article review on advanced financial accounting

Does automation improve financial reporting? Evidence from internal controls

• Published: 23 April 2024

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• Musaib Ashraf   ORCID: orcid.org/0000-0002-6935-2480 1  

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Automation—such as machine learning, robotic process automation, and artificial intelligence—is the next major technological leap in accounting and financial reporting, and I empirically study whether public firms’ use of automation technology improves their financial reporting, specifically focusing on the internal control environment. I document two critical inferences. First, I find evidence which suggests that automation improves financial reporting quality. Specifically, firms’ use of automation in the financial reporting process is associated with a reduction in internal control material weaknesses. This association is consistent in a levels analysis with firm and year fixed effects, in a changes analysis, and in a propensity score matched difference-in-differences analysis. Second, I find evidence which suggests that monitoring of the financial reporting process decreases after automation, likely because of a perception that automation reduces the need for monitoring vis-à-vis stronger internal controls. Specifically, automation is associated with higher external audit fees and audit committee meetings in the initial years after a firm implements automation but associated with lower external audit fees and audit committee meetings in subsequent years. I also find evidence which suggests that this decreased monitoring may be costly: when internal control failures do happen for firms with automation, the failures are more material, as proxied by stronger negative market reactions. In aggregate, my evidence provides nuanced insights regarding whether automation technology improves financial reporting.

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Artificial intelligence and machine learning are relatively more sophisticated forms of automation in which the software has (at least some) built-in ability to make decisions, whereas robotic process automation is relatively less sophisticated where software repeats the tasks it is programmed to do but with no decision-making (Deloitte 2017 ).

The complexity also varies depending on the type of automation technology. For example, artificial intelligence and machine learning depend on models that must be trained on and learn from data, while robotic process automation is rules-based and completes repetitive tasks with little to no training or learning from data. Thus, the source of risk for artificial intelligence and machine learning stems from training on or learning from poor quality or unapplicable data whereas the source of risk for robotic process automation stems from being confined to pre-defined rules that are unable to adapt to potentially changing scenarios.

Concurrent work by Awyong et al. ( 2022 ) finds that firms’ digitalization improves financial reporting quality. However, like other manuscripts, Awyong et al. ( 2022 ) study the effect of job postings that require candidates to have digital skills, whereas I study the effect of implemented automation. Aside from being different empirical proxies, conceptually digitalization (which is related to information technology more generally) is a different construct than automation (which tends to focus on specifically on technologies like machine learning, artificial intelligence, and robotic process automation). Awyong et al. ( 2022 ) also do not document the same nuanced implications that I do (i.e., stronger overall financial reporting but decreased oversight).

According to SEC ( 2008 , p.19), “a company must disclose any change in its internal control over financial reporting that occurred during the fiscal quarter covered by the quarterly report, or the last fiscal quarter in the case of an annual report, that has materially affected, or is reasonably likely to materially affect, the company's internal control over financial reporting.”.

CALCBENCH is a data aggregator that extracts data directly from SEC filings (Hoitash and Hoitash 2018 ). CALCBENCH is similar to traditional data sources such as Compustat, except CALCBENCH extracts more than just financial statements from SEC filings. Specifically relevant to my research design, CALCBENCH gathers and allows textual analysis on the individual sections of 10-K and 10-Q filings – such as the Controls and Procedures section (Calcbench 2023 ).

Chen and Srinivasan ( 2023 ) search for seven types of words in their analyses: analytics-related, automation-related, artificial intelligence-related, big data-related, cloud-related, digitization-related, and machine learning-related (see their Appendix A). Given that my construct of interest is automation (which tends to focus specifically on things like machine learning, artificial intelligence, and robotic process automation) and not Chen and Srinivasan’s ( 2023 ) construct of digitalization (which is related to information technology more generally), I focus on their words that are related to automation, artificial intelligence, and machine learning.

