Fraud Prevention: Behavioural Analytics and Behavioural Biometrics in Risk Mitigation

Understanding GPG 45

Behavioural analytics and behavioural biometrics are powerful tools organisations can use to identify risk and tackle fraud as part of their wider fraud prevention strategies. However, despite often being grouped in the same category, the two solutions are inherently different. 

Behavioural analytics collects and analyses user behaviour patterns that may indicate fraudulent activity, whereas behavioural biometrics verifies a user is who they say they are based on how they interact with their device compared to their past performance. 

Given these differences, it is perhaps unsurprising that these two solutions offer varying benefits and results. Where behavioural analytics is effective at detecting risky behaviour at scale, particularly at the top of the customer application funnel, behavioural biometrics is useful for detecting fraud on an individual level. However, this technology is reliant on customers having previously provided more personal data, such as typing patterns or voice recordings, which can carry regulatory implications and create a longer time-to-value for each customer. 

This raises a key question for many fraud prevention leaders: Which fraud detection tool is right for my organisation’s fraud prevention strategy? In this blog, we’ll explore the roles of behavioural analytics and behavioural biometrics in risk mitigation, the differences between the two solutions, and how they can impact on customer experience.

What is behavioural biometrics? 

Behavioural biometrics is a type of biometric authentication that measures unique patterns in human activity. This data generates a baseline of normal behaviour for each user. Any deviations from the baseline can then later be flagged as suspicious and investigated further. Examples of behavioural biometrics include keystroke, signature and voice data. 

Challenges associated with using behavioural biometrics:

  • Can be less accurate than other biometric authentication methods because behavioural biometrics is based on patterns in human activity — which can be impacted by factors such as fatigue, stress, and environmental conditions.
  • Can be more costly to implement than other solutions because behavioural biometrics requires software (and potentially hardware) that can collect and analyse behavioural data.
  • Usually has a higher time-to-value, as the technology authenticates a user by comparing their behaviours to their own past behaviours rather than a crowd of users. This means there will be a training period to build an individual profile, and a period where the solution cannot make a determination about a user at all, leading to a period of higher risk. 
  • Biometric data requires explicit consent for processing. The EU's General Data Protection Regulation (GDPR) classifiies biometric data as sensitive personal information requiring explicit consent for its processing — including for identity verification and fraud prevention — this can lead to increased operational costs and negatively impact on the customer onboarding journey.

What is behavioural analytics? 

Behavioural analytics connects a user’s behaviour to their intention – not their identity. Unlike behavioural biometrics, it does not need the history of use from the person or the device. Instead, the solution helps to detect genuine from risky behaviour based on independent session engagement.

Behavioural data is collected while a user engages with a website or digital application. Instead of capturing the specific information entered into form fields, behavioural analytics focuses on how the user interacts with the form. It analyses factors like entry methods (autofill, typing, etc.), edits, fluency, timing, transition patterns and click rate. All of which can help your organisation eliminate fraudulent applicants before they even hit submit.

What are the benefits of behavioural analytics?

  • Behavioural analytics does not require user profiles or personal data. It uses independent session engagement to distinguish between genuine and risky behaviour without storing personal data.  

  • Behavioural analytics does not require a tuning period, providing instant value. By taking a neuroscience-based approach to security, behavioural analytics can begin detecting risky behaviour instantly after installation, accurately identifying low-risk users and detecting potential risks that biometric data may miss.  

  • Helps protect against bots. Because behavioural analytics analyses user behaviour, it can quickly detect and reject bot activity that may otherwise have gone undetected. 

Comparing behavioural analytics and biometrics

Behavioural biometrics and behavioural analytics are two distinct types of identity technology that have evolved over time to satisfy the need for secure digital interactions in different ways. Where behavioural biometrics stores biometric data and ties it back to one user, behavioural analytics tracks this data to reveal insights and predict risk. 

To further illustrate the key distinctions between these solutions, the table below breaks down what behavioural analytics is and isn’t, the regulations that determine where the differences lie, and how to better evaluate the use of behavioural analytics and biometrics within your digital identity fraud stack. 

 

Physiological biometricsBehavioural biometricsBehavioural analytics

 

Data type

Collects physiological characteristics like fingerprints or facial scans.

Collects behavioural characteristics such as a customer’s voice and signature. 

Collects and analyses user behaviour patterns, such as keystroke dynamics, mouse movements, and browsing patterns.

Authentication 

Verifies identity based on physical traits, often using liveness tests.

Verifies identity based on how they interact with their device compared to their past performance. 

Detects genuine from risky behaviour based on independent session engagement. This works alongside traditional verification methods as an additional layer of security.

Privacy implications 

High. Biometric data is sensitive and personally identifiable. Its collection and storage are highly regulated.

Low. Behavioural analytics technology collects data entry pattern information—anonymized—not physical traits, which makes it non-intrusive.

Regulations

Under GDPR, biometric data is classified as “special category data” whenever it is processed for the purpose of uniquely identifying a natural person. As a result, additional controls are needed for the collection and processing of biometric data.  

Limited and often with fraud exceptions. 

