There is currently much fear and hype around Artificial intelligence (AI) and its impact on accountants. In Gartner’s Hype Cycle of Artificial Intelligence, the majority of AI applications are climbing and cresting the Peak of Inflated Expectations—meaning that expectations are high and many technologies are already failing to meet those expectations. But this doesn’t mean that AI is going to go away. It means that we’re starting to push through the hype and figure out realistic applications for AI—some of which will be useful to accountants and many of which will be leveraged by the organizations we serve.
The Spectrum of Artificial Intelligence
Part of the challenge with an emerging technology is that there is often an unclear definition of what technology is, and what it is not. As vendors’ marketing departments seek to leverage the hype to drive sales, they often start referring to technology buzzwords in a loose sense and cause additional blurring of the definition.
To help properly set the stage for analyzing the implications of AI on accountants, I’m going to use the broad AI technology categories of:
- Machine learning: the ability of the computer to recognize and apply patterns, derive its own algorithms based on those patterns, and refine those algorithms based on feedback.
- Deep learning: the ability of the computer to identify relationships and associations, and apply those in similar circumstances (this partly what our brain does).
- Machine reasoning: the ability of the computer to apply its “understanding” of data, relationships, rules, etc., to “think” though the implications of a particular set of information and provide some analysis or interpretation.
- Natural language processing: the ability of the computer to “understand” human speech.
- Computer vision: the ability of the computer to “see” images and “recognize” people, things, activities, and states (e.g. happy, sad, in motion, etc.) in those images.
Of these categories, machine learning has the broadest available applications and its functionality can most greatly supplement the faculties of an accountant, so this article will focus on machine learning.
You’ve Already Experienced Machine Learning
Machine learning is good at “inductive reasoning”—where based on a set of existing data points or examples, a computer can figure out what the “rules” are to determine a result. Take a step back to your statistics class in university and you may remember techniques like linear regression, the measurement of the correlation, and reliability of various data points. At a very basic level, these are the types of analyses that machine learning algorithms are applying to predict outcomes. With the computational power of a computer behind it, machine learning can process thousands of data points about a given set of situations to figure out which ones are relevant, and which are not, and then apply the inferred rules to another similar set of data to predict outcomes.
Amazon’s, Kindle’s, and Netflix’s suggestions to users are a great example of this. Netflix is able to use your ratings of other shows in its library and data points, like genre, director, actors, etc., to predict whether you will like another show. Kindle does something similar for books. Amazon uses product views, other shoppers’ purchase history, and complimentary items to those in your cart to suggest additional products you may like. Whether you realize it or not, you’ve already had machine learning applied to try and predict what you may like.
The Impact of Biased Data on Inductive Reasoning
Because inductive reasoning “learns” from existing data sets, it is important to understand whether the data sets that are used to “teach” machine learning algorithms have inherent biases. A simplistic example of this is if you only watch horror movies on Netflix and rate them all high, and you also happen to watch other low-budget movies on Netflix because you can’t get them on another platform, Netflix will probably predict that you only like horror and low-budget movies. Netflix doesn’t know that you actually like a wide variety of movies—it just doesn’t have access to that data.
As you can see, there is the potential for positive and negative impacts from biased data. If the biased data represents an outcome that you want, then using all the data points from that biased data is a positive impact. On the other hand, if that biased data causes machine learning to provide analyses that will result in a negative impact, then the proper safeguards must be put in place to prevent or detect the negative impact. Or phrased in a more familiar way: we must ensure that internal controls are implemented to manage the risks associated with a negative impact from the application of machine learning.
Machine Learning Implications for Auditors
There is a high potential for machine learning to provide augmented analyses to auditors. Note that I did not say that it would replace auditors—machine learning is just another tool in the auditor’s belt of Computer Assisted Auditing Tools and Techniques (CAATTs).
Instead of sampling data, auditors can push an entity’s entire ledger through automated analysis. This, by the way, is not AI or machine learning; this is a capability that already exists in tools like IDEA and ACL. These tools can perform a variety of analyses, designed by humans, and then provide lists of exceptions for the auditor to evaluate. Machine learning comes into play as the auditor confirms the exception or invalidates that exception and the machine learns to “look” at the auditor’s conclusions and try to identify additional data points about the positives or negatives to apply to additional exceptions it identifies. In this way it learns to better identify exceptions.
