Worldline wins AI Tech of the Future Award for PayTech Awards!
16 / 07 / 2024
The rise of cyberattacks and the utilization of AI by fraudsters have created an urgent demand for innovative digital security solutions. According to the Global Fraud Report, in 2023, 42% of banks reported an increase in deepfakes, 26% experienced synthetic identity fraud, and 34% faced account opening fraud. While artificial intelligence poses a threat to our customers, AI technology can also be used for detecting and preventing fraud use cases. Almost a third of respondents plan to invest in newer AI fraud detection solutions such as machine learning and predictive analytics to safeguard their businesses and end users.
In response to these challenges, Worldline has developed the "Digital Security Suite”, offering a device intelligence solution based on AI and machine learning to combat fraud and identity theft for our customers. Our solution has the capability to protect all devices for financial institutions and their users against various types of fraud (including malware, phishing, account takeover, impersonation frauds, etc) during sensitive operations such as authentication, payment, and digitalization of sensitive use cases. In addition, our solution relies solely on device-based intelligence and does not depend on payment habits.
Worldline Digital Security Suite utilizes machine learning to generate three different scores to assess the risk of each operation:
- The Prevention score: this is the first line of defence, an initial check that prevents potential threats from progressing further. It detects whether the device is subject to potential alteration or malware.
- The Biometrics score: this monitors how the user interacts with the device (orientation, movement, keystrokes…) It determines whether we recognise the behaviour of the user.
- The Imprint score: functioning like a digital detective, it observes and records the unique characteristics of the device used and browsing habits. It determines whether we recognise the device of the user.
The combination of these scores provides a comprehensive overview for banks to determine the appropriate response. Our device intelligence scores work regardless of the use case, making it a unique fraud detection solution that adapts and learns to continuously protect devices from the ever-evolving new types of fraud.
Thanks to the use of machine learning and data collection performed on devices, we see a 30% improvement in relevance in cases where a device is being used for the first time with a card, regardless of the number of previous transactions.
Cassandra Neron
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