AI in Financial Forecasting: A Study on Artificial Intelligence Adoption in Corporate Finance

Abstract

Traditionally, financial forecasting has been based on historical data, statistical models, spreadsheets and the expertise and experience of financial personnel. With the advent of big data, sophisticated computation, and artificial intelligence (AI) tools, financial forecasting can be made faster, more accurate, and more efficient. This research investigates the leveraging of Artificial Intelligence in Financial Forecasting in Corporate Finance and assesses the expected effects on the accuracy of forecasting, planning effectiveness and users' trust. Also examined are the key organizational and technical challenges impacting AI adoption, such as data quality, explainability, organizational readiness and resistance to change. A cross-sectional study was applied with a quantitative research design, prompted by a structured survey of finance, accounting and technology professionals whose knowledge or experience was related to financial forecasting using AI enabled technologies. The results show that there are perceived advantages over the accuracy of forecasts and planning efficiency using an AI-based forecasting approach over traditional forecasting approaches. Some of the findings do underscore, however, continuing questions about the quality, transparency, trust, and readiness of organizations. The study also suggests that AI is perceived more as a tool to aid in decision-making and assist finance professionals than as a superior method for making decisions. The results offer valuable guidance on successful and responsible implementation of AI in the financial planning and forecasting organization.