Digital asset transactions generate vast amounts of data, which, in turn, can be processed, analyzed, and used to assist in decision-making. For instance, on-chain data is harvested and analyzed by firms such as Glassnode to gain insights into possible price movements for traders and investors, and companies like Chainalysis to trace fund movements linked to on-chain fraud and hacking incidents for law enforcement agencies and private firms.
AI offers the opportunity to automate and enhance data processing and analytics in many areas of the cryptocurrency sector, as well as automating tasks that humans would otherwise carry out less efficiently. As such, we can expect AI to continue to develop and play a more significant role in the cryptocurrency sector over time.
Projects at the convergence of AI and blockchain technology are also finding broader use cases, where blockchain can be used in situations such as enhancing AI transparency and expanding capacity for data storage. The scope of this article covers only AI in relation to cryptocurrencies.
AI in crypto trading
Trading is one of the most developed areas in AI and crypto since algorithmic trading bots such as 3Commas and CryptoHopper have existed since 2017. Trading bots are programmable software for trading strategies, allowing the user to customize multiple parameters to execute trades in reaction to particular trends or market patterns, on technical indicators, or as part of a broader risk management strategy.
Bots can analyze a vast amount of historical market data and execute many thousands of trades per second (depending on the latency of the underlying infrastructure) without any risk of human error.
However, trading using bots can also come with risks. Bots will continue to execute the strategy as programmed even if the market doesn’t behave as expected, leading to unintended consequences. Furthermore, since the algorithm is configured by a human, the programming may contain errors that mean the bot doesn’t perform as expected, even if the market does. Therefore, backtesting algorithmic trading strategies is critical.
Predictive analytics
Predictive analytics is a powerful use case for AI; and in the cryptocurrency sector, there is plenty of data that can be used to feed prediction algorithms. Along with historical price movements and on- and off-chain trading data, prediction engines can also parse social media, news publications, and online forums to gather sentiment data. Users can then access this data and use it to model future scenarios based on certain conditions. These models can then feed into the development of trading and risk management strategies.
Predictive analytics is an evolving segment and many platforms and models are still in their earliest stages, and may not leverage all available data sources. Therefore, the accuracy of any given prediction may not necessarily be trustworthy.
AI in crypto security and fraud detection
Detecting fraud in the cryptocurrency sector is notoriously difficult since most public blockchains operate pseudonymously. Criminals, such as Silk Road’s Ross Ulbricht, have often been caught through traces to their real-world identity that they’ve left behind online. The immutable nature of blockchain technology often means that once stolen, funds can’t be recovered.
AI compliance tools can now scan blockchain transaction activity in real time, identifying suspicious wallets and transactions and notifying authorities or even intervening to prevent a transaction. The algorithms can also continue to learn from new techniques used by fraudulent actors, enabling more rapid response and fraud prevention.
AI tools can also be used in code audits, to rapidly parse smart contract programming and identify any vulnerabilities that could be exploited by hackers.
AI in crypto compliance
AI is already used extensively in the banking and crypto sector to reduce the operational workload involved in Know-Your-Customer (KYC) checks. KYC service providers use AI to scan identity documents for signifiers of authenticity, as well as any anomalies and make sure it meets the required parameters for passing the check, such as age or residency.
AI in crypto risk management
Many of the use cases shown above are likely to be applied in tandem as part of a comprehensive risk management strategy. For example, a trading algorithm could be programmed to respond to changing market conditions for a given portfolio asset to mitigate losses, while fraud tools help to avoid hacks and theft incidents.
The scalability that AI offers can also mitigate risk in other ways. For example, automation makes it easier to manage multiple different accounts, allowing a portfolio to be diversified across multiple platforms and protocols, reducing the loss risk if one or more is compromised.
AI tokens
AI tokens are a segment of the cryptocurrency markets, representing tokens for blockchain-based AI projects, such as AI trading algorithms, or decentralized marketplaces for AI-related use cases, such as algorithms or data. AI tokens often have utility within their respective platforms, such as paying transaction fees or accessing services, or may be used for staking or governance.
Examples of projects with tokens in the AI category include the decentralized trading platform Injective (INJ), indexing protocol The Graph (GRT), and digital rendering services network, Render (RNDR).
AI in crypto essentials
- Cryptocurrency activities rely on and generate large amounts of data, so there is an opportunity to apply AI in several areas.
- AI applications for crypto cover use cases including trading algorithms, predictive analytics, fraud detection, compliance, and risk management, among others.
- AI tokens have emerged as a segment of the crypto markets, representing AI projects based on blockchain infrastructure.