Machine Learning for Predicting the Procurement of an Audit at Small Private Banks: Is the Decision to Procure an Audit Systematic?

19 Pages Posted: 2 Jun 2011 Last revised: 15 Oct 2011

See all articles by David Y. Chan

David Y. Chan

St. John's University - Department of Accounting & Taxation

Alexander Kogan

Rutgers, The State University of New Jersey

Date Written: October 2011

Abstract

Absent regulatory requirements, 64% of small private commercial banks voluntarily procured an independent audit in our dataset. Hence, it can be argued that an independent audit may have a perceived value. In our study, we examine whether the decision to procure an audit is systematic. First, we model and predict the decision to procure a voluntary audit using classification learning algorithms. We find, in particular, that the IBK and Random Forest learning algorithms have the highest cross-validated prediction accuracy of 81% and 80% respectively, thus supporting the claim that the decision to procure an audit is not made arbitrarily, but rather systematically, and therefore is amenable to analysis. Second, we use logistic regression to identify those characteristics that may systematically influence the decision to procure an independent audit. Generally, we find that bank size, profitability, growth, leverage, complexity of operations, and type of ownership may systematically influence the decision to procure an independent audit. In contrast, banks that are less capitalized are less likely to procure an audit.

Keywords: Audit Demand, Data Mining, Machine Learning, Classification Problem, Commercial Banks, FDIC Call Reports

JEL Classification: M40, M41, M49

Suggested Citation

Chan, David Y. and Kogan, Alexander, Machine Learning for Predicting the Procurement of an Audit at Small Private Banks: Is the Decision to Procure an Audit Systematic? (October 2011). Available at SSRN: https://ssrn.com/abstract=1856367 or http://dx.doi.org/10.2139/ssrn.1856367

David Y. Chan (Contact Author)

St. John's University - Department of Accounting & Taxation ( email )

New York, NY
United States

Alexander Kogan

Rutgers, The State University of New Jersey ( email )

311 North 5th Street
New Brunswick, NJ 08854
United States