The Tremendous potential of AI tech and its uses in the DeFi ecosphere.
Today, we will begin our delve into the numerous marvels that will be brought to the forefront of our field thanks to the fusion between DeFi tech and AI tech, with the objective of giving a concrete and extensive analysis of every aspect of said integration.
The Appealing introduction.
The DeFi ecosystem has experienced remarkable growth and innovation in recent years, and by harnessing the power of blockchain technology, the world of DeFi has been able to offer open and permissionless financial services, challenging the traditionally established systems of old, while also creating new intermediaries in the field.
Simultaneously, artificial intelligence has emerged as a game-changing technology across various industries, introducing new levels of automation, prediction, and optimization.
The convergence of AI and DeFi has the potential to revolutionize the financial landscape and shape the future of traditional and digital finance, with a plethora of use cases at every level of the field.
One compelling application of AI in the DeFi ecosystem could and should be the automation of financial processes since AI algorithms can be trained to perform complex tasks such as loan underwriting, risk assessment, and portfolio management, as long as they are properly set up and monitored.
By analyzing vast amounts of data, including transaction histories, credit scores, and market trends, AI-powered systems can make informed decisions quickly and accurately without the need to have a human operator behind the screen at all times, creating something akin to a self-managing investing bot.
This automation not only enhances the efficiency of financial operations but also reduces human bias and error, leading to more reliable and consistent outcomes devoid of risks such as emotional investing, FOMO and many other pitfalls that can be commonly seen in the trading ecosphere.
Furthermore, AI tech can greatly contribute to improving the security and fraud detection capabilities of DeFi platforms which is maybe one of the key points of this merge, simply because as the adoption rate of the DeFi ecosystem continues to grow, so does the importance of safeguarding user assets and preventing malicious activities.
And AI algorithms can do exactly that: by monitoring user behaviour, transaction patterns, and network activities in real-time, identifying potential fraud attempts and suspicious activities can be as easy as installing an API on your website.
By proactively detecting and mitigating fraud, AI-powered systems can enhance the overall security and trustworthiness of the DeFi ecosystem, without the need to employ multi-layered cyber security firms for simple bot detection protections, while also granting the flexibility to empower those protections to safeguard more complex instances of your projects.
Another exciting area where AI can make a significant impact in DeFi is through the optimization of liquidity provision, which is a crucial pillar for the efficient functioning of DEXes CEXes and lending platforms.
Displaying a fearsome level of adaptability, AI algorithms can analyze historical market data, order book dynamics, and user behaviour to optimize liquidity allocation and pricing, once again without the constant need for a human operator directing them.
By dynamically adjusting liquidity pools and token reserves, AI-powered systems can improve liquidity depth, reduce slippage, and enhance price stability, thereby attracting more participants to the DeFi market for any given project.
We can see something similar in the various data analysis tools already provided by some players in the DeFi ecosystem, with a crucial distinction: previously employed tech doesn’t learn, doesn’t offer the same level of scalability, and most crucially, doesn’t cover multiple different pain points at the same time, meaning that new tools need to be made from scratch instead of adapted to the situation.
Going straight to the next advantage AI has the potential to transform the user experience in DeFi by enabling a set of ever-changing personalized financial services.
Since AI algorithms are able to constantly analyse and adapt to individual user data, including financial histories, investment preferences, and risk profiles, they are able to offer tailored recommendations and customized financial products at every step of said user’s financial journey.
This level of personalization can help anyone make better-informed decisions, achieve their financial goals, and ultimately improve their overall financial well-being, without the need for any fancy manoeuvre and especially without the need to thrust shifty operators that are promising you the moon without anything to back them up.
This integration also poses its challenges.
One such roadblock is the availability and quality of data since AI algorithms require vast and diverse datasets to train effectively and produce accurate results.
In the context of DeFi, obtaining reliable and comprehensive datasets can be a complex task due to privacy concerns and the fragmented nature of blockchain data, and as such another significant force that will come to the field in the future is going to be something that is able to gather and employ those data in a stable and efficient manner.
Ensuring access to high-quality data while respecting user privacy will be a critical factor in leveraging the full potential of AI in DeFi, and we will see many projects coming to this field and trying their luck with data management frameworks.
