How AI Will Influence DeFi

Introduction

The intersection of two disruptive technologies, Decentralized Finance (DeFi) and Artificial Intelligence (AI), heralds a transformative era in their respective domains. While AI harnesses the power of machine learning and data patterns to emulate human intelligence, DeFi revolutionizes traditional finance through blockchain technology, eliminating intermediaries and enabling peer-to-peer transactions.

This article delves into the imminent impact of AI on DeFi, exploring its potential to reshape interactions within DeFi platforms, mitigate inherent limitations, and fortify the sector against vulnerabilities. From scrutinizing smart contracts for vulnerabilities to enhancing oracle reliability and revolutionizing credit scoring, AI presents a spectrum of opportunities and challenges when integrated into DeFi. Moreover, through in-depth case studies, this article illustrates how pioneering projects are actively integrating AI, offering a glimpse into a future where AI’s augmentation of DeFi is poised to redefine financial landscapes.

What is Artificial Intelligence?

Source: Simplilearn

Artificial Intelligence (AI) is a branch of computer science that develops machines capable of performing tasks associated with human intelligence, by learning from data and recognizing patterns, to make predictions or execute tasks autonomously.

Popular applications of AI are all around us; self-driving cars, chatbots, virtual personal assistants, medical assistant robots, and image recognition systems.

Common techniques used in developing AI systems

Machine Learning

A field of artificial intelligence where algorithms are trained on data to learn patterns and make inferences without explicit programming. It includes supervised learning, unsupervised learning, and reinforcement learning.

Deep Learning

A subset of machine learning that simulates the human brain using neural networks of many layers (deep neural networks). It is commonly applied to hierarchical data representations and speech recognition.

Natural Language Processing (NLP)

NLP allows computers to understand, interpret, and generate human language. It involves tasks such as speech recognition, language translation, and sentiment analysis. NLP is applied to chatbots, language understanding models, and virtual assistants.

Computer Vision

Computer vision trains machines to interpret and make decisions based on visual data. It involves tasks like image recognition, object detection, and image segmentation. Computer vision is used in various applications, including medical imaging analysis, facial recognition, and self-driving cars.

AI Hardware

These are specialized devices that facilitate and accelerate the processing demands of artificial intelligence tasks, such as the Graphics Processing Unit, Tensor Processing Unit, and Neutral Processing Unit.

An Overview of How AI Works

Here’s a simplified analysis of how artificial intelligence is developed.

Data Collection: AI systems rely on vast amounts of data to learn and make informed decisions. This data can be labeled (for supervised learning) or unlabeled (for unsupervised learning).

Training: During training, algorithms use the provided data to identify patterns and relationships. The model adjusts its parameters iteratively to improve performance.

Inference: Once trained, the AI model can make predictions or decisions when presented with new, unseen data. This process is known as inference and is the phase where AI systems demonstrate their learned capabilities.

AI vs Automation

AI is often confused with automation, a popular concept already used in DeFi i.e. in smart contracts. Automated systems lack cognitive abilities. They are rule-based and do not possess the capacity to learn, reason, or understand data beyond predefined instructions. For example, a smart contract will only execute its designed function when the predefined conditions are met. Whereas AI systems can mimic human intelligence, recognize patterns, detect errors, solve problems, and provide evidence-based solutions and explanations while generating results.

Understanding DeFi and its Components

Decentralized Finance, commonly known as DeFi, refers to financial services built on blockchain technology. It integrates services offered by traditional financial institutions, such as savings, borrowing, lending, and more sophisticated activities like asset management and the creation of investment products.

A distinguishing feature of DeFi is its execution through peer-to-peer transactions, facilitated by self-executing codes known as smart contracts.

Unlike conventional banks, the DeFi space operates without intermediaries or central authorities. Transactions within the DeFi ecosystem occur 24/7 in near real-time, and crypto assets can be securely stored on computers, hardware wallets, or other platforms, allowing users flexibility in access.

