Machine learning improves accounting estimates: evidence from insurance payments

Kexing Ding, Baruch Lev, Xuan Peng, Ting Sun, Miklos A. Vasarhelyi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1098-1134
Number of pages37
JournalReview of Accounting Studies
Volume25
Issue number3
DOIs
StatePublished - 1 Sep 2020
Externally publishedYes

Keywords

  • Accounting estimates
  • Machine learning

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