AI-Driven Fraud Prevention for More Accurate User Profiles & Risk Signals
Today’s fraud landscape is evolving faster than traditional systems can respond, as our CEO, Tamas Kadar, has recently highlighted in TechRadar:
“Thanks to artificial intelligence and machine learning democratizing access to enable fraud at speed and scale, traditional, rules-based systems are proving insufficient."
The new way: AI-driven fraud prevention
AI and machine learning analyze vast datasets beyond human capacity to detect anomalies, uncover hidden connections across devices and networks and adapt in real time, enabling a proactive approach that prevents threats before financial damage occurs.
With these new systems, you can cluster and analyze insights from your historical data to reveal hidden patterns and deliver whitebox machine-learning-suggested rules that you can review, customize and turn on and off with full control.
See how iGaming operator Lottoland achieved 32x return on investment and 190% increase in multi-accounting detection using real-time fraud detection & machine-learning-suggested rules in our case study.
More precise fraud prevention in real time
AI-driven systems rely on rich data signals, ranging from digital footprint analysis and device intelligence to behavioral patterns, in order to create accurate user profiles and assign risk scores with greater precision.
Financial institutions can uncover sophisticated fraud rings by detecting multiple accounts with shared device fingerprints and overlapping transaction histories. Similarly, eCommerce platforms can detect high-risk orders based on behavioral anomalies, reducing chargebacks while maintaining a seamless customer experience.
AI-driven systems don’t just react
AI-driven systems predict potential threats based on emerging patterns. You can intervene faster, reducing the time and resources required to fight fraud and enhancing security and operational efficiency.