Verisoul recently showcased their advanced fraud detection capabilities during a live demo, where co-founder Niel demonstrated how they combat fraud in real time using the Chalk platform. Starting with a dataset of 1,000 labeled domains, Niel built three progressively sophisticated detection systems, culminating in a solution that achieved a 96% accuracy rate by leveraging a language model (LLM) for pattern recognition. Verisoul's approach to fraud detection includes three distinct modules: network intelligence, device fingerprinting, and behavioral analysis, with the network module alone comprising over 800 features. Chalk enables rapid iteration by simplifying the integration of various data sources and allowing for immediate testing and deployment, which significantly reduced iteration time from weeks to under an hour. By utilizing Python functions and providing a safe experimentation environment with instant feedback, Chalk facilitates seamless integration of traditional machine learning and AI features, enabling innovative solutions to complex fraud challenges.