DeFi aims to be universally accessible, especially to anyone with an internet connection, thus challenging the restrictions prevalent in traditional financial institutions such as cumbersome documentation, delayed settlement time, and geographical barriers.

However, DeFi platforms are susceptible to smart contract exploits and hacking incidents. There is a need for further refinements of the technology in use, to gain user trust and increased adoption.

Key Components of DeFi

Decentralized Exchanges (DEXs)

Think of DEXs as decentralized banks operating on the blockchain. They are platforms that facilitate the peer-to-peer trading of cryptocurrencies. Users are in the custody of their private keys, and liquidity is often provided by participants in the form of liquidity pools and automated market makers (AMM).

Yield Farms and Liquidity Pools

Users can earn by providing liquidity to decentralized exchanges or stake their assets to receive additional tokens or rewards.

Lending and Borrowing

Users can lend and borrow cryptocurrencies without the need for traditional financial intermediaries or discouraging bureaucracy. DeFi also provides flash loans, unsecured loans that are borrowed and repaid within the same transaction, often used for quick arbitrage opportunities.

Oracles

In DeFi, oracles provide external data e.g. price feeds, for the blockchain, enabling smart contracts to react to real-world events.

Essentially, AI can be applied to these and other components of DeFi, affecting how we interact with them. This is further discussed in the next section.

The Influence of AI on DeFi

Artificial intelligence is a tool capable of changing the way we interact with DeFi. AI can be applied to develop new DeFi products, audit smart contracts, verify information provided by oracles, and determine credit scores for lending. While there are potential challenges facing the use of AI in DeFi, the benefits outweigh the limitations. Currently, several DeFi projects are incorporating AI into their services either as a product or foundational part of their technology.

Smart Contract Audit and Automation

Source: ResearchGate — Artificial Intelligence-powered smart contracts can be deployed on a blockchain network in its off-chain mode

Smart contracts operate based on deterministic code and do not possess the ability to learn, adapt, or make decisions beyond their pre-programmed logic.

AI can audit smart contracts for bugs that could compromise their function, ensuring that the code is secure and resistant to exploits.

NLP (Natural language processing) algorithms can be employed to analyze audit reports, documentation, and comments related to the smart contract.

Before deploying a smart contract, a pattern recognition algorithm can identify patterns associated with common coding errors, such as buffer overflows and re-entrancy issues. Execution of smart contracts can be optimized leading to more efficient transactions within decentralized applications (DApps).

Anomaly Detection in Oracles

Oracles are third-party services that enable smart contracts to access off-chain data that are capable of influencing their execution on-chain. Essentially, oracle is responsible for querying, verifying, and authenticating external data before relaying it to the blockchain.

Given that smart contract outcomes rely on the accuracy of Oracle-provided data, ensuring its reliability is paramount. Inaccurate data can lead to irreversible smart contract executions, resulting in a permanent loss of user funds due to the automatic and immutable nature of blockchain transactions.

To enhance the integrity of data processed by oracles, various AI techniques can be employed such as generative adversarial networks (GANs), Isolation Forests, Local Outlier Factors, etc. These techniques can identify irregular patterns or outliers within datasets.

Hypothetically, an AI model would assist in detecting anomalous behavior in data aggregated by oracles from different sources. The Oracle network can then scrutinize these anomalies, taking corrective actions before relaying the data to the Blockchain.

Credit Scoring

To assess the creditworthiness of users in DeFi lending protocols. AI-based credit scoring can use machine learning algorithms to analyze transaction history and other necessary data points.

Fraud Detection

The decentralized system is at a higher risk of fraud due to the relative anonymity of users. Fake exchange trading volume or the suspicious transfer of liquidity, for instance, can be targeted for identification using data analysis techniques.

Novel Product Offerings

The advent of AI will open a new market of projects that apply AI in their product offerings. For example, adopting AI-powered trading tools for sale or hire by yPredict, Fetch.ai. More creative use cases for AI will be explored as the technology develops.

Predictive Analytics for Automated Trading

Data is an integral part of DeFi, and while there are numerous data sources, processing them to make profitable decisions can be tasking.

