In our new blog post, we explain what DeFi AI, how it works, what benefits it gives, and how your financial business can throw through DeFi AI implementation.
For centuries, financial institutions have relied on traditional financial systems and centralized intermediaries like banks, but the inception of Decentralized Finance (DeFi) has changed the game. DeFi is a system that works on blockchain and uses smart contracts with pre-set conditions to perform various complex peer-to-peer financial transactions autonomously. In 2024, the worldwide DeFi market was valued at $20.48 billion, and this number is expected to surpass the $230 billion mark, growing at a compound growth rate of 53.7% in the forecast period.
DeFi has already started reinventing the sphere of finance, enabling more transparency and autonomy. Underpinned by AI (DeFi AI or DeFAI) this tech can give even more, supercharging your business.
What is DeFAI and how it works
The convergence of DeFi and AI, DeFAI represents a financial ecosystem in which autonomous AI agents perform complicated transactions through a number of steps:
- Data collection and analysis. AI agents collect relevant real-time data from blockchain networks (market prices, liquidity pools, trading volumes, token balances, smart contract states, etc.) and external sources (social media posts, news, trend dynamics, user sentiments). The gathered data is analyzed with the help of advanced machine learning models for identification of market patterns, trends, risks, anomalies, and opportunities.
- Decision-making and execution. Underpinned by this analysis, an AI agent builds the optimal course of action for a particular task and executes its decision, interacting directly with DeFi protocols and smart contracts. For instance, for the task of maximizing yield, the agent will take into account risk-adjusted return, liquidity and withdrawal constraints, as well as gas costs for moving funds to split funds across protocols.
- Monitoring and adaptation. After executing the task, AI agents continuously analyze the market to dynamically adapt their strategies, when needed. They also evaluate the outcome of their operations to optimize strategies over time.
DeFAI benefits for the financial industry
The collision of AI and DeFi benefits all the actors in the DeFi ecosystem, including financial institutions, fintech businesses, and end users. Here’re some of the key perks:
- Intelligent automation. AI agents don’t need human intervention to perform complex financial processes, including managing loans, rebalancing portfolios, and executing trades. This is done based on real-time data analysis through smart contracts and ML algorithms.
- Enhanced risk management. Thanks to its automated nature, AI agents can continuously monitor platform activities and market conditions to detect potential risks and fraudulent activities. This results in more secure trade execution, reduced losses, and users’ asset protection.
- Boosted operational efficiency is reached thanks to reduced human error, faster task execution, and better scalability. As a result, you get more reliability and accuracy, while lowering operational costs.
- Better user accessibility. Fueled by powerful analysis capabilities, AI agents can give users tailored advice and personalized financial services and products. This and 24/7 availability make DeFAI accessible to a broader audience, promoting financial inclusion globally.
Harness the power of AI to
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How AI is changing DeFi: Top-4 use cases
DeFAI platforms are revolutionizing the financial landscape in a number of ways.
Intelligent trade execution
The profitability of token swaps depends heavily on price predictions across multiple decentralized exchange (DEX) systems. Here is where AI systems come into play. AI agents continuously monitor and analyze historical and real-time market data, including:
- Price feeds from multiple DEXs
- Token reserves and liquidity pool indicators
- Transaction volumes and slippage metrics
- Market volatility dynamics
- Gas fees and network congestion
Then, this data is used to build the input for predictive machine learning models. By leveraging reinforcement learning, time-series forecasting (ARIMA, LSTM neural networks), and other AI algorithms, AI agents predict:
- Short-term price movements
- Price arbitrage opportunities
- Slippage levels for different trade sizes
With such predictions at hand, AI agents build the optimal route for executing a trade through:
- Analysis of multiple paths across different DEXs, factoring in liquidity depth, fees, and predicted prices
- Trade splitting (deciding trades into smaller chunks) might take place to minimize market impact
- Timing of trade executions might differ based on predicted price movements
After that, agents automatically execute trades through smart contracts or APIs, while dynamically adjusting decisions when needed. As a result, such a process allows avoiding unfavorable trades, minimizing transaction fees and gas costs, increasing profits, as well as enabling utmost speed and scalability.
