Financial Data Mining and Predictive Analysis in Intelligent Business

Abstract

The financial industry is going through a great change from operational to data-driven decisions (“Intelligent Business”) a problem that offers challenges to classical inferential statistics as well, given the difficulties in dealing with high dimensions, non-linearity and complexity of contemporary financial data. In this paper, this paper have offered a broad literature synthesis defining the resulting paradigm shift in the move from classical statistical inference to predictive machine learning (ML), with a taxonomy of applications, and the analysis of the barriers to adoption. The main thesis of this paper is that better performance of ML in the predictive aspects is limited by a ”Governance Trilemma“, i.e., a fundamental trilemma of combining model performance, regulator compliance (interpretability, algorithmic fairness) and data privacy. This is defined as the strategic dilemma to which responsible AI deployment amounts. This paper closes with a discussion of emergent frontiers, such as the potential of Large Language Models (LLMs), causal machine learning and combination with behavioral economics. The main contribution in this paper consists of offering a topographical outline of what one has to think about as a scholar and a macro-strategy guideline as a practitioner, and summarizing that proper application of AI requires sound governance and full organizational dedication.