Predictive analytics, using data mining, statistics, and machine learning to make more informed decisions, can analyze past market trends to predict what will happen in the future. They can be coupled with AI trading bots that optimize strategies, execute trades, and manage portfolios more efficiently—minimizing losses and increasing liquidity.

Predictive analytics can also be employed to dynamically manage DeFi portfolios. Algorithms can continuously analyze market conditions and adjust the composition of a portfolio in real-time, ensuring it aligns with the predicted market trends.

Case Studies of DeFi Projects Integrating AI Technology

This section highlights projects that have integrated AI into their functionality.

Cortex

Source: Cortex

Cortex is an open-source public blockchain designed to incorporate machine learning capabilities into smart contracts and decentralized applications (DApps). By addressing the challenge of on-chain AI execution, developers can combine the Solidity language with off-the-shelf AI models on the Cortex storage layer to create AI-enhanced DApps and smart contracts.

Injective

Source: Injective

Injective is a Cosmos-based blockchain that combines elements of artificial intelligence (AI) with decentralized finance (DeFi). DApps built on Injective can employ AI algorithms, improving market efficiency and optimizing decision-making processes, particularly in decentralized exchanges. Injective claims to be the pioneer in providing “auto-executing smart contracts”.

Dune AI

Dune Analytics, a blockchain analytics tool, developed Dune AI to simplify the extraction of crypto data queries. Using a natural language processing engine similar to OpenAI’s ChatGPT4, Dune AI will give users access to crypto-related data using chat functionalities without having to learn SQL commands.

yPredict

Source: yPredict

A Polygon-based decentralized marketplace and trading platform that provides traders and investors access to dozens of AI-powered signals, breakouts, pattern recognition, and social/news sentiment features. Expanding its scope beyond trading, it has developed two content creation tools, a Backlink Calculator and a Writing Assistant.

Every model submitted by AI engineers will be verified by DAO members before it is offered on the platform for a subscription. yPredict runs a tier-based business model, where tools and services are offered at different levels, each with its own pricing and set of features. This approach allows for inclusivity, catering to both high-end traders and those just starting.

RociFi

Source: RociFi

RociFi is a credit-scoring, under-collateralized, capital-efficient lending protocol that uses on-chain data, machine learning, and decentralized identity data points, including social media accounts, participation in decentralized autonomous organizations (DAOs), and ownership of non-fungible tokens (NFTs).

Fetch.ai

Source: Fetch.ai

Fetch.ai focuses on applications related to decentralized finance, transportation, energy management, and various business tasks. This platform empowers developers to integrate artificial intelligence into their applications for more efficient and intelligent automation.

Potential Challenges

On-Chain Deployment

Deploying complex AI models directly on-chain can be resource-intensive, leading to scalability challenges and higher gas fees. Many AI operations involve significant computational power, which may not align with the constraints and costs associated with on-chain execution. Also, storing large AI models and datasets on-chain could pose challenges due to the storage limitations of blockchain networks.

Security Risks

AI tools are often created by centralized entities unless they’re open-source, these tools can be a point of attack if their security features are compromised.

Centralization

DeFi projects that choose to rely on centralized AI services are at risk if these services experience outages or changes in policies.

Paucity of Data

The success of AI largely relies on being trained with vast data sets for efficiency and accuracy. Decentralized finance, still in its early stages, may need more data for AI models to function effectively. Skewed data can produce biased algorithms producing inaccurate credit scores, bad loans, etc.

Conclusion

The fusion of AI and DeFi is a transformative union of innovative technologies, reshaping the financial landscape. AI brings intelligent tools to optimize DeFi, from securing smart contracts to predicting market trends. While challenges like data scarcity and centralized dependencies exist, pioneering projects like Cortex and yPredict showcase the vast potential. As AI matures and DeFi ecosystems grow, this symbiotic union promises to democratize finance, unlock innovative products, and usher in a future where decentralized intelligence fuels financial freedom.