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<p>This perspective paper argues that, given the strong theoretical foundations and clear economic interpretations of traditional financial models, the integration of artificial intelligence (AI) in finance should prioritize enhancing these models—by incorporating alternative data sources and recalibrating key variables—rather than replacing them with opaque, albeit accurate, black-box models. We summarize studies that follow this approach, with a focus on the cases of Capital Asset Pricing Model (CAPM), Markowitz Mean-Variance Optimization (MVO), and the Black-Litterman Model (BLM). We demonstrate how AI, particularly Natural Language Processing (NLP) models, enables dynamic input estimation, nonlinear pattern discovery, sentiment extraction from financial text and sentiment-aware forecasting, and improved risk modeling, thereby addressing longstanding limitations in traditional frameworks. In addition, we highlight how this approach to some extent preserves interpretability—essential for regulatory compliance and investor trust—by tracing model decisions to intuitive, often human-understandable sources of information. By <i>augmenting</i> rather than <i>replacing</i> financial theory, this approach not only improves empirical performance but also enriches theoretical understanding, marking a paradigm shift in how financial models are built, explained, and applied.</p>

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AI reshaping financial modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月1日

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  • Frank Xing,
  • Kelvin Du,
  • Gianmarco Mengaldo,
  • Erik Cambria,
  • Roy Welsch

摘要

This perspective paper argues that, given the strong theoretical foundations and clear economic interpretations of traditional financial models, the integration of artificial intelligence (AI) in finance should prioritize enhancing these models—by incorporating alternative data sources and recalibrating key variables—rather than replacing them with opaque, albeit accurate, black-box models. We summarize studies that follow this approach, with a focus on the cases of Capital Asset Pricing Model (CAPM), Markowitz Mean-Variance Optimization (MVO), and the Black-Litterman Model (BLM). We demonstrate how AI, particularly Natural Language Processing (NLP) models, enables dynamic input estimation, nonlinear pattern discovery, sentiment extraction from financial text and sentiment-aware forecasting, and improved risk modeling, thereby addressing longstanding limitations in traditional frameworks. In addition, we highlight how this approach to some extent preserves interpretability—essential for regulatory compliance and investor trust—by tracing model decisions to intuitive, often human-understandable sources of information. By augmenting rather than replacing financial theory, this approach not only improves empirical performance but also enriches theoretical understanding, marking a paradigm shift in how financial models are built, explained, and applied.