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<p>This study aims to understand how to predict financial performance by systematically reviewing its conceptual evolution and integration with Artificial Intelligence (AI). This study analyses 85 articles from 74 Scopus-indexed journals published between 1968 and 2023. We compile articles from 42 different countries written by 203 authors. This study explores the evolution of predicting financial performance, reviews existing research, highlights the use of AI in predicting financial performance, and guides future research. Predicting financial performance research was developed during the Asian Economic Crisis in 1997/1998 and the Subprime Mortgage Crisis in 2008. Research expanded in 2019 and peaked in 2022, with most articles highlighting the integration of AI to predict bankruptcy and company performance. Using AI in predicting financial performance, such as the Neural Network technique, RF technique, KNN technique, NLP and LLMs improves predictive accuracy, efficiency, and decision-making effectiveness. This study shows that traditional models are out of date. Predicting financial performance models that use AI improves prediction accuracy and agility in business processes, and management can make decisions more quickly and precisely. AI can predict and adjust financial performance models to the company’s conditions. This study reviews existing research on predicting financial performance to identify gaps in future research, especially regarding the use of AI in predicting financial performance.</p>

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Integrating artificial intelligence to improve predicting financial performance accuracy: a systematic literature review

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Akhmad Sigit Adiwibowo,
  • Ersa Tri Wahyuni

摘要

This study aims to understand how to predict financial performance by systematically reviewing its conceptual evolution and integration with Artificial Intelligence (AI). This study analyses 85 articles from 74 Scopus-indexed journals published between 1968 and 2023. We compile articles from 42 different countries written by 203 authors. This study explores the evolution of predicting financial performance, reviews existing research, highlights the use of AI in predicting financial performance, and guides future research. Predicting financial performance research was developed during the Asian Economic Crisis in 1997/1998 and the Subprime Mortgage Crisis in 2008. Research expanded in 2019 and peaked in 2022, with most articles highlighting the integration of AI to predict bankruptcy and company performance. Using AI in predicting financial performance, such as the Neural Network technique, RF technique, KNN technique, NLP and LLMs improves predictive accuracy, efficiency, and decision-making effectiveness. This study shows that traditional models are out of date. Predicting financial performance models that use AI improves prediction accuracy and agility in business processes, and management can make decisions more quickly and precisely. AI can predict and adjust financial performance models to the company’s conditions. This study reviews existing research on predicting financial performance to identify gaps in future research, especially regarding the use of AI in predicting financial performance.