Furthermore, there is a need for transparency and interpretability in AI-powered systems. As AI algorithms become more sophisticated and complex, understanding their decision-making processes becomes crucial, especially if we want to be sure that nobody gets shafted by the implementation of AI tech.
Transparent and interpretable AI models can provide explanations for their predictions and actions, helping users and regulators gain trust in AI-driven financial services, and as previously mentioned this transparency can also assist in identifying and addressing potential biases or ethical concerns that may arise in the use of AI in finance.
The first Layer: Enhancing Risk Assessment and Underwriting in DeFi
Just like the introduction of DeFi tech has revolutionized the financial industry by providing innovative solutions that operate on blockchain networks, enabling users to access various financial services without relying on traditional intermediaries, we will see another such revolution with the introduction of AI tech in the DeFi industry.
One of the ever-looming key challenges in DeFi is assessing the creditworthiness of borrowers in the absence of traditional credit histories, however, the various advancements recently made in our field, and particularly the use of artificial intelligence algorithms, have opened up new possibilities for enhancing risk assessment and underwriting in the DeFi ecosystem.
We already said it in our introduction: AI-powered algorithms have the ability to analyze massive amounts of data from diverse sources, including transaction history, social media profiles, and decentralized identity systems, to build comprehensive borrower profiles.
And by leveraging machine learning techniques, AIs will be able to provide more accurate risk assessment datasets, enabling lenders to make more informed decisions and reducing the likelihood of defaults.
This efficiency, this precision, and this enhanced speed will allow for faster decision-making, enabling borrowers to access funds quickly when they need them the most, and giving investors the chance to take their own opportunities in hand even in the most volatile market periods.
Secondly, AI algorithms have the capability to analyze data from unconventional sources, such as social media profiles, to gather insights about a borrower’s financial behaviour and reputation.
This can provide a more holistic view of the borrower’s creditworthiness, supplementing traditional credit histories that may be absent in DeFi transactions. By incorporating non-traditional data sources, AI algorithms can provide a more comprehensive risk assessment, reducing the reliance on limited data points.
Moreover, AI algorithms can adapt and learn from patterns in historical data, enabling them to identify correlations and trends that may not be apparent to human underwriters and operators.
Additionally, AI algorithms can continuously improve their performance over time through iterative learning, enhancing the accuracy of risk assessments in the long run, if properly set up and maintained.
To implement AI-powered solutions in this particular DeFi niche, developers and financial institutions need to leverage machine learning models that are trained on large and diverse datasets, and while we already have something similar in other markets, these models should be specifically designed from the ground up to handle the unique characteristics of DeFi transactions and consider factors such as smart contract behaviour, transaction history, and user reputation within the decentralized ecosystem if we want them to be the best product they can be for our ecosphere.
However, we need to address some points before going on an investment rampage over anything AI: there are always challenges when integrating tech, and one such challenge is the interpretability and explainability of AI models.
While AI algorithms can provide accurate risk assessments, understanding the underlying factors and reasoning behind the model’s decisions is essential for building trust and regulatory compliance efforts should still be made to develop transparent AI models that clearly explain their risk assessments, allowing stakeholders to understand and verify the decision-making process without leaving everything in the hands of the AI itself.
Furthermore, privacy and security considerations are paramount when dealing with sensitive financial data. Adequate measures must be implemented to protect the confidentiality and integrity of borrower data throughout the entire data-gathering process, while also ensuring that said data won’t be used for anything else.
Compliance with data protection regulations, such as GDPR (General Data Protection Regulation), should be a top priority to ensure AI’s ethical and responsible use in DeFi.
We can safely say that AI-powered algorithms offer immense potential for enhancing risk assessment and underwriting in the DeFi ecosystem and that by analyzing diverse data sources and utilizing machine learning techniques, AI can provide a more accurate overview of your users and clients, enabling lenders to make informed decisions and reducing the likelihood of defaults in this specific DeFi niche.
But we still need to remember how crucial it is to address challenges such as data quality, interpretability, and privacy to ensure the responsible and effective implementation of this tech in our ecosphere simply because while it’s true that by leveraging the power of AI, the DeFi ecosystem has the opportunity to transform the way creditworthiness is assessed and drive further innovation in the financial industry, it’s also true that while improperly used AI technology has the potential to scatter enormous amounts of personal data to the general public, or even worse, to malicious entities able to actually use that data for their personal gain.
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.