 

Introducing TruValidate Device Risk with Behavioural Analytics Solution

TruValidate™ Device Risk with Behavioural Analytics is a powerful new enhancement within the Device Risk solution. Leveraging device history, device-to-device and device-to-account associations based on confirmed fraud reports from our global network alongside crowd-level behavioural insights, powered by NeuroID, the solution is designed to help organisations discern between risky transactions and trusted digital connections using device intelligence and behaviour technology. 

The solution empowers organisations to capture insights in real –time – based on our platform’s knowledge of billions of devices and transactions and NeuroID;s one trillion behavioural signals – to help reduce false positives.

Enhanced fraud detection capabilites will give businesses the power to: 

  • Streamline transactions: Reduce false positives and manual reviews to exceed customer expectations and increase loyalty. 

  • Increase insight at top of funnel: Detect risky behaviours earlier in the customer journey. 

  • Detect hidden connections: Identify links between suspicious users, devices and transactions to outsmart fraudsters and help stop fraud rings. 

  • Proactively monitor threats: Help prevent fraud before it starts with a real-time threat level indicator.

  • Prevent fraud in real-time: Identify devices with evasive behaviours, risky attributes or a history of fraud the moment they connect with your digital platform. 

  • Enhance the user experience for genuine customers: Streamline digital applications, help reduce basket abandonment rates, and confidently onboard new customers with non-intrusive, reliable device and behavioural insights and information.

Sixty percent of UK consumers stated that ease of filling out forms and applications was “very important” when choosing who to transact with online. How can you enhance your organisation’s fraud controls without negatively impacting on the customer experience? Find out more in our latest blog.

 

Which types of fraud can Device Risk with Behavioural Analytics help to prevent?

In 2022, more than £1.2 billion was stolen from UK consumers through fraud. Using Device Risk with Behavioural Analytics, companies can better detect over 50 types of fraud risks, including:  

  1. Identity theft: This occurs when an individual exploits someone else's personal information to open accounts, make purchases, or receive services by posing as the victim. Behavioural analytics identifies anomalies in customer data that highlight potential identity theft.

  2. Money laundering: This involves disguising money obtained from illegal activities as legitimate funds within the financial system. Behavioural analytics compares normal consumer behaviour patterns with suspicious individuals or entities, which can detect potential money laundering. 

  3. Synthetic identities: A combination of fabricated credentials where the implied identity is not associated with a real person. Fraudsters may create synthetic identities using potentially valid details alongside false personally identifiable information (PII). 

  4. Payment fraud: This type of financial fraud occurs when a fraudsters intentionally uses false or stolen payment information to make a purchase.  

  5. Promotion abuse: Also known as bonus abuse, this type of online fraud involves customers taking advantage of a business’s promotional offers. This can be achieved through disposable email addresses and phone numbers, compromised names and sometimes stolen debit card details.

     

How Behavioural Analytics enhances the Device Risk solution to improve fraud detection

The TruValidate Device Risk solution tracks relationships between devices and accounts by leveraging device history and confirmed fraud reports from our global network of fraud analysts.  

Behavioural analytics, on the other hand, applies patented neuroscience technology to measure how familiar users are with the personal information they are providing during an online application. By analysing this data in real-time, they can help inform if the applicant’s intentions are genuine or deceptive without adding any unnecessary friction for genuine customers.  

By combining these two factors, Device Risk with Behavioural Analytics provides organisations with stronger signals for more confident decisioning. The table below compares the key enhancements behavioural analytics can offer: 

Device Risk 

Device Risk with Behavioural Analytics 

 

  • The Device Risk solution captures powerful device insights, which include:
    • Detecting anomalies in device usage like evasion attempts and time-zone mismatches. 
    • Assessing device location data like geolocation, past experiences and the true device location. 
    • Assessing device reputation such as suspicious history and returning customers. 
    • Tracking device velocity. 
  • The detection of fraud is managed from a friction-right perspective. 

 

  • Gain additional insights into the user’s behaviour behind the device.
  • Combining solutions delivers: 
    • Configurable identification of risky behaviour alongside risky device activity, providing deeper insight and higher confidence in fraud capture.  
    • Reduced false positive rates from strengthened insights, enabling more good customers to onboard.  
    • Combined insight and recommendations assessed through one API.  
  • This is all done with predictive insights available from day one — without any additional customer friction.  

For more information on the differences between Device Risk and Device Risk with Behavioural Analytics, click here.  

Why TransUnion?

TransUnion is a global information and insights company that makes trust possible in the modern economy. We do this by providing a comprehensive picture of each individual so they can be reliably and safely represented in the marketplace.

Our TruValidate suite of fraud and identity solutions helps organisations from multiple sectors to confidently identify genuine consumers and engage with them securely at each stage of the customer journey. Drawing on our identity, device and behavioural insights, we enable businesses to discover anomalies, assess risks and make more informed decisions so they can drive conversions, reduce fraud losses, and deliver enhanced, friction-right user experiences. 

For more information on TruValidate Device Risk with Behavioural Analytics solution and how it can enhance your fraud prevention strategy, contact a member of our team.

If you’re a consumer with questions or issues related to your personal credit report, drivers history report, disputes, fraud, identity theft, credit report freeze or credit monitoring services, please visit our Customer Enquiries page for assistance.

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