Global peer-to-peer crypto platform Noones has integrated an AI-based monitoring layer into its escrow system to flag suspicious activity for active transactions. The company announced the development in a blog post on Thursday jobjoining a leading list of crypto platforms integrating AI tools and machine learning models to combat fraudulent activities and their technical sophistication.
Move fraud detection earlier in the transaction lifecycle
Historically, peer-to-peer networks have relied on reputation systems to establish trust between buyers and sellers. Although this system was effective early in the market’s development, these mechanisms become increasingly unreliable as trading volumes and fraudulent 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 rules-based fraud detection systems. The new system deployed by NoOnes evaluates contextual signals at the start of business transactions to mitigate fraud risks and reduce disputes. The model analyzes patterns such as trading behavior, reputation signals, pricing anomalies, payment method inconsistencies, and unusual transaction frequency, and transactions that deviate from typical patterns can be flagged for additional verification before completion.
Initial pilot tests revealed that trade disputes decreased by 28%, while more than 85% of fraudulent transactions were detected early. Improvements have been most visible in markets where alternative payment methods are widely used and in environments where traditional anti-fraud systems may struggle.
AI-based fraud detection is common in the financial sector
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 create fraud detection systems and frameworks for years. Industry data suggests that these AI frameworks can reduce fraud losses and false positives by 40-60% within the first few months of deployment. This is because AI-based systems can respond to new fraud techniques more quickly than traditional systems. For example, anti-fraud technology provider SEON, which uses machine learning models to combat malicious activity, reported that its systems have helped identify or prevent more than $300 billion in fraudulent transactions.
Security vs user experience
One of the challenges of AI-based fraud detection systems is balancing stronger security systems with the speed and flexibility that many users expect from peer-to-peer platforms. Unlike general verification systems that affect and slow down all transactions on the platform, NoOnes adopts an adaptive model that evaluates trading activities dynamically. Low-risk transactions can be carried out without interference or interruption, while higher-risk scenarios are subject to additional checks and can be escalated for further investigation. This type of transaction monitoring is already used in the banking industry, 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 reputations, but as trading volumes and fraudulent schemes increase, reputation systems alone are no longer enough to protect users.
“We are creating a system where trust is based on behavioral analytics and predictive models,” » added Bennett.
AI systems are increasingly seeing new use cases and adoption in transaction analysis, 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 could play an important role in addressing this challenge in the years to come. Building on this momentum, new AI-based tools are being developed to further enhance these capabilities.
NoOnes says the AI deposit system will be rolled out gradually in 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.
