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Although recent advances in artificial Intelligence (AI) have led many to predict that automation will replace humans with machine workers in many fields, we usually think of machines replacing people in relatively unskilled tasks—not in high-skilled professions like tax, audit, or consulting.
And yet, the potential applicability of AI to professional services is exactly what the leadership of KPMG are asking about these days. To that end, KPMG have begun to explore ideas about where and how machine learning might be applicable to their business. According to Shamus Rae, Partner and Head of Operational transformation, KPMG recently developed a proof of concept that reached a 73% quality threshold in an unnamed professional services task after spending just two weeks preparing some training data and 45 minuntes training the engine” to do the task. Said Rae, “Clearly, this demonstrates the potential.”
Building on this work, a group of Imperial students undertook a Data Spark to assess a simple question: how much exactly will AI impact the future of audit services?
A primary challenge to the team, which included four MBA students, was to develop a model that links features of traditional audit work to innovations in AI and data science that have an impact on staffing levels. Building on guidance from Imperial researchers and interviews with audit professionals, the team developed a framework for relating AI to jobs and a simplified outline outline of two key steps of the audit process, the audit strategy and year-end audit, and mapped steps. To project the potential impact on staffing, the team then built a model that linked the framework to tasks and staffing for the audit steps in focus.
According to the model, AI has the potential to improve productivity in these two tasks of the audit process by over 20% in 5 years and over 65% in 10 years. Moreover, the team found that the largest share of these productivity gains are likely to come from supervised learning —a machine learning technique that reproduces human expert decisions through trained data sets. There are still many questions around the potential effects of AI and by attempting to quantifying it’s effect on a specific industry, this study starts to provide companies such as KPMG with tangible results and a strong argument to invest In AI.
The Imperial team’s research was conducted as part of the Data Spark 2017 scheme, run in association with KPMG. The Data Spark is an Imperial College London project that connects academics with business leaders to analyse real-world data. For MSc and MBA students, the six-week consulting project is an opportunity to work alongside academic and business mentors while gaining practical experience of business analytics: for business leaders, it is a chance to leverage the minds of academics to unlock new and unexpected value from their existing data.
Team members:
Salim Anbar-Colas (FT MBA), Fahad Jahangir (FT MBA), Adam Painter (Global MBA), Daniel Rose (FT MBA)
Academic mentors:
Dr Julio Amador, Dr Mark Kennedy
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