Improving Financial Forecasting Accuracy with Artificial Intelligence (AI) Models

Abstract

In this paper, the author examines the role of the AI models in promoting a radical shift in financial forecasting, planning, and analysis (FP&A). Compared to its human counterparts, AI is uniquely suited to the process of interpreting large data sets so that it can greatly refine the estimates upon which business finances depend. Most of the classical AI approaches, which are mainly developed for making prediction, need to be adjusted for dealing with planning processes in organization, which often require more causal analysis. This paper describes the difficulties arising from the non-critical implementation of the AI in this context, and assesses the potential of the double machine learning framework in responding to causal questions. Also, we investigate how AI transforms the approaches to assessing and optimising the financial risks. Harnessing state of the art algorithms including neural networks and big data analytics, AI models allow for bringing up real time big data analysis of various financial data, where patterns could be obscure even to standard techniques. All these capabilities enable the right prognosis of market trends, estimation of credit risk and then provide the best means of investment. Another issue discussed in the paper is the challenges related to AI integrated financial forecasting, including the issues of data protection, model explainability, as well as the challenges that arise in terms of AI based decision making. Using a simulation analysis, we explain that as the data grows, both accuracy in forecasting and effectiveness in planning are enhanced, stressing that AI is indispensable for creating a wiser and stronger financial industry.