15 May 2024, Nice – AzurSafe, a pioneer in blockchain security solutions powered by AI, today unveiled a groundbreaking advancement in fraud detection technology. The new AI-powered model, dubbed “Fraud Detection 64K”, showcases unprecedented accuracy in differentiating between legitimate and fraudulent activities across Ethereum and other comparable blockchain networks.
Developed by our team, Fraud Detection 64K leverages advanced machine learning algorithms using XGBoost to scrutinize and classify blockchain transactions with unparalleled precision. This model marks a significant leap forward in combating fraud in the digital assets space.
Detailed Performance Metrics:
- Legitimate Transactions: Achieved a precision rate of 90%, a recall of 95%, and an F1-score of 92% across 6,242 validation instances.
- Fraudulent Transactions: Demonstrated a precision of 94%, a recall of 90%, and an F1-score of 92% over 6,149 validation cases.
The model’s overall accuracy stands at 92.10%, with a ROC-AUC of 97.05% and a Precision-Recall AUC of 97.62%, tested against a training dataset comprising 64,098 wallet audits.
Enhanced Security and Operational Efficiency
Fraud Detection 64K not only boosts security but also significantly enhances the operational efficiency of cryptocurrency exchanges and financial platforms. By automating the detection of fraudulent transactions, our model reduces the need for manual verification processes, enabling faster and more reliable transaction processing.
“Our new AI model is set to transform how transactions are monitored on blockchain networks. By providing a tool that can accurately pinpoint fraud based solely on transactional behaviour with such high precision and recall, we are paving the way for safer blockchain transactions. This technology is crucial, especially as digital assets play increasingly central roles in global finance.”
The company’s CEO, Sam Dabiri
Innovative Approach and Model Validation
The integrity and efficacy of our AI model, Fraud Detection 64K, are achieved through ChainWatcher, a proprietary audit solution that meticulously traces the origins of crypto assets. This tool compares transaction data against sanction lists and flagged wallets to determine the legitimacy of assets, providing a solid foundation for our training datasets.
Each dataset encompasses a range of 20 transactional behaviors, captured directly from onchain data, ensuring that our AI model trains on accurate and relevant fraud indicators. This methodical approach to data gathering allows for nuanced understanding and detection of fraudulent activities within Ethereum and similar blockchains.
Development and Validation Process
The development of Fraud Detection 64K began with training on smaller datasets, assessing the model’s initial effectiveness. With each incremental addition of data, there was a corresponding increase in the model’s accuracy, underscoring the reliability and scalability of our approach. This iterative process ensures that each version of the model surpasses the previous one in both precision and adaptability.
The technology behind Fraud Detection 64K has undergone extensive technical audits conducted by independent experts. These audits validate the robustness and effectiveness of the model, ensuring it meets high standards of accuracy and reliability necessary for real-world application.
About Us
AzurSafe is committed to advancing blockchain security technologies through innovative solutions. By leveraging in-depth research and proprietary technologies, we aim to provide state-of-the-art fraud detection tools that enhance the safety and integrity of blockchain transactions.