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Machine learning improves accounting estimates: evidence from insurance payments

Author

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  • Kexing Ding

    (Southwestern University of Finance and Economics
    Rutgers the State University of New Jersey)

  • Baruch Lev

    (New York University)

  • Xuan Peng

    (Southwestern University of Finance and Economics
    Rutgers the State University of New Jersey)

  • Ting Sun

    (The College of New Jersey)

  • Miklos A. Vasarhelyi

    (Rutgers the State University of New Jersey)

Abstract

Managerial estimates are ubiquitous in accounting: most balance sheet and income statement items are based on estimates; some, such as the pension and employee stock options expenses, derive from multiple estimates. These estimates are affected by objective estimation errors as well as by managerial manipulation, thereby harming the reliability and relevance of financial reports. We show that machine learning can substantially improve managerial estimates. Specifically, using insurance companies’ data on loss reserves (future customer claims) estimates and realizations, we document that the loss estimates generated by machine learning were superior to actual managerial estimates reported in financial statements in four out of five insurance lines examined. Our evidence suggests that machine learning techniques can be highly useful to managers and auditors in improving accounting estimates, thereby enhancing the usefulness of financial information to investors.

Suggested Citation

  • Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, September.
    • Handle: RePEc:spr:reaccs:v:25:y:2020:i:3:d:10.1007_s11142-020-09546-9
    DOI: 10.1007/s11142-020-09546-9
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    References listed on IDEAS

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    6. Kelton, Andrea Seaton & Murthy, Uday S., 2023. "Reimagining design science and behavioral science AIS research through a business activity lens," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    7. Zhang, Chao & Zhu, Weidong & Dai, Jun & Wu, Yong & Chen, Xulong, 2023. "Ethical impact of artificial intelligence in managerial accounting," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    8. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    9. Evaggelia Siopi & Thomas Poufinas & James Ming Chen & Charalampos Agiropoulos, 2023. "Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(1), pages 15-30, May.
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    11. Yixuan Peng & Sayed Fayaz Ahmad & Ahmad Y. A. Bani Ahmad & Mustafa S. Al Shaikh & Mohammad Khalaf Daoud & Fuad Mohammed Hussein Alhamdi, 2023. "Riding the Waves of Artificial Intelligence in Advancing Accounting and Its Implications for Sustainable Development Goals," Sustainability, MDPI, vol. 15(19), pages 1-12, September.
    12. Diego Valentinetti & Michele A. Reaa, 2023. "Intelligenza artificiale e accounting: le possibili relazioni," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2023(2), pages 93-116.
    13. Jaewon Park & Minsoo Shin, 2022. "An Approach for Variable Selection and Prediction Model for Estimating the Risk-Based Capital (RBC) Based on Machine Learning Algorithms," Risks, MDPI, vol. 10(1), pages 1-20, January.
    14. Gina Raluca Guse & Marian Dragos Mangiuc, 2022. "Digital Transformation in Romanian Accounting Practice and Education: Impact and Perspectives," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 24(59), pages 252-252.
    15. Benjamin P. Commerford & Sean A. Dennis & Jennifer R. Joe & Jenny W. Ulla, 2022. "Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence," Journal of Accounting Research, Wiley Blackwell, vol. 60(1), pages 171-201, March.
    16. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).

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