Accountants: This Emerging Tech will be a Reality in Three Years’ Time

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A total of 58% of member accountants surveyed by ACCA in 111 countries believe that AI will become a reality in three years’ time, said the accounting body recently.

According to ACCA which has published the survey results in a new report titled Machine learning: more science than fiction, 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.

“As with any technology, with power comes responsibility. In the case of machine learning ethical considerations are never far away,” said Narayanan Vaidyanathan, the report’s author and head of business insights at ACCA. “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.’

Both 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, he added.

“As a result, accounting professionals need to understand how AI and machine learning works, especially given its role in influencing the trust we have in the decisions of these systems,” Vaidyanathan noted.

ACCA advised all finance professionals should at a minimum know how AI is evolving and be alert to how the developing capabilities could overlap with their impact on their roles.

"Machine learning’s entrance into the accountancy mainstream is a huge opportunity, where professional accountants have the chance to develop a core understanding of emerging technologies, while continually building their interpretive, contextual and relationship-led skills,” Vaidyanathan predicted. “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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