ACCA director sees AI/ML play greater role in accountancy
“A FUTURIST once famously said – the future is already here, it’s just distributed unevenly,” ACCA Global Director of Professional Insights Jamie Lyon told Tech Wire Asia.
“Point being that it’s not about waiting for some mythical future. This stuff is already here – indeed 17 percent of Malaysia survey respondents report active work on adopting machine learning in their organizations and a further 20 percent report being in discussion to do so.”
According to Lyon, machine learning (ML) is increasingly entering the fabric of accountancy workflows to drive value, oversight, and ethical sustainable adoption.
“You don’t think of predictive texting in your smartphone as AI, its become embedded into normal ways of operating, and that may hold some parallels for the future of accountancy and AI.”
According to a recent ACCA Global report on the subject of ML in accountancy, the capabilities that machine learning offers could assist the work of professional accountants in various ways over time.
The report, and Lyon, both highlight that the biggest reason the professional body believes ML will play a critical role in the future of accountancy is the explosion of digital data and the phenomenal growth in transactional data that will warrant more intelligent accounting, auditing, and compliance.
Given the expert insights from analysts at IDC and Gartner, it seems ACCA is on the right track.
A whitepaper issued by IDC recently pointed out that the think tank believes that the global datasphere will grow from 33 zettabytes last year to 175 zettabytes by 2025.
In a world where we still talk in terms of gigabytes, terabytes, and petabytes, zetabytes are a big leap forward in terms of data and storage.
ML to save accountants from being buried under mountains of data
For financial services executives, especially in accountancy and auditing/compliance, this is a big step forward because it’s not only the amount of data available to them that is increasing but also the veracity and reliability of transactions as a direct result.
As a result, professionals — accounting as well as IT — have begun developing AI and ML powered tools to simplify workflows while simultaneously leveraging the volume of data available.
“In bookkeeping, ML systems have already been in full production for a few years, particularly in the SME enterprise sector,” pointed out the ACCA Global report.
“ML techniques like supervised and unsupervised learning will become increasingly familiar to many organizations,” explained Lyon.
“So looking ahead I see the data explosion increasingly supporting the business use case for ML and more sophisticated related uses like deep learning and natural language processing which enable analyses of more complex unstructured data.”
Lyon pointed out that in the future, he believes there will be greater convergence across technologies. For eg, robotic process automation (RPA) will increasingly overlap with AI to create IPA (Intelligent Process Automation).
“So it’s not just about process automation, but learning from data and driving process improvement as well.”
While there are many considerations that must be taken into account before ML can make a debut in mainstream accounting at scale, companies must prepare to overcome the biggest obstacle: The lack of skilled staff to drive the adoption.
Accountants need to acquire an appreciation for ML
“Professional accountants need an appreciation of how ML can affect their organizations. Use of ML could affect the way they track and influence how the organization creates value, require changes to risk and control mechanisms, or perhaps create ethical considerations,” according to the ACCA Global report.
While the profession might never demand coding skills, an ability to understand, trust, and rely on algorithms will be critical in the future.
An ACCA Global survey showed a high degree of skepticism about allowing algorithms to take the lead in areas requiring complex judgment and interpretation among accountants.
Depending on the type of decision needed, only between one-tenth and one-third of respondents thought that an ML algorithm could lead or be given full reliance when applying such judgment.
In the future, the attitude towards ML needs to change.
Accountants need to not only understand and trust ML algorithms but also help shape their development when the organization is able to find talent to build its own solutions to augment accountancy workflows and capabilities.
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