Introduction
With more and more banking being done online, banks are finding it harder to ensure the correct customers are accessing their systems. As criminal activity increases within the sector, fraudsters are gaining new ways to access their systems including using fake identities, stolen information, forged identification and AI generated identities. In order to combat this, banks are using intelligent systems to detect any suspicious activity. This involves using AI and data analytics to help verify customers and detect any unusual behavior.
The increasing importance of intelligent identity protection has made AI identity verification within the banking sector popular. Using AI, banks are able to use machine learning, facial recognition, biometric recognition, document recognition and device intelligence to look at multiple aspects of a customer’s identity. This gives the banking sector more insight to detect any discrepancies, identify any potential identity fraud and detect suspicious activity. AI can also help ensure customers are protected throughout the whole process by constantly analyzing identity risks.
Synthetic Identity Fraud and Account Takeover Attacks
Synthetic identity fraud is a type of fraud where criminals use real customer information along with fake details. This can involve using someone’s real ID number along with fake names, dates of birth or addresses. Unlike regular identity theft, synthetic identity fraud can involve creating completely fake identities. This can be very hard to detect as some of the information used during the sign up process will match real customer information. Once created, criminals can use their identity to open bank accounts, gain credit facilities and build up transaction history.
Account takeover attacks are another major concern when using digital banking. This involves criminals gaining access to existing customer accounts using stolen passwords, phishing, malware, social engineering and stolen personal information. Once they’ve gained access, criminals may steal money, change account recovery information, ask for new payment cards or perform fraudulent activity. This can be very damaging as they are stealing from real customers who may have built up a transaction history. Also, if a fraudster has the correct log in details, traditional security measures may not be effective. This means that banks are increasingly using intelligent systems to verify customers by looking at the information they provide as well as how, where and when they are accessing their accounts.
How AI Helps Verify Customer Identities
1. Facial Recognition and Biometric Recognition
Facial recognition is a key tool used by banks to help verify digital identities. Customers may be asked to take pictures or perform a short video verification when signing up or performing sensitive actions. AI facial recognition analyses unique facial features and compares them to pictures on official ID or previous verifications. This can show any similarities and discrepancies that may indicate identity fraud. Biometric recognition can also be used to verify customers using fingerprint and voice recognition. If implemented correctly, this can provide an extra layer of security as biometric information is more difficult to replicate than traditional passwords or ID numbers.
Unfortunately, criminals are increasingly trying to get around facial recognition systems. They can use pictures, pre-recorded videos, masks and deepfake AI. Deepfake technology can alter images, videos and voices to make one person seem like someone else. To get around this, banks have begun using liveness detection with biometric recognition. This looks at whether a real person is performing the action. Some systems ask customers to perform natural actions, such as blinking or turning their head. More advanced systems analyze small movements on someone’s face. This helps ensure customers are real.
2. Document Verification with AI
Using AI to verify documents is an important step during the customer onboarding process. Unfortunately, criminals are increasingly using altered passports, driver’s license, national ID and other official documents. Using AI, banks can analyze the documents. This can include analyzing pictures, text, ID numbers, layout and security features. Optical character recognition can extract information from documents. Machine learning models can then look at whether the information matches the expected format and available identity information. This can also identify any suspicious alterations, different fonts, altered pictures and other discrepancies.
This can also help compare the information provided with other sources. For example, the name, date of birth and ID number can be compared to the information provided when signing up and information available from authorized verification systems. If there are large discrepancies, further checks may be required. This means less reliance on visually verifying documents and can help banks process real applications quicker. However, AI generated results shouldn’t be the only factor. If documents aren’t clear, unusual or may be affected by technical issues, they should be reviewed by a human.
3. Behavioural Biometrics and Device Intelligence
Behavioural biometrics can also help verify a customer’s identity. Unlike traditional biometrics, behavioural biometrics looks at how fast someone is typing, how they are using their touch screen, how they navigate, how they are moving their mouse and how they are holding/using their device. This can help create a behavioural profile for every customer. When someone attempts to access their account, AI can compare how they are interacting with their account compared to previous interactions. For example, if a customer typically types at certain speed and uses certain devices, they may raise a risk alert as they’ve suddenly performed multiple sensitive actions on a new device with different interactions.
Device intelligence is an important step to take. Banks can look at device details including device type, operating system, browser, network and any suspicious software. This can identify any devices previously identified for fraudulent activity or unusual logins. This can also be used to look at location patterns. If a customer appears to be logging in from far away locations in a short space of time, they may raise a risk. AI can analyze device intelligence, behavioural biometrics and transaction information to give a more detailed view of the customer’s identity risk. This can make it harder for criminals to be successful by just having a customer’s password.