Personalized swap optimization
As we’ve mentioned in the previous section, executing swaps at optimal times and prices is made easy with DeFAI. On top of that smart trade execution, you can also use AI agents for more tailored optimization.
For example, a user wants to swap Token A on Ethereum for Token B on Binance Smart Chain. AI models analyze market data (network congestion, fees, market sentiments, and liquidity) and personal data like user profiles, trading history, risk appetite, and market preferences. Based on this analysis, agents then provide individual users with personalized swap strategies, including the best swap path, the optimal amount, and favorable timing.
Users who don’t want much risk, will be offered swaps that prioritize stability, while more aggressive traders might receive options that emphasize higher returns with higher risks. As a result, every user gets the maximum value at the most favorable and preferable conditions, leading to high satisfaction and engagement.
Liquidity provision optimization
To maximize pool profitability for liquidity providers, at the same time balancing risks, AI agents perform a number of tasks in the DeFAI environment:
- Data collection and analysis. To build insights around the optimal liquidity deployment, DeFAI platforms gather and analyze huge volumes of on-chain and off-chain data such as trading volumes, price volatility, token correlations, and market trends.
- Execution. Powered by AI algorithms, DeFAI dynamically adjusts liquidity allocation across multiple pools / trading pairs based on the previously analyzed data and real-time market conditions. Thus, providers form liquidity where it is most profitable and least risky.
- Rebalancing. Through smart contract pairing and market movements analysis, DeFAI automates liquidity positions rebalancing, re-allocating underperforming assets to more profitable opportunities. AI agents can also analyze fee structures, reward mechanisms, and liquidity incentives to propose the most favorable participation levels and strategies.
- Risk management. ML-fueled predictive analytics allows liquidity providers to proactively adjust their positions or withdraw liquidity before significant divergence happens, thus minimizing the risk of impermanent loss. AI agents can also simulate various market scenarios to forecast the potential outcomes of various liquidity strategies, helping improve decision-making. On top of that, when unusual trading patterns or manipulative behaviors are detected, DeFAI can react by adapting slippage limits, avoiding pools with suspicious activities.
Automated loan management
The loan approval process can also be optimized with DeFAI through a combination of sophisticated data analysis, smart contract automation, and real-time decision-making. Here’s how the workflow looks like:
- Data aggregation. Once borrowers submit their requests, AI-powered financial systems access their profiles by collecting information from on-chain data (transaction patterns, credit history, creditworthiness proxies, etc.) and off-chain information (social reputation, external credit scores, etc.). Moreover, credit scores are continuously updated based on real-time data changes.
- Credit score assessment. AI agents leverage the power of artificial intelligence to gauge complex data patterns. This way, they evaluate the risk of each borrower in real-time and forecast the likelihood of repayment, while accordingly adjusting interest rates.
- Automated underwriting. This decision-making stage includes determining whether to approve or reject the loan application. If approved, the DeFAI model sets loan parameters like amount, appropriate interest rates, and repayment schedule. If rejected, the model might suggest alternatives.
- Smart contract generation. After the underwriting stage, smart contracts are created. They disburse funds upon approval, track repayments, dynamically adjust interest rates, send borrowers reminder, and automate possible dispute resolution. Collateral value fluctuations are also continuously monitored, and automatic liquidation triggers are set (in case the collateral value drops below certain thresholds) to protect lenders.
DeFAI implementation with Aetsoft
DeFAI can significantly improve business operations, helping financial institutions increase security, enhance operational efficiency, and cut costs. However, such implementation presupposes certain challenges like integration complexity, regulatory compliance, and accountability.
An experienced DeFi development company, Aetsoft is here to give you a leg up on the following areas:
- Intelligent digital trading
- Loan application development
- DeFi wallet management
You can rely on our solid competence at every stage of the DeFAI platform development process:
- AI-based data analysis
- DeFi platform optimization and customization
- Platform integration with third-party blockchain modules and applications
- Full-fledged DeFi development
