HARNESSING MACHINE LEARNING AND AI FOR PREDICTIVE FINANCIAL MODELING: A NEW ERA IN BUSINESS MANAGEMENT AND RISK OPTIMIZATION

Abstract

This paper discusses how AI and ML are changing business management by providing tools for improved risk assessment, fraud detection, and wealth management through stepwise approaches in investing. The world of predictive financial modeling has been transformed by the advancement of machine learning and artificial intelligence which has helped businesses improve decision-making, risk management, and forecasting. While most financial models have to deal with traditional economic models that are bound by static and simplistic economic and AI solutions, they offer real-time automation and pattern recognition. Using a polyglot approach incorporating literature review and empirical analysis, this research is carried out. The most relevant financial datasets are examined with supervised and unsupervised ML algorithms, including regression models, neural networks, and decision trees. The market is studied using sentiment analysis and natural language processing powered by AI. Industry case studies are incorporated to determine the effectiveness of the ML models utilized for predictive financial analytics. Sources of data include, but are not limited to, historical stock prices, macroeconomic indicators, and company financial reports. It is shown that AI and ML increase the dimensionality and the accuracy of business models, which enhances decision-making and helps in risk mitigation. AI-driven models are more responsive to marketplace changes, making them less uncertain and less financially risky. Issues like data protection, privacy, model explainability, and compliance with laws are tackled to use AI responsibly, ethically, and effectively. The report confirms the supposition that AI changes the nature of financial management, effectively creating intelligent devices and systems that result in an agile business ecosystem.