Ernst & Young (EY), a leading professional services firm, has taken significant strides in integrating artificial intelligence (AI) and machine learning into its auditing processes, leveraging a substantial $1 billion investment in talent and technology. This strategic move aims to enhance efficiency and accuracy in audit operations by harnessing modern technological advancements.
Over the past decade, EY has progressively incorporated AI to refine its audit methodologies, initially relying on legacy systems for data analysis. "We started with Excel and moved into business intelligence solutions, but we were dependent on our auditors spotting the outliers based on tables and charts," explained Marc Jeschonneck, EY's Global Assurance Digital Leader. Currently, the firm is exploring new frontiers by utilizing AI to detect anomalies within audit data, marking a significant evolution in its technological approach.
Central to this evolution is the development and use of a general ledger anomaly detector. EY has embedded AI capabilities within its GL analyzer, analysing over 800 billion line items of general ledger data annually. This includes features like online detection with time series regression, which represents an advancement aimed at more precisely identifying inconsistencies in financial records.
Beyond anomaly detection, EY's AI applications extend to its EY Canvas platform, which supports more than 500,000 users worldwide, including clients and EY professionals. The platform incorporates a recommendation engine powered by Canvas AI, designed to highlight potential risks and aid auditors by referencing prior insights garnered from similar sector engagements. "It really focuses their attention on what we think matters most," Jeschonneck noted, emphasising the platform’s ability to streamline risk assessment processes.
Document intelligence is another domain where EY applies machine learning, albeit with existing mathematical limitations. The firm is exploring generative AI capabilities to bolster document summarisation and search functions, geared towards enhancing user experience and audit quality. Jeschonneck highlighted the focus on ensuring data quality, stating, "The quality of the respective results is highly dependent on the quality of the underlying data."
EY's careful approach to AI utilisation involves aligning with regulatory standards and maintaining client confidentiality. The firm implements rigorous testing and documentation processes when deploying new technologies. "Making sure that you know about the respective risks, the guardrails that need to be put in place here, is essential for us," Jeschonneck remarked, underlining the necessity of careful monitoring and certification.
Furthermore, EY collaborates closely with tech giant Microsoft, deploying tools like Power BI and Microsoft Azure to advance business intelligence and streamline administrative tasks. Microsoft Copilot, for example, is instrumental in boosting productivity among EY auditors by automating routine processes.
The global nature of EY's operations enables the firm to draw innovations from its member firms worldwide. Developments such as the general ledger analyzer, initiated in the U.S., and the time series regression analysis, a collaborative European and U.S. effort, exemplify the shared technological contributions across EY's international network. Japan's contribution also includes the inception of the general ledger anomaly detector.
Ongoing training in AI for EY's personnel ensures that the workforce is well-equipped to maximise the benefits of advancing technologies. "Technology really gives new opportunities to the people," Jeschonneck asserted, highlighting the critical role of skilled professionals in leveraging AI advancements effectively.
The integration of AI and machine learning within EY's audit procedures is reflective of the broader shift towards technology-driven services in the professional sector, aiming to set new benchmarks in accuracy, efficiency, and client service offerings.
Source: Noah Wire Services