Head of FIS AI product: the integration of blockchain and artificial intelligence will promote financial technology innovation
Original compilation: 黑米@白泽研究院
Original compilation: 黑米@白泽研究院
text
text
Blockchain and artificial intelligence (AI) are two of the most revolutionary technologies of the 21st century. It is widely believed that the confluence of these two megatrends could bring about the "fourth industrial revolution". According to technology research firm Gartner, the business value generated by blockchain and artificial intelligence alone will grow rapidly. They predict that the blockchain market will be worth $176 billion by 2025 and $3.1 trillion by 2030. Additionally, the AI software market size will reach nearly $134.8 billion by 2025.
Blockchain and artificial intelligence have a large number of applications in various fields. In this article, we will introduce fintech as a field and how the integration of these two technologies will help to foster innovation.
Blockchain can create a decentralized ecosystem that removes the need for a centralized control agency. AI architectures can be created on top of this decentralized ecosystem.
secondary title
What is blockchain?
We all know the popular encrypted assets such as Bitcoin, Ethereum, etc. These are blockchain-based tokens, but blockchain is not just encrypted assets.
Blockchain is a secure and shared decentralized data ledger.
Blockchain technology enables a specific set of parties to share data. It can collect and share transaction data from multiple sources, subdivide data into shared blocks linked together by unique identifiers in the form of cryptographic hashes, and ensure data integrity through a single source of information, eliminating data duplication and improving data security.
In a blockchain system, data cannot be changed without the permission of a quorum, a feature that helps prevent fraud and data tampering. In other words, blockchain ledgers can be shared, but not changed. If one party attempts to alter the data, all parties involved in the blockchain will be alerted as to which party attempted to alter the data.
The following definitions help you further understand the blockchain and its underlying technology and usage scenarios.
Decentralized trust: The main reason why many companies adopt blockchain technology instead of other data storage technologies is that blockchain can guarantee data integrity without relying on centralized authority, that is, to achieve decentralized trust based on reliable data.
Block: As the name suggests, the blockchain stores data in blocks, and each block is connected to the previous block to form a chain structure. It only supports adding new blocks, once added, it cannot be modified or deleted.
· Consensus algorithm: The consensus algorithm is responsible for the execution of rules within the blockchain system. After the various parties set the rules for the blockchain, the consensus algorithm will ensure that all parties abide by these rules.
secondary title
What is artificial intelligence?
Alan Turing (British mathematician and father of artificial intelligence) once asked an important question: "Can machines think?". He published a major paper in 1950 on "Computers and Intelligence," which led to the creation of "thinking machines," also known as artificial intelligence. Artificial intelligence uses computers and machines to mimic the problem-solving and decision-making abilities of the human mind.
first level title
Fintech Application of Artificial Intelligence in Blockchain
1. Encrypted quantification and algorithmic trading:
Machine learning has practical implications in the crypto ecosystem. Provides traders with predictive insights into crypto assets through historical trends, technical indicators and market sentiment.
For example, encrypted bots through APIs can collect data in real time. Through machine learning, bots can provide actionable indicators or conclusions, known as trading signals. The bot can be run standalone or integrated into a crypto trading platform. These bots not only predict future prices but also automate trading. According to the accuracy of the forecast, the user can realize a certain profit.
As of March 2022, there are nearly 18,000 encrypted assets in the encrypted market, of which more than 10,000 are still active. These bots are suitable for crypto assets with large user ecosystems and more volatile.
2. Effective data/model sharing:
Data is the most important resource for an artificial intelligence or machine learning model. The quality and quantity of data directly affect the accuracy of both, but the current process of sharing data is not efficient. Since data providers do not trust each other, it is difficult to authorize or verify data using traditional methods, but it turns out that some blockchain-based solutions can use decentralized data operations to solve this problem.
The solution focuses on developing a blockchain-based marketplace where data providers and AI/ML models will be able to collaborate and transact with each other using blockchain smart contracts.
Financial institutions can securely share data, algorithms, and calculations through the blockchain.
Platform providers such as Ocean Protocol and NUMERAI are some of the major players in this space.