It is possible that some firms introduce accounting automation that they do not discuss in the Controls and Procedures section. However, firms are required to disclose material changes to their financial reporting process in this section of 10-K and 10-Q filings (SEC 2008 ), filings that the CEO and CFO personally attest to the validity of. Consequently, any automation that a firm has introduced but has not disclosed in the Controls and Procedures section is likely to be not material. Empirically, this noise in my test variable should not bias toward statistical significance.

I employ a linear probability model, instead of a logistic regression, because of the incidental parameters problem that can arise from complex fixed effect structures in nonlinear models (Greene 2004 ) and because interactions can be difficult to interpret in nonlinear models (Ai and Norton 2003 ). Results are consistent in an analogous fixed effects logistic regression (see Table OA.2 in the online appendix).

My sample begins in 2009 because CALCBENCH coverage starts in 2008 and I require at least one prior period to calculate my test variable.

I calculate economic significance by re-estimating Eq. ( 1 ) in an analogous fixed effects logistic regression and then calculating the odds ratio for the coefficient on AUTOMATION (odds ratio = 0.3820) (see Table OA.2 in the online appendix).

TREAT equals one if firm i is part of the treatment group and zero if firm i is part of the control group. POST equals one if year t is after the year that firm i is treated (for treatment observations) or after the year that firm i ’s matched treatment firm is treated (for control observations) (zero otherwise).

AUTOMATION_GROUP_1 equals one when AUTOMATION equals one but only for observations that introduced automation into the {expenses & payables} area of accounting; equals zero when AUTOMATION equals zero; and all other observations are discarded. AUTOMATION_GROUP_2 , AUTOMATION_GROUP_3 , and AUTOMATION_GROUP_4 are calculated similarly except for the {consolidations, reconciliations, and journal entries}, {revenue & receivables}, and {segregation of duties, user access and monitoring, and IT} areas of accounting, respectively. AUTOMATION_GROUP_1&2&3&4 equals one when either of AUTOMATION_GROUP_1 , AUTOMATION_GROUP_2 , AUTOMATION_GROUP_3 , or AUTOMATION_GROUP_4 equals one; equals zero when AUTOMATION equals zero; and all other observations are discarded.

MATERIAL_WEAKNESS_GROUP_1 equals one when MATERIAL_WEAKNESS equals one but only for observations that Audit Analytics categorizes as {code 29 [expense recording (payroll, SG&A) issues], code 14 [capitalization of expenditures issues], code 32 [inventory, vendor and cost of sales issues], code 27 [deferred, stock-based or executive comp issues], code 33 [liabilities, payables, reserves and accrual estimation failure issues], code 80 [pension and other post-retirement benefit issues], or code 41 [tax expense/benefit/deferral/other (FAS 109) issues]}; equals zero when MATERIAL_WEAKNESS equals zero; and all other observations are discarded. MATERIAL_WEAKNESS_GROUP_2 , MATERIAL_WEAKNESS_GROUP_3 , and MATERIAL_WEAKNESS_GROUP_4 are calculated similarly except for observations that Audit Analytics categorizes as {code 76 [journal entry control issues], code 24 [consolidation, (Fin46r/Off BS) & foreign currency translation issues], code 8 [intercompany/investment w/ subsidiary/affiliate issues], code 12 [untimely or inadequate account reconciliations], or code 38 [foreign, related party, affiliated and/or subsid issues]}, {code 39 [revenue recognition issues] or code 15 [accounts/loans receivable, investments & cash issues]}, and {code 42 [segregations of duties/design of controls issue] or code 22 [information technology, software, security & access issue]}, respectively. MATERIAL_WEAKNESS_GROUP_1&2&3&4 equals one when either of MATERIAL_WEAKNESS_GROUP_1 , MATERIAL_WEAKNESS_GROUP_2 , MATERIAL_WEAKNESS_GROUP_3 , or MATERIAL_WEAKNESS_GROUP_4 equals one; equals zero when MATERIAL_WEAKNESS equals zero; and all other observations are discarded.

