Machine Learning: More Science Than Fiction

August 12, 2019

Published: IFAC

By: Narayanan Vaidyanathan, Head of Business Insights, ACCA

Artificial intelligence (AI) is having an enormous impact on our consciousness. And machine learning (ML), which uses mathematical algorithms to crunch large data sets, is being increasingly explored for business applications in AI-led decision making.

Following several years with the belief that AI was the stuff of movie fantasy, now, with far more data and processing power, ML seems set to challenge that view.

This is an area with a wealth of terminology and a minefield of differing interpretations as to what they exactly mean. ACCA’s survey of members and affiliates ‘Machine Learning: more science than fiction’ reflected this challenge when asked about their understanding of terms such as AI, ML, natural language processing (NLP), data analytics and robotic process automation (RPA).

On average for any given term: 62% of respondents had not heard of it, or had heard the term but didn’t know what it was or had only a basic understanding, 13% of respondents had a high or expert level of understanding. This suggests considerable potential for greater education and awareness building among the accountancy community worldwide.

One way to describe AI is the ability of machines to exhibit human-like proficiencies in areas related to thinking, understanding, reasoning, learning or perception. ML is a sub-set of AI that is often understood as the ability of the system to predict or make decisions based on the analysis of a large historical dataset.

Essentially, ML involves the machine, over time, being able to learn the attributes of data sets and identify the characteristics of individual data points. In doing so, it ‘learns’ in the sense that the outcomes are not expressly programmed in advance. They are arrived at by the ML algorithm as it is exposed to more data and thereby determines data characteristics, in a sense, through learning by examples.


ACCA’s report begins with an introduction to the basics. This is because it is important to have some appreciation of what these applications are doing, to be able to trust such systems and to grasp how machine learning can be a step towards developing a greater level of machine intelligence. In this context, ‘intelligence’ refers to the ability of the technology, in certain circumstances, to make decisions or draw inferences, without there being an instruction to treat a given dataset in a fixed, predetermined way. But it does not mean that the technology has suddenly developed an independent consciousness – this is not about robots going on the rampage!

The market is recognising the power of ML with 2 in 5 respondents stating that their organisations are engaged with this technology in some way. This includes those who stated that their organisations are in full production mode dealing with live data (6%), advanced testing with ‘go-live’ within 3-6 months (3%), early stage preparation with go-live within 12 months (8%) and in initial discussions exploring concepts/ideas (24%). Applications for adoption range across multiple areas, including for example, invoice coding, fraud detection, corporate reporting, taxation and working capital management. The report explores various products and initiatives across these areas.

These findings reinforce the need for the accountancy profession to prioritise building awareness and understanding in this area, as organisations will increasingly need these skills. In fact the biggest barrier to adoption cited in the survey was the lack of skilled staff to lead the adoption (52%).

As with any technology, with power comes responsibility. And in the case of ML, ethical questions are never far away. Professional accountants need to consider, and appropriately manage, potential ethical compromises that may result from decision making by an algorithm. Who has accountability in this situation?

What is the risk of bias, given that ML algorithms will inevitably reflect any bias in the data sets that feed them? About 8 in 10 respondents were of the view that organisations have a responsibility for some form of disclosure to highlight when a decision has been made by a ML algorithm.

We consider a range of ethical considerations relevant to professional accountants, using for guidance, the fundamental principles established by the International Ethics Standards Board for Accountants (IESBA) International Code of Ethics for Professional Accountants (including International Independence Standards).

The ability of AI to take over jobs is a narrative often communicated in the media. And there is certainly some truth about the ability of these technologies to do a variety of tasks more efficiently.  But even sophisticated technology such as AI appears to struggle with the full contextual understanding and integrated thinking of which humans are capable. Despite advancements in AI, it does not yet appear to be the case that human oversight can be done away with completely; or that the technology can take into account human factors, such as when building client relations or leading successful teams.

ACCA’s work on the emotional quotient (EQ) strongly demonstrated the need, in a digital age, for competencies related to emotional intelligence ‘Emotional Quotient in a digital age’ (ACCA 2018). In fact as we look ahead, the Digital Quotient (DQ) and EQ are best seen combined for either to be really effective for professional accountants.

Even outside behavioural areas such as leadership, core technical activities require judgement and interpretation that draw on multiple considerations. ML can provide truly insightful information, using sophisticated algorithms to analyse historical data sets. But in some situations, a human may choose to take note of this but for perfectly valid reasons, make decisions based on additional/other factors, that do not follows patterns seen in the past.

Looking ahead, professional accountants have an opportunity to develop a core understanding of emerging technologies, while continually building their interpretative, contextual and relationship-led skills. They can then truly benefit from the ability of technologies such as ML to support them in the intelligent analysis of vast amounts of data.

As with any technology, with power comes responsibility. And given the power of machine learning, that awareness is all the more important.

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