AI for Economics: Innovative Approaches with Machine Learning to Predict Upcoming Recession

The recent adoption of machine learning and artificial intelligence tools in economic studies has improved the capacity of predicting recessions. It is making predictions more accurate and exact. This project favors the application of state-of-the-art machine learning techniques to predict latent recessions from a number of macroeconomic, market, financial and global data sources. This work is guided by the practical application of advanced machine learning algorithm ie neural network, Support vector machine, Random forest, ensemble techniques among others which illustrate that such techniques are capable of discovering complex patterns and identifying threat signals which traditional methods cannot. The most successful recession prediction techniques are those ascertained through several Machine Learning model comparisons in this project. In the results, it will be seen that the application of ML models is much better than that of using econometric theory for predicting downturns in the economy which will be very helpful information for the governments or institutions such as banks and even investors. This information allows creating policies in advance so that the adverse consequences of the recession are lessened, improving the economic strength and stability. In addition, the project covers the ethical issues and potential challenges in deploying AI for economic forecasting and recommends the need for supplementing human judgment with machines. In conclusion, this project presents a vision of economic forecasting that has been revolutionized by AI and ML, and shows how this vision could eventually lead to better economic policies. The work will also serve as a foundation for further research to improve these models and broaden their use in different areas of the economy.