Watch the full video: https://youtu.be/C_FJzfj-R0w?si=dAO8tXkWbqkLm3oz&t=123
1. Introduction to the Session
The discussion begins with Martin and Tarma introducing themselves as co-founders of Smart Credit and ChainAware.ai. They shift from their usual Telegram AMA format to a Twitter space to explore the topic of AI and blockchain convergence. The speakers outline that they will discuss AI’s role in enhancing blockchain’s security and usability, particularly through fraud detection and trust-building.
2. Overview of Smart Credit and ChainAware.ai
Smart Credit is introduced as a fixed-term, fixed-rate lending platform in the DeFi space. ChainAware.ai, a derivative product, provides AI-driven fraud detection. The discussion points out that current DeFi platforms mainly use variable-rate, variable-term models, which do not provide the predictability needed for real-world financial systems.
3. Smart Credit: Bridging Real-World Finance and DeFi
In the world of traditional finance, fixed-rate, fixed-term lending is the norm because it provides predictability. However, in the DeFi world, most platforms operate on variable terms, which can be risky. Smart Credit aims to offer fixed-rate and fixed-term loans, making DeFi more predictable and secure. Additionally, a credit scoring system is critical to this model, allowing better evaluation of loan applicants, something variable-rate platforms don’t require as much due to their over-collateralization mechanisms.
4. The Role of AI in Fraud Detection
The speakers highlight the importance of developing their own AI models for credit scoring and fraud detection, distinguishing real AI from platforms that simply use wrappers around ChatGPT. Their fraud detection system, initially only 60-70% accurate, improved over time through model training and iteration. Now, it has reached 98% accuracy in predicting fraudulent behavior based on blockchain transaction histories.
5. Fraud Detection in Blockchain
Traditional financial systems have two main fraud detection systems: AML (Anti-Money Laundering) and transaction monitoring. However, in blockchain, fraud detection is underdeveloped, often focusing solely on AML algorithms. These algorithms are not sufficient in a decentralized, real-time environment where transactions cannot be reversed, unlike traditional finance. AI, through real-time pattern matching, can bridge this gap and detect fraud much faster.
6. ChainAware.ai’s Fraud Detection and Trust Restoration
ChainAware.ai’s fraud detection system leverages AI to predict fraud with 98% accuracy, offering a new level of trust in DeFi platforms. This is crucial for reducing the so-called “hackers fee,” a 2-3% annual loss in total value locked (TVL) caused by hacks and fraud. ChainAware.ai offers free fraud detection tools for the community, allowing anyone to verify addresses before engaging in transactions.
7. Rug Pulls and Real-Time Fraud Detection
The speakers discuss the widespread issue of rug pulls, particularly in ecosystems like PancakeSwap, where pools are created and disappear within hours. ChainAware.ai’s real-time fraud detection can identify these frauds before users fall victim to them. Pre-transaction auditing is critical in preventing fraud and safeguarding users, especially newcomers to blockchain.
8. Building Trust Through AI and Blockchain Integration
Blockchain technology alone cannot guarantee trust between actors, as it only provides an algorithmic solution to ensure trust in the system, not the participants. Using AI, ChainAware.ai enables real-time actor-based trust assessments, identifying which blockchain actors (addresses) are trustable and which are not. This behavioral trust layer adds significant value in decentralized ecosystems.
9. Convergence of AI and Blockchain: Predicting User Behavior
ChainAware.ai’s AI goes beyond fraud detection, using blockchain data to predict user behavior, intentions, and risk profiles. This predictive power is drawn from users’ transaction histories, enabling the AI to forecast whether someone is likely to borrow, lend, or act as a fraudster. This level of prediction was previously used in traditional finance to foresee customer needs, but blockchain data allows even greater accuracy due to its high volume and quality.
10. The Future of AI and Blockchain Convergence
Real AI in blockchain goes beyond simple integrations of ChatGPT or other linguistic models. It involves building and training predictive models to solve real problems, like fraud detection, credit scoring, and predicting user behavior. The speakers conclude that AI is essential for the future growth of blockchain by enhancing security, trust, and user experience. Future developments in Smart Credit and ChainAware.ai include additional blockchain support and more advanced fraud detection capabilities.
This analysis highlights how AI and blockchain convergence can address many of the security and trust issues currently plaguing the DeFi space, while also driving innovation and improving user experiences.
