Machine learning improves accounting estimates: evidence from insurance payments

Kexing Ding (), Baruch Lev (), Xuan Peng (), Ting Sun () and Miklos A. Vasarhelyi ()
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Kexing Ding: Southwestern University of Finance and Economics
Baruch Lev: New York University
Xuan Peng: Southwestern University of Finance and Economics
Ting Sun: The College of New Jersey
Miklos A. Vasarhelyi: Rutgers the State University of New Jersey

Review of Accounting Studies, 2020, vol. 25, issue 3, No 8, 1098-1134

Abstract: 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.

Keywords: Machine learning; Accounting estimates (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (30)

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DOI: 10.1007/s11142-020-09546-9

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