Artificial intelligence in accounting: GAAP's “FAS133”

DOIhttp://doi.org/10.1002/jcaf.22407
AuthorLouis P. Le Guyader
Published date01 July 2020
Date01 July 2020
OTHER
Artificial intelligence in accounting: GAAP's FAS133
Louis P. Le Guyader
Department of Accounting and Finance,
Southeastern Louisiana University,
Hammond, Louisiana
Correspondence
Louis P. Le Guyader, Department of
Accounting and Finance, Southeastern
Louisiana University, Hammond, LA.
Email: lleguyad@aol.com
Abstract
Artificial intelligence (AI) is the necessary element to ensure that the most complex
modern accounting rules are implemented correctly. This is particularly true when
accounting interacts with increasingly sophisticated capital markets activities. The
last 20 years' of accounting rulemaking and the dictates of regulators have ensured
a need for AI. With user-friendly AI solutions, reporting entities will not shy away
from business practices because they fear they cannot account for them correctly.
AI will remain an opportunity and challenge to the accounting profession.
KEYWORDS
accounting, artificial intelligence, derivatives
Artificial intelligence (AI) was the necessary ingredient needed
to implement GAAP's FAS133rule on derivatives.
1
This
new AI requirement came as a shock! FAS133 was the regula-
tors' accounting solution to a market problem with derivatives
and it was too complex for manual implementation!
The accounting rules proved to be impossible to implement
without systems support in the larger and more sophisticated
hedging programs of SEC registrants and some private entities.
Regulators effectively demanded the creation of those AI solu-
tions. They allowed FAS133 to proceed after a 1-year delay so
that a minimal AI or systems solutionwould be available.
The FAS133-AIexperience has become a model for how
accounting can use AI solutions, and regulators may demand
them. The FAS133-AIstory is also an excellent illustration of
the various levels of functionality to which AI solutions can be
developed in both the capital markets and accounting contexts.
AI solutions in this context can range from accepting external
data feeds, automating mathematical functions, producing full
financial reports, and even making decisions. This article
reviews the FAS133 experience as a critical AI case in point.
1|REGULATORY BACKGROUND
This AI story is one reflecting how a regulatory push in a
given field can fuel the development of a new generation of
automation and AI.
1.1 |Derivative debacles fuel the need for
FAS133
In the 1980s and early 1990s, the U.S. capital markets faced
a number of derivative debacles. Losses from soured deriv-
ative positions were staggering and unexpected. Account-
ing was blamed for part of this problem. A governance
group was convened to study the matter under the leader-
ship of Paul Volcker, the former Chairman of the Federal
Reserve, as the Group of 30. One of their recommendations
was to improve accounting for derivatives(Group of
30 Reports, 1993).
The accounting community's response was to help the
SEC and the FASB formulate FAS133. This process took
more than half a decade and the results were not perfect. The
initial rule responding to the recommendation required
amendments that amendment process continues to this day.
FAS133is the well-known label for the FASB's rule
on Accounting for Derivative Instruments and Hedging
Activities(FASB, 1998). The 1-year delay was accom-
plished by issuing FAS137 (FASB, 1999). Despite the cost
of regulatory deliberations, FAS133 was stopped cold
because of this missing element, an AI solution.
In some cases it was not even a question of costthe AI
system cost versus the staffing cost for manual solutions.
Too much of the rule's functionality was outside accountants'
standard toolkits. Without the various capabilities of AI
Received: 1 June 2019 Accepted: 8 July 2019
DOI: 10.1002/jcaf.22407
J Corp Acct Fin. 2020;31:185189. wileyonlinelibrary.com/journal/jcaf © 2019 Wiley Periodicals, Inc. 185

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