In today’s rapidly evolving digital landscape, the threat of digital fraud looms large over all businesses. It is now essential for organizations to have a purposeful approach to fraud prevention that doesn’t add unnecessary friction for valid customers. It’s about striking the right balance between security and user experience. This blog examines how to use machine learning technology in fraud detection and prevention, and how Telesign can help organizations protect their business processes from fraudsters while ensuring a low-friction user experience.
Fraud in the digital age
Digitalization has fundamentally transformed how we carry out everyday transactions, enabling us to conduct them anytime and anywhere. On the other hand, it has also increased the likelihood of threats from bad actors who exploit control gaps in digital applications, impersonate real customers, and rack up costly transactions on their behalf, which result in financial losses.
Organizations need to pay close attention to digital fraud as it also affects their brand value and reputation. Many have learned their lessons from the past and seem to understand the importance of detecting fraudulent activity in real time. However, scammers are also getting smarter by the day, and continued focus is required to prevent fraud and stay ahead of malicious actors. It is important to put in place techniques that monitor key patterns that could help differentiate a real transaction from a fraudulent one. This is how machine learning (ML), a branch of artificial intelligence (AI), plays a pivotal role in fraud pattern detection.
How machine learning helps prevent fraud
ML is increasingly used in e-Commerce businesses, apps, online services, and even government agencies to detect and prevent sophisticated, often automated attacks, that threaten to damage infrastructures and steal data, goods, and funds. In order to detect fraudulent activity, machine learning models are trained with historical fraud data (attack attempts, sources, methods, etc.). ML algorithms are used to recognize patterns in a historical dataset, and then dynamically change a solution’s security rules to prevent future fraud attempts—even those that use methods that have never been seen before.
ML is among the best responses to the evolving nature of online threats, giving users a massive advantage in the fight against onboarding fraud, fake account creation, account takeovers (ATOs), and credential stuffing. Supported by 15+ years of historical data patterns and backing analytics, Telesign’s machine learning algorithms deliver continuous performance improvement that grows and adapts to your business, enabling you to:
- Understand the risk of every interaction.
- Receive a dynamic risk-based assessment score with prioritized, actionable reason codes.
- Allow, block, or flag a user’s interaction in a matter of milliseconds.
To learn more about how your business can verify that you’re dealing with real users and not fraudsters, talk with the experts at Telesign today.