Global peer-to-peer crypto platform Noones has integrated an AI-based monitoring layer into its escrow system to flag suspicious activity for active trades. The company announced the development in a Thursday blog post, joining a high-ranking list of crypto platforms integrating AI tools and machine learning models to tackle fraudulent activities and their technical sophistication.
Moving Fraud Detection Earlier in the Transaction Lifecycle
Historically, peer-to-peer networks have relied on reputation systems to establish trust between buyers and sellers. While this system was effective during the early stages of market development, these mechanisms are becoming less reliable as trading volumes and fraud schemes increase.
Technological advancements, fraud sophistication, and compliance requirements have forced the financial industry to increasingly turn to predictive analytics and AI tools to replace traditional rule-based fraud detection systems. The new system deployed by NoOnes evaluates contextual signals at the beginning of trade transactions to mitigate fraud risks and reduce disputes. The model analyzes patterns such as trading behavior, reputational signals, pricing anomalies, payment-method inconsistencies, and unusual transaction frequency, and trades that deviate from typical patterns may be flagged for additional verification before completion.
The initial pilot testing revealed that trade disputes dropped by 28%, while over 85% of fraudulent transactions were detected early. Improvements were most visible in markets where alternative payment methods are widely used and environments where traditional anti-fraud systems can struggle.
AI-based Fraud Detection is Common in the Finance Industry
The use of AI to identify and mitigate fraudulent transactions is not new to the financial industry. Banks, payment processors, and e-commerce companies have been adopting machine-learning techniques to build fraud detection systems and frameworks for years. Industry data suggest that these AI frameworks can reduce fraud losses and false positives by 40-60% within the first few months of their deployment. This is because AI-based systems can react to new fraud techniques more quickly than traditional ones. For instance, anti-fraud technology provider SEON, which uses machine learning models to combat malicious activities, reported that its systems have helped to identify or prevent more than $300 billion in fraudulent transactions.
Security vs User Experience
One of the challenges of AI-driven fraud detection systems is balancing stronger security systems with the speed and flexibility that many users expect from peer-to-peer platforms. Unlike blanket verification systems that affect and slow down all platform transactions, NoOnes is adopting an adaptive model that evaluates trading activities dynamically. Low-risk transactions can be conducted without interference and interruption, while higher-risk scenarios receive additional checks and can be escalated for further investigation. This kind of transaction monitoring is already used in the banking sector, where adaptive security models have helped reduce user complaints by more than 30% while maintaining stronger fraud detection. According to Michael Bennett, Head of Market Intelligence at NoOnes, the P2P market has historically been built on strong user reputation, but as trading volumes and fraudulent schemes increase, reputational systems alone are no longer sufficient for user protection.
“We are creating a system where trust is built on behavioral analytics and predictive models”, Bennett added.
AI systems are increasingly seeing new use cases and adoption in trading analytics, market surveillance, compliance monitoring, and fraud detection as crypto platforms continue to face the challenge of maintaining security without compromising service accessibility. Systems that can identify suspicious activity during the transaction lifecycle may play an important role in addressing this challenge in the coming years. Building on this momentum, new AI-powered tools are being developed to further enhance these capabilities.
NoOnes says the AI escrow system will be rolled out gradually across key markets over the coming months. Future iterations are expected to incorporate additional risk-scoring models and explainable AI tools designed to provide clearer reasoning behind risk assessment for complex transactions.