The sample size varies between columns in Table  5 because in each column I exclude (i) observations that automated but did not specify the area of accounting that the automation was introduced in, (ii) observations that automated an area of accounting that is different than the focal area being studied, and (ii) observations that have a material weakness but in an area that is other than the focal accounting area being studied. In other words, the zeroes for each test variable are observations that did not introduce automation, and the zeroes for each dependent variable are observations that do not possess any material weaknesses.

I obtain data on audit committee meetings from Ashraf, Deore, and Krishnan ( 2024) , who programmatically extract data on audit committee meetings from firms’ proxy statement filings (firms are required to make such disclosures, see 17 CFR §229.407(b)). The sample size in Table  7 is relatively smaller than other analyses due to the fact that the audit committee meetings data is unstructured in proxy filings and therefore it is not possible to programmatically extract meetings data from every proxy filing. Observations with missing data on AC_MEETINGS are excluded from the analysis.

The ‘main effect’ of YEARS_SINCE_AUTOMATION is omitted from Table  6 and 7 due to collinearity: AUTOMATION*YEARS_SINCE_AUTOMATION and YEARS_SINCE_AUTOMATION are effectively the same variables and therefore both cannot be included in the same regression analysis.

RESTATEMENT_GROUP_1 equals one when RESTATEMENT equals one but only for observations that Audit Analytics categorizes as {code 7 [expense (payroll, SGA, other) recording issues], code 12 [liabilities, payables, reserves and accrual estimate failures], code 23 [capitalization of expenditures issues], code 20 [inventory, vendor and/or cost of sales issues], code 17 [deferred, stock-based and/or executive comp issues], code 48 [deferred, stock-based options backdating only], code 39 [deferred, stock-based SFAS 123 only], code 69 [pension and other post-retirement benefit issues], or code 18 [tax expense/benefit/deferral/other (FAS 109) issues]}; equals zero when RESTATEMENT equals zero; and all other observations are discarded. RESTATEMENT_GROUP_2 and RESTATEMENT_GROUP_3 are calculated similarly except for observations that Audit Analytics categorizes as {code 13 [consolidation issues incl Fin 46 variable interest & off-B/S], code 37 [consolidation, foreign currency/inflation issue], code 24 [intercompany, investment in subs./affiliate issues], code 43 [intercompany, only—accounting issues], code 11 [foreign, related party, affiliated, or subsidiary issues], or code 44 [foreign, subsidiary only issues]} and {code 6 [revenue recognition issues] or code 14 [accounts/loans receivable, investments & cash issues]}, respectively. RESTATEMENT_GROUP_1&2&3 equals one when either of RESTATEMENT_GROUP_1 , RESTATEMENT_GROUP_2 , or RESTATEMENT_GROUP_3 ; equals zero when RESTATEMENT equals zero; and all other observations are discarded. I do not conduct any analysis of AUTOMATION_GROUP_4 for restatements because there are no analogous restatement categories.

Following the advice of extant literature (e.g., Chan, Chen, Chen, and Yu 2012 ; Jha and Chen 2015 ; Ashraf et al. 2020 ), the control variables in Table  12 are based on DeFond and Zhang’s ( 2014 ) audit fees model.

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Acknowledgements

I thank Lakshmanan Shivakumar (editor), two anonymous reviewers, Preeti Choudhary, Ranjani Krishnan, Adi Masli, Vernon Richardson, Juan Manuel Sanchez, David Wood, and the workshop participants at University of Connecticut for their feedback. I also thank the Eli Broad College of Business at Michigan State University for funding that enabled this study. Any errors are my own.

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• 19 Nov 2018

Lazy Prices

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How Cost Accounting is Improving Healthcare in Rural Haiti

The cost of healthcare in rural Haiti was found to vary widely, even inside the same health organization. A pioneering cost accounting system co-developed by Robert Kaplan was called in to determine the cause. Open for comment; 0 Comments.

