Today we will be giving a deeper look at two specific fields that can be enormously improved by the introduction of AI technology.
Market Making? Liquidity Provision? Headaches of the past.
DEXes and CEXes exist as huge hubs of trade within the DeFi ecosystem, and for them and their customers, liquidity is a vital component that enables efficient trading and ensures a seamless user experience.
Traditionally, market makers play a crucial role in providing liquidity by constantly quoting bids and asking prices for different tokens. However, with the emergence of AI technologies, automated market making has gained significant attention due to its ability to optimize liquidity provision in a dynamic and efficient manner.
AI algorithms can revolutionise liquidity provision in DEXes and CEXes by leveraging vast amounts of data and real-time market conditions constantly, with automated updates, multi-functional integrations and much more.
By analyzing order book data, including the depth and distribution of buy and sell orders, AI-powered systems can dynamically adjust token prices to match supply and demand, effectively maintaining equilibrium in the market, and those algorithms can also take into account historical trading patterns and external market factors, such as news events or market sentiment, to make more informed pricing decisions.
One of the key benefits of AI-driven liquidity provision is the reduction of slippage for traders and with slippage referring to the difference between the expected price of a trade and the actual executed price, this is a huge deal for many of the most notable exchanges since higher slippage can occur especially when executing large or illiquid orders.
AI algorithms can optimize token prices in real-time, ensuring that traders receive the best possible execution price by minimizing slippage, going to solve a problem that has plagued this particular ecosphere since its inception, improving trading efficiency and enhancing the overall trading experience.
Furthermore, AI-powered market-making strategies can adapt to changing market conditions quickly, since they can respond to sudden shifts in supply and demand, volatility spikes, or liquidity imbalances, ensuring continuous liquidity provision even during times of market turbulence.
This adaptability is essential in the fast-paced and constantly evolving DeFi ecosystem, where market conditions can change rapidly, and can be a lifesaver for many in periods of higher volatility.
Additionally, AI algorithms can optimize liquidity allocation across different token pairs in DEXs, allowing for faster and more efficient transactions.
By analyzing historical trading volumes, token correlations, and user preferences, these algorithms can intelligently allocate liquidity to the most active and in-demand token pairs, and such an optimization not only maximizes the utilization of available liquidity but also enhances the overall market depth and stability for all trading pairs.
It’s worth noting that the integration of AI in automated market-making and liquidity provision also introduces certain challenges to the table.
The accurate and timely availability of market data is crucial for AI algorithms to make informed decisions, and this is something that we already saw and that we will keep seeing across the board in regard to the use of AI integrations.
Moreover, maintaining the privacy and security of sensitive trading information while utilizing AI algorithms requires careful consideration and robust data protection measures.
We can still see how AI algorithms have the potential to revolutionize how decentralized and centralized exchanges functions within the DeFi ecosystem: analyzing order book data, historical trading patterns, and external market factors, AI-powered systems can optimize token prices, reduce slippage, and enhance overall market liquidity, providing the best possible experience for both sides of the equation.
Another Problem, Another Solution.
Safeguarding your users in this field can be quite difficult, and security is one of the pillars on which all DeFi projects should be built.
In particular smart contracts play a fundamental role in the decentralized finance ecosystem, enabling automated and trustless execution of financial transactions and thus, smart contracts security should be one of the main priorities of any project interested in making it far in the DeFi world.
However, the complexity of smart contracts and the potential for human error in their development can introduce vulnerabilities that may be exploited by malicious actors, and the consequences of smart contract vulnerabilities can be severe, resulting in financial losses for users and undermining the overall confidence in DeFi applications.
To mitigate these risks, the integration of artificial intelligence (AI) in the analysis and auditing of smart contracts has emerged as a very promising solution.
AI-based tools can leverage machine learning algorithms to detect potential security flaws and vulnerabilities in smart contracts, enhancing the overall robustness and security of DeFi applications.
One of the key advantages of AI-based smart contract analysis is its ability to learn from past audits and continuously improve its detection capabilities.
By analyzing a vast number of smart contracts, AI algorithms can identify patterns and common vulnerabilities that may be overlooked by manual auditing processes, and can therefore quickly patch them way sooner than a human operator can.
As AI systems encounter new types of vulnerabilities, they can adapt and update their detection algorithms to stay ahead of emerging threats, going as far as preventing threats way before the first breach can even happen.
Machine learning techniques, such as anomaly detection and pattern recognition, can be utilized by AI tools to identify potential security risks in smart contracts, and these algorithms can analyze the code structure, transaction flows, and potential attack vectors within smart contracts to identify suspicious or potentially harmful patterns.
AI-based auditing can also consider the context in which the smart contract operates, taking into account the interaction with external systems and potential sources of vulnerabilities.
Moreover, AI-powered smart contract analysis can significantly reduce the time and effort required for auditing, since traditional manual auditing processes can be time-consuming and resource-intensive, often leading to delays in identifying and resolving vulnerabilities.
AI algorithms, on the other hand, can automate the analysis process, rapidly scanning and evaluating smart contracts for potential security issues this not only improves the efficiency of auditing but also enables faster deployment of secure DeFi applications, allowing users to enact all of their transaction with confidence, and allowing projects to spend more time on building the core of their projects.
By integrating AI-based auditing tools into the DeFi ecosystem, developers and users can benefit from proactive security measures such as specifically tailored AI algorithms that can provide real-time monitoring and analysis of smart contracts, detecting and alerting users to potential vulnerabilities as they arise.
This proactive approach minimizes the risk of exploitation and allows developers to take prompt action to rectify identified vulnerabilities.
While AI-based smart contract analysis offers significant advantages, it is important to acknowledge the limitations and challenges that come with its implementation.
Once again, we see how AI algorithms heavily rely on the quality and diversity of training data to effectively detect vulnerabilities.
Therefore, ensuring the availability of comprehensive and up-to-date data sets for training AI models will be crucial in the future, and we can see many projects already branching in this field at the time I’m writing this article.
Additionally, maintaining the security and privacy of smart contract code and sensitive transaction information is of utmost importance when utilizing AI in the auditing process, even more than in previously mentioned AI applications, since this time we are talking about directly accessing users’ funds.
Robust security measures must be implemented to safeguard against potential attacks targeting AI systems and the confidential information they process.
We can safely say that the integration of AI-based tools in the analysis and auditing of smart contracts presents a promising avenue for enhancing the security and robustness of DeFi applications.
By leveraging machine learning techniques, AI algorithms can continuously learn from past audits, detect vulnerabilities, and improve the overall resilience of smart contracts.
The ability of AI to automate the auditing process and provide real-time monitoring offers developers and users a proactive approach to addressing potential security risks.
However, it is essential to address challenges related to data availability and security to ensure the responsible and effective integration of AI in smart contract analysis within the DeFi ecosystem, and those pain points are something that we will keep in mind very carefully while auditing possible projects in the future, to guarantee that all the Synapse Users are well protected from faulty iterations of this tech.
About Synapse Network
Synapse Network is developing a cross-chain investment and start-up acceleration ecosystem based on blockchain technology to give everybody an equal chance to contribute to great upcoming projects and to do so early on. We are bridging the gap between the traditional & crypto market. The idea of the Synapse Network technology goes beyond the standard offer of launchpads available on the market, becoming a true technological brand providing tech solutions.