For example, in NUMERAI they even play a role in the economics/financial industry, especially in the field of hedge funds. NUMERAI's goal is to create the world's largest crowdsourced hedge fund powered by artificial intelligence. By uploading hedge fund data to the data marketplace, thousands of data scientists collaborate and test models to predict the stock market.
3. Construct open banking through federated learning:
Generally, your financial data is owned by the bank/financial institution and kept in their database of record, but the open banking concept allows its users to own their banking data.
It is foreseeable that with Federated Learning, we will have decentralized (ownerless) data ownership in financial institutions.
Federated learning is essentially a distributed machine learning technology, the purpose of which is to achieve common modeling and improve the effect of artificial intelligence on the basis of ensuring data privacy security and legal compliance.
In other words, federated learning enables data owners to conduct model training without transferring their raw data to third-party servers. This distributed learning framework allows users to be provided with AI-based recommendations and services while protecting their privacy.
In short, machine learning models can be trained in a decentralized architecture. Normally we aggregate data to train a model, but in this case the model is sent to individual data owners. The model is then trained on each data node, the updated weights are sent to the coordinator and averaged for the final model.
Therefore, in this method, the data never leaves the hands of its original owner, which makes this method highly secure. There is also trust between data owners and data scientists without compromising model performance.
4. On-chain analysis
Due to the complete transparency of the blockchain, participants can see all the transactions and activities taking place on the blockchain, as well as the total balances and holdings of certain wallets. Analyzing all activity and data on the blockchain to generate insightful views on market sentiment and investment decisions is what we call on-chain analytics.
Many basic on-chain analysis tools, such as EtherScan for the Ethereum blockchain or SnowTrace for the Avlanche blockchain, are free blockchain explorers that allow tracking of all transactions on the respective blockchain. Many platforms leverage these tools while leveraging machine learning and aggregating data nodes to offer their platforms as a service to consumers. Here are some of the most popular:
· Glassnode
· IntoTheBlock
· Nansen
· Dune Analytics
· Messari
As we mentioned, many of these platforms are leveraging artificial intelligence and machine learning to generate market insights and make recommendations on potential investment opportunities based on their on-chain analytics. For example, they can use machine learning to find "wallets that have historically performed well and outperformed the market" and make new investment recommendations based on changes in asset allocation of those wallets.
On the other hand, while anonymity is a great value proposition of blockchains and cryptoassets, it does increase the risk of money laundering and other illicit activities. CipherTrace’s 2020 Crypto Asset Crime and AML Report shows that crypto asset theft, hacks, and fraud totaled $1.9 billion during the year. This is where on-chain analytics + machine learning may prove useful. Machine learning can help spot patterns that humans might not notice, such as detecting interactions of crypto wallets with other accounts or wallets linked to known criminal activity. By using on-chain machine learning, financial institutions and exchanges will better understand the risks involved in each transaction, and in addition, as the rate of false positives is reduced, the required manual review work will be significantly reduced.
Overall, we believe that on-chain use cases for machine learning are only in their infancy and will mature along with the crypto industry.
5. Future Web 3 and Smart Blockchain
Just as software infrastructure such as networking, storage, and operating systems are becoming intelligent, the next generation of Layer 1 (base) and Layer 2 (supporting) blockchains may be driven by machine learning as a native function.
Let's imagine that when a blockchain is running, it uses machine learning to predict transactions to enable massively scalable consensus protocols in the future; Web3's smart contract protocols will have machine learning capabilities, such as a lending agreement, which uses artificial intelligence To balance the types of borrowing and lending of different wallets; even smart DApps (decentralized applications) will soon become a trend.
According to the "Notice on Further Preventing and Dealing with the Risk of Hype in Virtual Currency Transactions" issued by the central bank and other departments, the content of this article is only for information sharing, and does not promote or endorse any operation and investment behavior. Participate in any illegal financial practice.
risk warning:
According to the "Notice on Further Preventing and Dealing with the Risk of Hype in Virtual Currency Transactions" issued by the central bank and other departments, the content of this article is only for information sharing, and does not promote or endorse any operation and investment behavior. Participate in any illegal financial practice.