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The Thirteenth Edition of Advanced Financial Accounting is an up-to-date, comprehensive, and highly illustrated presentation of the accounting and reporting principles and procedures used in a variety of business entities. Every day, the business press carries stories about merger and acquisition mania, the complexities of modern business entities, new organizational structures for conducting business, accounting scandals related to complex business transactions, the foreign activities of multinational firms, the operations of governmental and not-for-profit entities, and bankruptcies of major firms. Accountants must understand and know how to deal with the accounting and reporting ramifications of these issues.

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Theodore Christensen

Ted Christensen has been a faculty member at the University of Georgia since 2015. Prior to coming to UGA, he was on the faculty at Brigham Young University for 15 years and Case Western Reserve University for five years. He received a BS degree in accounting at San Jose State University, a MAcc degree in tax at Brigham Young University, and a PhD in accounting from the University of Georgia (so he is now teaching at a second alma mater). Professor Christensen has authored and coauthored articles published in many journals including The Accounting Review , Journal of Accounting Research , Journal of Accounting and Economics , Review of Accounting Studies , Contemporary Accounting Research , Accounting Organizations and Society , Journal of Business Finance Accounting , Accounting Horizons , and Issues in Accounting Education . Professor Christensen has taught financial accounting at all levels, financial statement analysis, both introductory and intermediate managerial accounting, and corporate taxation. He is the recipient of numerous awards for both teaching and research. He has been active in serving on various committees of the American Accounting Association and is a CPA.

David Cottrell

Professor Cottrell joined the faculty at Brigham Young University in 1991. He currently serves as the Associate Director of the School of Accountancy. Prior to coming to BYU he spent five years at The Ohio State University, where he earned his PhD. Before pursuing a career in academics he worked as an auditor and consultant for the firm of Ernst & Young in its San Francisco office. At BYU, Professor Cottrell has developed and taught courses in the School of Accountancy, the MBA program, and the Finance program. He has won numerous awards from the alumni and faculty for his teaching and curriculum development. He has received the Outstanding Professor Award in the college of business as selected by the students in the Finance Society; he has received the Outstanding Teaching Award as selected by the Marriott School of Management; and he is a four-time winner of the collegewide Teaching Excellence Award for Management Skills, which is selected by the Alumni Board of the Marriott School of Management at BYU. Professor Cottrell also has authored many articles about accounting and auditing issues. His articles have been published in Issues in Accounting Education, Journal of Accounting Case Research, Quarterly Review of Distance Education, Journal of Accountancy, The CPA Journal, Internal Auditor, The Tax Executive , and Journal of International Taxation , among others.

Professor Budd has been a faculty member at Brigham Young University since 2005. Prior to coming to BYU, she was on the faculty at Utah State University for three years. She received a BS degree in accounting at Brigham Young University and a MAcc degree in tax at Utah State University. Before pursuing a career in academics she worked as an auditor for the firm of PricewaterhouseCoopers LLP in its Salt Lake, San Jose, and Phoenix offices and continues to maintain her CPA license. Professor Budd has taught financial accounting at all levels, introductory managerial accounting, undergraduate and graduate auditing, and partnership taxation. She is the recipient of numerous awards for teaching and student advisement, including the Dean Fairbanks Teaching and Learning Faculty Fellowship, Brigham Young University; School of Accountancy Advisor of the Year, Utah State University; State of Utah Campus Compact Service-Learning Engaged Scholar Award, and the Joe Whitesides Scholar–Athlete Recognition Award from Utah State University. She has been active in serving on various committees of the American Accounting Association, including chairing the annual Conference on Teaching and Learning in Accounting.  Professor Budd is currently serving as the VP-Academic for the Teaching, Learning and Curriculum Section of the American Accounting Association.

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Corporate social responsibility and credit rating: evidence from French companies

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Nonpecuniary Benefits: Evidence from the Location of Private Company Sales

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