Prospects for applying deep learning models in economics and finance

The article is devoted to the study of the prospects of applying deep learning models in the field of economics and finance. The main types of deep learning architecture that have been applied in the economic sphere are identified. Based on the analysis of publications, the main areas of application of deep learning models in economics are identified, namely in the areas of macroeconomics and microeconomics for analyzing consumer behavior, pricing strategies, and competition. It is noted that most of the work on using machine learning models for market analysis relates to financial markets rather than commodity markets. More deep learning models must be developed for most goods and services markets. It has been established that the financial sector is one of the critical areas for using deep learning models. In the financial sector, deep learning is used for analyzing the situation in the financial sector and forecasting financial market indicators (stock prices, exchange rates, and cryptocurrencies); analysis of financial statements; analysis and management of risks (credit risk analysis, fraud detection, securities portfolio risk analysis, securities portfolio optimization), etc. The following areas of application of deep learning models are considered: financial market forecasting, foreign exchange market forecasting, algorithmic trading, credit risk analysis and assessment, and fraud detection. The author identifies several problems and limitations of using deep learning models in economics and finance: lack of research comprehensiveness; problem of bringing to a single period; problem of availability and quality of source data; need for large amounts of data for model training; complexity of interpretation; risk of overfitting models; limited computing resources. It is concluded that deep learning has proven to be effective in forecasting economic indicators by analyzing large and complex data sets to identify patterns and create accurate forecasts. Keywords: economic research, artificial intelligence, machine learning, deep learning, macroeconomics, microeconomics, finance, market analysis.