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Over the past decade, the integration of Artificial Intelligence (AI) into personal financial management has emerged as a strategic trend, transforming how individuals approach budgeting, investing, retirement planning, and debt management. Although academic interest in this area has grown, systematic mapping of publication trends, intellectual structures, and research gaps remains limited. This study conducts a bibliometric analysis of 624 Scopus-indexed publications from 2015 to 2024 using the Biblioshiny platform. The findings indicate a marked increase in publications following the COVID-19 pandemic, with most contributions originating from institutions in Asia, while Western countries produced the most highly cited works. Author keyword analysis and Multiple Correspondence Analysis (MCA) mapping identified three major thematic dimensions: algorithmic decision support, behavioral finance applications, and experimental AI methodologies. Additionally, several underexplored yet promising areas were identified, including demographic personalization, the integration of ethical considerations, and behavioral-based AI design. This study provides a comprehensive knowledge map and offers actionable insights for advancing research in AI-driven personal finance.

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A Decade of AI in Personal Financial Management: A Bibliometric Analysis of Trends, Intellectual Structures, and Research Opportunities

  • 会议论文

  • 发表日期：

    2026年

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  • Ayatulloh Michael Musyaffi,
  • Rida Prihartini,
  • Muhammad Ikhwan,
  • Rochma Sudiati,
  • Diah Armeliza,
  • Maulana Amirul Adha,
  • Maudy Shinta Maychella

摘要

Over the past decade, the integration of Artificial Intelligence (AI) into personal financial management has emerged as a strategic trend, transforming how individuals approach budgeting, investing, retirement planning, and debt management. Although academic interest in this area has grown, systematic mapping of publication trends, intellectual structures, and research gaps remains limited. This study conducts a bibliometric analysis of 624 Scopus-indexed publications from 2015 to 2024 using the Biblioshiny platform. The findings indicate a marked increase in publications following the COVID-19 pandemic, with most contributions originating from institutions in Asia, while Western countries produced the most highly cited works. Author keyword analysis and Multiple Correspondence Analysis (MCA) mapping identified three major thematic dimensions: algorithmic decision support, behavioral finance applications, and experimental AI methodologies. Additionally, several underexplored yet promising areas were identified, including demographic personalization, the integration of ethical considerations, and behavioral-based AI design. This study provides a comprehensive knowledge map and offers actionable insights for advancing research in AI-driven personal finance.