Behavioural biometrics and device intelligence in modern fraud and AML strategiesYour guide to understanding behavioural biometrics, device intelligence, and their use cases.
Guide
Why do behavioural biometrics matter for your business?
With technology continually evolving and emerging risks in fraudulent activities, having strong fraud and Anti-Money Laundering (AML) strategies is more important than ever. We explore why behavioural biometrics and device intelligence are becoming essential tools in the fight against fraud.
Understanding the changing fraud landscape
Over the past few years, we’ve seen a huge increase in first-party fraud due to rising costs of living, like fuel, utilities, and housing. But now, with interest rates starting to drop and people adjusting to higher living costs, first-party fraud is finally starting to decline and return to pre-Covid levels.
On the other hand, third-party fraud is still on the rise and remains a big challenge for everyone. With personal information from data breaches easily available on the dark web and AI making it easier to create fake documents and identities, third-party fraud has become more widespread. Instead of targeting specific individuals, fraudsters are now using a scattergun approach, hitting as many targets as possible.
The impact of regulatory changes
Recent changes from the Payment Systems Regulator (PSR) mean that both the sender and receiver are now equally responsible for reimbursing authorised push payment (APP) fraud. This shift, along with requirements outlined in GPG45, highlights the need for strong fraud detection systems.
Behavioural biometrics and device intelligence go beyond traditional fraud detection methods. These technologies help spot fraud and protect good customers during every online interaction.
What are behavioural biometrics and device intelligence?
From account opening and login to purchases and account management, device intelligence can recognise genuine returning customers while blocking high-risk and unfamiliar devices. For example, it can detect the use of VPNs or proxies, which fraudsters often use to hide their location.
Behavioural biometrics, on the other hand, look at patterns like mouse movements, typing speed, and copy-paste actions. These patterns can reveal suspicious activities, such as the use of stolen identities or automated bots and help identify when a genuine customer is being manipulated by a fraudster. Combining these insights gives a complete view of user interactions, enhancing fraud detection.
Tackling new threats with advanced technologies
The rise of generative AI (GenAI) has brought new challenges in fraud prevention. Fraudsters are using AI tools to create deepfakes and other sophisticated scams. For instance, deepfake technology can bypass selfie liveness checks, posing significant risks to identity verification processes. Behavioural biometrics and device intelligence can help detect these threats by spotting inconsistencies in device usage and user behaviour.
Behavioural biometrics and device intelligence use cases
There are several case studies showing how effective behavioural biometrics and device intelligence can be. One example is a fraud ring using VPNs and device emulators to apply for credit with UK lenders using stolen identities. By analysing attributes like device time zones, network data, and language packs, along with behavioural signals, the fraud can be detected and stopped.
Another example is promotional abuse in the gaming industry, where fraudsters create multiple accounts to exploit sign-up bonuses. Device intelligence helps identify and block these fraudulent activities by detecting common devices used in the sign-up process, even when fraudsters try to manipulate their device data.
Practical steps for implementing these technologies
Implementing behavioural biometrics and device intelligence involves several steps:
- Integration of SDKs: Deploying software development kits (SDKs) on web pages and mobile apps to capture device and behavioural data.
- Data analysis: Using advanced analytics to interpret the captured data and identify suspicious patterns.
- Customisable risk strategies: Configuring risk strategies based on specific business needs and fraud patterns.
- Continuous monitoring: Regularly updating and refining fraud detection mechanisms to stay ahead of emerging threats.
The future of fraud prevention
As fraudsters continue to evolve their tactics, integrating behavioural biometrics and device intelligence into fraud and AML strategies is becoming increasingly important. These technologies provide deeper insights into user interactions, helping businesses protect themselves and their customers from emerging threats. By leveraging advanced analytics and collaborative efforts, organisations can stay ahead of fraudsters while ensuring a secure and seamless customer experience.
The integration of behavioural biometrics and device intelligence represents a significant advancement in the fight against fraud. As businesses continue to navigate the complexities of the digital landscape, these technologies will play a crucial role in safeguarding financial transactions and maintaining trust in the financial system.
How can we help?
At Experian, we advocate for a comprehensive fraud and identity solution that combines traditional methods like email risk scoring, document verification, and fraud consortia matching with advanced technologies such as behavioural biometrics and device intelligence. By integrating these capabilities into a single platform with smart orchestration and machine learning, we can effectively help protect customers, reduce fraud, and support a positive user experience for legitimate customers.
