eLibrary
  • Home
  • Sources
  • Search
  • About

M MResearch on the Mechanisms and Effects of Artificial Intelligence in Expanding the Production Possibility Frontier in Finance

Junru Tang
Frontiers in Sustainable Development, (2026), Vol.6, No.1, pp.1-6
Published: January 26, 2026
DOI: 10.54691/4vh60h16
PDF: Download Full Text PDF
Abstract

Building on the production possibility frontier (PPF) theory from economics, this paper develops a theoretical framework to analyze how artificial intelligence (AI) is transforming the financial sector. The study proposes that AI systematically shifts the financial PPF outward through four key mechanisms: activating data as a production factor, improving technological efficiency, restructuring service models, and optimizing risk management. In practice, this shift leads to revolutionary improvements in supply efficiency, broader and more inclusive financial services, more precise risk control, and varied innovation in financial products. At the same time, the paper also examines challenges such as algorithmic bias and black-box decision-making, emphasizing that technology, ethics, and regulation form new constraints on the expansion of the frontier. This research provides a new theoretical perspective for understanding the nature and development of financial technology.

Keywords: Artificial Intelligence; Financial Technology; Production Possibility Frontier; Expansion Mechanisms.
APA Citation: Junru Tang (2026). M MResearch on the Mechanisms and Effects of Artificial Intelligence in Expanding the Production Possibility Frontier in Finance. Frontiers in Sustainable Development, 6(1), 1-6. https://doi.org/10.54691/4vh60h16

References

  1. [Ali H, Zafar MB, Aysan A F. Generative AI in Finance: Replicability, Methodological Contingencies, and Future Research Directions[J]. Finance Research Letters, 2025.
  2. Athey S, Morton F S. Artificial Intelligence, Competition, and Welfare[R]. National Bureau of Economic Research, 2025.
  3. Carrera A, Benalcázar M E. Optimizing the Collection Process in Credit Risk Management: A Comparison of Machine Learning Techniques for Predicting Payment Probability at Different Stages of Arrears[J]. Journal of Risk and Financial Management, 2025, 18(11): 630.
  4. Gramespacher T, Posth J A. Employing explainable AI to optimize the return target function of a loan portfolio[J]. Frontiers in Artificial Intelligence, 2021.
  5. Zhang Y, Zhu Y, Linnainmaa J T. Man versus machine learning revisited[J]. The Review of Financial Studies, 2025.
© 2026 eLibrary · Stand on the shoulders of giants