Machine learning improves accounting estimates: evidence from insurance payments

• 成果类型: Article
• 署名作者: Ding, Kexing; Lev, Baruch; Peng, Xuan; Sun, Ting; Vasarhelyi, Miklos A.
• 署名单位: Rutgers University System; Rutgers University New Brunswick; New York University; College of New Jersey
• 刊物名称: REVIEW OF ACCOUNTING STUDIES
• ISSN/ISSBN: 1380-6653
• DOI: 10.1007/s11142-020-09546-9
• 发表日期: 2020
• 页码: 1098-1134
• 关键词: insurer reserve error

摘要

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.