4. Digital Identity Signals
One of the most important improvements in banking security is looking at continuous identity verification. Traditional systems tend to look at verifying a customer’s identity once they’ve opened an account. However, criminals can gain access to customer accounts months or even years after they’ve been created. Even if a customer passes verification, they can still be the victim of phishing, stolen passwords or device compromise. Continuous identity verification looks at relevant identity signals throughout the customer’s journey. AI can analyze login details, device changes, transaction details, network information and other signals to see if anything is unusual.
Risk based authentication can also help banks. Depending on the risk involved, banks can ask for different levels of verification. For example, if a customer is asking for their balance and they’re using a recognized device, they may only be asked for standard verification. If they’re making an unusually high transaction, changing their account recovery information suddenly or logging in from an unknown environment, they may be asked for extra verification. They may be asked to perform facial recognition, one time passwords or confirm the transaction using another trusted method. This can increase the security and means they aren’t being asked for extra verification each time they access their account. It also means banks can act quicker to any suspicious activity.
5. Deepfake Fraud
With the increase in generative AI, financial institutions are facing new challenges. Criminals are able to create realistic fake voices, altered videos and realistic faces. These can be used during the remote account opening process, customer support and identity recovery. For example, criminals can create a deepfake video to pretend to be a customer during a video verification. They can also create a fake voice to pass voice verification. This shows why banks shouldn’t rely on one factor of biometrics.
To reduce risks, financial institutions are using multi-factor verification. This includes facial recognition, liveness detection, document verification, behavioural analysis and device intelligence. AI can be used to detect deepfake fraud. This looks at discrepancies in facial movements, picture quality, voice and other signals. Banks can also ask for extra verification if they are performing sensitive actions such as account recovery or changing registered contact information. It is important to regularly test and update the verification systems as criminals are constantly updating their methods. Using multiple independent factors can make it harder for criminals to bypass the verification. However, there is no one method that can completely stop identity fraud.
6. Privacy, Accessibility and AI Verification
Whilst AI can help increase security, there are concerns around privacy. Using facial images, fingerprints, voice, device information and behavioural patterns can be sensitive. It is important to have clear policies in place to inform customers what information is being collected, why it is needed, how long it will be kept for and who will have access. Strong encryption, access controls and secure storage should be used to ensure nobody can access biometric or identity information. Banks should also follow data protection requirements and ensure they are not collecting or processing any information beyond what they need to. In Nigeria, banks should consider the requirements set out in the Nigeria Data Protection Act 2023.
Some customers may struggle with facial recognition due to poor lighting, poor quality cameras, different facial features, disabilities or issues with their device. Others may also have poor internet connections or do not have newer smartphones. If banks rely on automated verification, genuine customers may be turned away. To help with this, banks should offer alternative verification methods, accessible customer support and review any disputes. AI should also be regularly analyzed to ensure they are accurate and not biased.
The Future of AI Identity Verification
In the future, banks will be increasingly relying on intelligent systems to identify risks. Improvements in machine learning, digital identity, biometrics and fraud analytics will mean identity verification is more accurate. Financial institutions may increasingly use multiple identity signals to identify any suspicious activity during the sign up process, log in, payments and account recovery. Technology can also be used to help with privacy. This can include securely sharing information and using stronger encryption methods. However, there will need to be ongoing investment into technology, staff training, security testing and responsible data handling.
Banks should also understand that technology cannot completely stop identity fraud. It is important to work with other financial institutions, regulatory bodies, identity verification providers and customers to provide effective protection. Banks should regularly update their fraud detection models, investigate new methods and educate customers on phishing, social engineering and not sharing their verification information. Customers should also use strong passwords, enable any security features and report any suspicious activity on their account. Using intelligent verification methods along with effective security policies and educating customers can help banks provide more protection from synthetic identities, deepfake fraud and account takeover attacks.
Conclusion
AI identity verification is revolutionizing how banks protect their digital accounts. Using facial recognition, biometric recognition, document verification, behavioural biometrics, device intelligence and continuous monitoring, financial institutions can identify any suspicious activity more effectively. These tools can help provide protection from synthetic identity fraud, stolen information, forged identification and increasingly sophisticated account takeover attacks. However, banks need to ensure they are balancing improved security with customer privacy, accessibility, fairness and following regulations. If implemented correctly, multiple verification methods can be used along with human reviews to improve security. As financial fraud continues to develop, AI will play an important role in helping banks protect their customer’s identities, stop unauthorized access and provide confidence in digital banking.
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