Simplifying Compliance through Explainable Intelligent Automation

Bowden, James and Cummins, Mark and Dao, Daniel and Jain, Kushagra (2024) Simplifying Compliance through Explainable Intelligent Automation. University of Strathclyde, Glasgow.

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Abstract

We discuss how explainability in AI-systems can deliver transparency and build trust towards greater adoption of automation to support financial regulation compliance among banks and financial services firms. We uniquely propose the concept of Explainable Intelligent Automation as the next generation of Intelligent Automation. Explainable Intelligent Automation seeks to leverage emerging innovations in the area of Explainable Artificial Intelligence. AI systems underlying Intelligent Automation bring considerable advantages to the task of automating compliance processes. A barrier to AI adoption though is the black-box nature of the machine learning techniques delivering the outcomes, which is exacerbated by the pursuit of increasingly complex frameworks, such as deep learning, in the delivery of performance accuracy. Through articulating the business value of Robotic Process Automation and Intelligent Automation, we consider the potential for Explainable Intelligent Automation to add value. The solution framework sets out the Explainable Intelligent Automation framework, as the interface of Robotic Process Automation, Business Process Management and Explainable Artificial Intelligence. We discuss key considerations of an organisation in terms of setting strategic priorities around the explainability of AI systems, the technical considerations in Explainable Artificial Intelligence analytics, and the imperative to evaluate explanations.

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