Author: Zen and the Art of Computer Programming
With the development and application of artificial intelligence becoming more and more widespread, artificial intelligence technology has also entered the financial field. Because artificial intelligence can analyze, predict, and simulate human behavior, using artificial intelligence technology for financial transactions has become a new business model. This article will elaborate on the main applications of artificial intelligence in the financial field. First, let’s introduce several important components of artificial intelligence in the financial field, including machine learning, deep learning, reinforcement learning, genetic algorithms, game theory, etc. Then, these models and their role in the financial field are discussed one by one, and the applicability of different models is further elaborated based on market demand. Finally, based on existing relevant research results, the future development direction and challenges of artificial intelligence in the financial field are discussed.
2. Explanation of basic concepts and terms
(1) Machine learning
Machine Learning is a branch of artificial intelligence that aims to allow computers to solve various complex problems through training. Its most basic idea is to obtain data, organize data, build models, and predict results. Machine learning is used for supervised learning and unsupervised learning, that is, when the target variable or label is known, predict the target variable based on the input feature; or when the target variable is unknown, identify Distribution rules of data, classification, clustering, etc. The three elements of machine learning: model, training, and prediction. When training a model, training samples and corresponding output values need to be provided. The computer calculates a model based on the training samples. This model can predict new data.
(2) Deep learning
Deep Learning is also a sub-branch of machine learning, which refers to multi-level learning of neural networks. Traditional machine learning algorithms can only handle simple data sets