be longer than for a fully automated check.
• Cost greater than an AI alone : Although less expensive than 100% human verification, this approach still requires a team of experts to manage exceptional cases.

Use case: Regulated service platforms

Online service platforms (fintechs, cryptocurrencies, etc.) generally use this approach. This allows them to meet their legal obligations to fight fraud and to secure their platforms while ensuring optimal efficiency of the process. The hybrid model is often considered to be the best approach, as it makes it possible to exploit the speed and efficiency of AI while benefiting from intelligence and human expertise to deal with the most complex cases.

Level 6 - Automatic identity verification using artificial intelligence

Speed and scalability

With advances in AI, identity verification can now be fully automated. It offers unparalleled speed and scalability. This method is based on algorithms for recognizing documents and detecting fraud. AIs identify anomalies and check the security features built into identity documents. These AIs are often combined with biometric technologies for double verification.

What about AI identity verification

• Instant treatment : Unlike manual or semi-manual methods, AI allows for almost immediate verification. This contributes to the optimization of the user experience.
• Reducing human errors : The algorithm applies strict and objective rules. It eliminates possible biases or oversights during human analysis.
• Unparalleled scalability : This approach can handle a large number of checks simultaneously. It is ideal for institutions dealing with large volumes of customers.
• Continuous improvement : Thanks to machine learning, models adapt and become more efficient in the face of new fraud attempts.

Some disadvantages remain

• Vulnerability to sophisticated attacks : Deepfakes and other circumvention techniques can trick some poorly trained or outdated AI systems.
• Difficulty interpreting some errors : If an identity is rejected, the user may not understand the exact reason. This can be detrimental to the customer experience.
• Dependence on training data : Effective AI requires a representative and regularly updated database. This is the only way to avoid bias and false positives.

Use case: Identity verification for real estate rentals

When renting a house or apartment, the tenant file is generally required. It includes all the identity documents and supporting documents of the potential tenant. With the large number of prospective tenants, it is often complex to deal with each file. Thanks to AI verification, rental agencies automate this process. Automated AI verification is the solution of choice for businesses seeking to combine efficiency and security, provided it is coupled with complementary measures to avoid breaches that can be exploited by fraudsters.

Level 7 - Automated document verification

Speed and efficiency at the heart of the process

Automated document verification is based on the use of advanced technologies. These aim to extract, analyze and validate information from digital documents. This method combines optical character recognition (OCR) and forgery detection. It is increasingly used by businesses to ensure fast and reliable data processing.

Technologies used

OCR (Optical Character Recognition)

This technology allows the rapid extraction of information contained in a document. This includes names, birth dates, identity card or passport numbers. Thanks to sophisticated algorithms, OCR can process a wide variety of documents with remarkable precision.

Detection of falsifications via metadata

Specialized tools analyze the metadata of documents (creation date, modifications, origin). They are looking for anomalies that could indicate falsification. These tools also detect visual inconsistencies such as retouching signs or format errors. These methods make it possible to detect basic falsifications. They are generally also set up for the verification levels mentioned above.

The advantages

Real-time treatment without human intervention

This technology allows instant document processing. This greatly improves the speed of verification processes and reduces response times for users. Automation ensures 24/7 service continuity, which is a major asset in environments with a high volume of transactions.

Reducing the risks of document fraud

Automatic document verification tools generally allow the detection of falsifications. This constitutes a first layer of controls, which is essential. The use ofmachine learning algorithms allows these systems to improve over time and become more efficient in the face of new fraud attempts.

Example use case: Automatic control of insurance receipts

In the insurance industry, document verification is essential to validate information provided by customers and to initiate reimbursement. Automated systems check the conformity of submitted documents, such as invoices or medical certificates, ensuring that requests are processed quickly and reliably. Document forgery attempts can be detected using these same AI systems.

Level 8 - Automatic Data Analysis

The intelligence layer to prevent risks

Automatic data analysis provides an additional dimension of security in the identity verification process. By exploiting artificial intelligence (AI) algorithms and advanced data processing techniques, this method focuses on analyzing and correlating collected information to detect inconsistencies, suspicious behavior, or fraud risks.

Alone, this level of security is not sufficient. It is when it is added, as an additional filter, to the other levels of verification that it proves to be powerful.

How does data analysis improve safety?

Correlation of information

Automatic analysis makes it possible to cross-reference information from different sources (identity documents, public databases, transaction history). In this way, it is possible to check the consistency of the data provided by the user. For example, if a customer's information on a registration form does not match that recorded in public databases, the system may report an inconsistency.

Detecting suspicious behavior

Behavioral data refers to transaction patterns, connections at unusual times, or IP addresses used. Their analysis makes it possible to identify repeated fraud or identity theft attempts. Systems can assess risks in real time. They alert operators or by blocking suspicious actions before they are finalized.

Why integrate an over-layer of automatic data analysis?

Continuous improvement of detection algorithms

Thanks to machine learning, data analysis systems can learn continuously from the new data processed. These allow adjustment to the new tactics used by fraudsters. This process allows rapid adaptation to changes in fraud techniques and more accurate detection.

Assistance in compliance with anti-money laundering regulations (AML)

Data analysis is a key tool for complying with the requirements of LCB-FT regulations. Automated systems allow suspicious transactions and behaviors to be monitored in real time. They ensure that institutions comply with legal obligations while minimizing risks.

Examples of use: Identifying identity thefts

Data analysis is also used to detect identity theft in large-scale contexts. Large-scale refers in particular to massive registrations for online services or the opening of bank accounts. AI systems can identify recurring patterns of identity theft. This is done by correlating information from different databases and by analyzing patterns of behavior.