The data bias risk in this application is that if an auditor incorrectly clears items that should be confirmed as exceptions, machine learning would start to clear other items that should be exceptions. So a review process must be put in place to ensure that cleared exceptions really are not exceptions. The converse is also true for confirmed exceptions that should not be exceptions.
In a more advanced application, a set of transactions could be provided to an AI tool and machine learning would identify the patters in the transactions and be able to identify what “normal transactions” look like. Using this method, it would then identify those exceptions that don’t match the norm as exceptions. This application of machine learning is also subject to data bias since its picture of a normal transaction is based on the set of data provided. If the data set that was used also happened to have a high incidence of fraudulent transaction, then those fraudulent transactions may be interpreted to be normal transactions since they are highly present in the data set that the AI learns from.
There is definitely a future need for a human auditor even as machine learning starts to augment audit procedures. The auditor role, for both internal and external auditors, will switch from performance of the procedures to design of the procedures, interpretation of the results, and monitoring the effectiveness of the interpretation.
Machine Learning Implications for Management Accountants
The implications of machine learning for management accountants and other professional accountants working in business and government is even greater than it is for auditors. In addition to machine learning being applied within finance, it may also be applied in other parts of the organization and management accountants must ensure that there are proper governance and internal controls applied to machine learning throughout the organization.
Within the controllership function, machine learning may be applied to help with the classification of transactions. Inductive reasoning could be applied to the source data of historical transactions to help “predict” the classification of additional transactions as they are recorded. Since the products of many vendors have a fairly consistent natural classification, for the most part this is ok. However, there are some vendors that could be placed in different natural classifications depending on how their product is used. Take, for example, an email newsletter tool used to provide marketing and promotional emails to customers and classified as advertising spend. However, if it were used to generate employee newsletters, it may instead be classified as employee relations or an IT expense. Human validation of the classification for vendors with this risk may be necessary depending on the materiality of the potential error.
When used as part of financial planning & analysis (FP&A), machine learning can be used to analyze data to define or refine data models used for forecasting. The quality of the data set being used and the risk of inherent biases may again impact the quality of the predictions provided by machine learning. FP&A accountants must exercise care due to the impacts of the data sets used for their models.
Opportunities in Enterprise Use of Machine Learning
Organizations that are exploring machine learning must also address the additional governance and internal controls considerations for the associated risks. As departments outside of finance seek to employ machine learning, FP&A accountants have a big opportunity to provide their data analysis and modeling expertise to help other departments develop their applications of machine learning.
This is not an area that can be addressed by IT alone. A holistic view of the data, processes, and use of information provided by machine learning must be obtained. For each project, accountants in finance and internal audit must be sure to understand the compliance requirements, and assess the design of controls to mitigate machine learning risks from biased data.
Internal auditors also play an important on-going role in evaluating the design and effectiveness of the governance and internal controls over machine learning, and in evaluating the effectiveness of the methods chosen to reduce the risk of negative impacts from biased data.
Accountants Must Embrace Machine Learning
AI as an accountant replacement myth is really just more of the hype that will be proven wrong. Instead, increased use of AI will allow accountants to focus on providing better decision support rather than on data gathering and manual analyses. Increased use of AI will also require accountants to step up and address associated risks with AI through effective governance and internal controls.
Accountants need to look at both how we can leverage machine learning to facilitate our role as auditors and accountants. There is also a large opportunity beyond the finance context to guide other departments in their use of machine learning and help with the design of internal controls over their applications.
The corporate governance function must also be adjusted to address business strategy alignment of the risks presented by machine learning technology. A sub-function focused on data governance should be established to address both data bias risks as well as compliance risks, like privacy. There are areas that IT cannot address alone since they do not have the risk and controls expertise that accountants do.
By embracing machine learning as a tool, accountants can shift where we’re spending our time from performing menial data preparation and analyses to the drawing of insights from those analyses. Accountants’ expertise in controls design and understanding data biases can also be used to serve other departments in the organization as the departments seek to embrace machine learning.
Machine learning provides an unprecedented opportunity for accountants and we must embrace it to enhance both our careers and the competitive advantage it can provide to the organizations that we serve.