Machine Learning Improves Accounting Estimates : Evidence from Insurance Payments

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 adversely affecting the reliability and relevance of financial reports. We show in this study 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. Obviously, the generalizability of our findings beyond insurance data remains to be examined by future research