Technology that enables computers to understand financial regulatory policies is introduced as follows:

Technology that enables computers to understand financial regulatory policies is natural language processing (NLP).

NLP is a technology that allows computers to understand and analyze natural language. With NLP, computers can read and understand financial regulatory policy text, extract key information, and determine its meaning and implications. This can help financial institutions automate their compliance analysis and interpretation of regulatory policies, thereby reducing the risks and costs of manual processes.

Computers have a wide range of applications in the understanding and application of financial regulatory policies. These technologies help regulators and financial institutions to better enforce and comply with relevant regulations, improving efficiency and accuracy.

Natural Language Processing (NLP) is a technique by which computers understand human language. In the field of financial regulatory policy, NLP helps computers automatically parse and extract key information from policy texts. By analyzing a large number of regulatory documents and reports, computers can automatically identify violations and alert regulators to take timely action.

Machine learning (ML) is an artificial intelligence technique that can predict future trends and behaviors by analyzing historical data. In the field of financial regulatory policy, ML can be used for risk assessment and prediction. By y analyzing large amounts of data from financial institutions, computers can identify potential risk points and take appropriate regulatory measures in advance.

Big data analytics is a technique for processing large amounts of data. In the field of financial regulatory policy, big data can help regulators gain a comprehensive understanding of the operation and risk profile of financial institutions. Through the analysis and mining of massive data, computers can detect abnormal patterns and potential risks, providing a valuable basis for regulators to make decisions.

Blockchain technology is a decentralized distributed database technology. In the field of financial regulatory policy, blockchain can be used to improve the transparency and credibility of regulation. By storing regulatory policies and related data on the blockchain, computers can ensure the immutability and traceability of the data and enhance the credibility of regulatory agencies.

In general, computers have an important role to play in the understanding of financial regulatory policies. By applying technologies such as NLP, ML, big data, and blockchain, computers can help regulators better enforce and comply with regulations, improving efficiency and accuracy.