Machine learning will become reality in the near future

  • Research reveals machine learning is becoming more relevant for many organisations as the hype increasingly becomes reality
  • Huge opportunities for better business intelligence and across a range of activities carried out by accountants
  • Adoption of machine learning needs to be based on legitimate business need rather than just wanting to be seen as using AI
  • Ethical challenges lie ahead – accountants need to align professional competence and due care with AI and machine learning

In Pakistan, 31per cent of accountants currently see AI as all or mostly hype, while 65 per cent say that in three years’ time it will become a reality, according to global research.

The findings are published in a new report from ACCA (the Association of Chartered Certified Accountants) Machine learning: more science than fiction which highlights how new tech developments have a massive potential for the accountancy profession. The report focuses on machine learning, which is the ability of computers to ‘learn’ and make decisions or predictions based on analysis of large sets of data.

Narayanan Vaidyanathan, the report’s author and head of business insights at ACCA says: ‘Machine learning is a critical area of development for accountants. Looking ahead it will be crucial to understand its value and benefits, as well as theethical challenges it presents.In all this, the starting point has to be a legitimate business need with a clear understanding of what it can bring to the organisation.

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‘AI and machine learning can add value to the work accountants do – from generating valuable insights for business decision-making, to fraud detection, risk assessment, understanding complexities in taxation and also with more effective non-financial reporting.  So the accountancy profession needs to understand how AI and machine learning works, especially given its role in influencing the trust we have in the decisions of these systems.’

Sajjeed Aslam, head of ACCA Pakistan adds: ‘Pakistan’s tech sector is growing, but when it comes to AI, accountants are trying to see through the hype to understand the realities. As with all technology, with power comes responsibility and in the case of machine learning ethical considerations are never far away. Accountants need to consider and manage potential ethical compromise from decision-making by algorithm, such as the risk of bias in the data set that feeds them and the issue of accountability for decisions made.’

The results for Pakistan show that a third has no plans to adopt machine learning in their organisation, with 17 per cent undecided about it. Twenty-eight per cent are having initial discussions or exploring concepts, compared to 24 per cent globally.Only five per cent are at advanced testing stage, with ‘go live’ in three to six months.However, nine per cent are in full production mode and dealing with live data, beating other countries in the sample, for China and Malaysia who are at five percent respectively.

The main barriers to adopting machine learning are lack of skilled staff at 51 per cent. Some 53 per cent cite cost as a barrier, with 17 per cent admitting there was no clear benefit from using machine learning.

The report emphasisesthat at a minimum all finance professionals should know how AI is evolving and be alert to how the developing capabilities could overlap with their impact on their roles.To prepare for the digital future, ACCA already examinesa range of digital topics within its Masters level ACCA Qualification. It has also enhanced the digital content across many of the exams for students, while also ensuring digital is weaved into members’ continuous professional development.

Sajjeed Aslam concludes: ‘Machine learning’s entrance into the accountancy mainstream is a huge opportunity here in Pakistan, but also globally. This is an area where professional accountants have the chance to develop a core understanding of emerging technologies, building their digital skills alongside their communication skills so they can explain the results really well. They can then truly benefit from the ability of technologies like machine learning to support them with intelligent analysis of vast amounts of data.’

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