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<p>The integration of Artificial Intelligence (AI) into financial services represents a developmental shift in the industry, presenting unprecedented opportunities and challenges. This scientometric review examines the evolution of AI in finance from 1989 to 2024, analyzing its pivotal applications in credit scoring, fraud detection, digital insurance, robo-advisory services, and financial inclusion. The analysis reveals significant trends, particularly the growing adoption of machine learning, natural language processing, and blockchain technologies in reshaping financial operations and decision-making processes. The review addresses critical regulatory and ethical challenges, emphasizing the imperative for explainable AI (XAI) and robust governance frameworks to ensure transparency, fairness, and accountability in AI-driven systems. Despite rapid advancements, persistent gaps remain, the most notable of which is the lack of standardized frameworks for AI implementation across financial sectors. The findings support the need for a balanced approach that promotes innovation while addressing ethical, regulatory, and societal concerns. This comprehensive synthesis maps the trajectory of AI in finance, identifies key areas for future research, and recommends interdisciplinary collaboration to advance responsible and sustainable AI integration within the financial ecosystem.</p>

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AI integration in financial services: a systematic review of trends and regulatory challenges

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月22日

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  • Darko B. Vuković,
  • Senanu Dekpo-Adza,
  • Stefana Matović

摘要

The integration of Artificial Intelligence (AI) into financial services represents a developmental shift in the industry, presenting unprecedented opportunities and challenges. This scientometric review examines the evolution of AI in finance from 1989 to 2024, analyzing its pivotal applications in credit scoring, fraud detection, digital insurance, robo-advisory services, and financial inclusion. The analysis reveals significant trends, particularly the growing adoption of machine learning, natural language processing, and blockchain technologies in reshaping financial operations and decision-making processes. The review addresses critical regulatory and ethical challenges, emphasizing the imperative for explainable AI (XAI) and robust governance frameworks to ensure transparency, fairness, and accountability in AI-driven systems. Despite rapid advancements, persistent gaps remain, the most notable of which is the lack of standardized frameworks for AI implementation across financial sectors. The findings support the need for a balanced approach that promotes innovation while addressing ethical, regulatory, and societal concerns. This comprehensive synthesis maps the trajectory of AI in finance, identifies key areas for future research, and recommends interdisciplinary collaboration to advance responsible and sustainable AI integration within the financial ecosystem.