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Home News Insights A Primer on AI Adoption
Insights • 2019-09-23

A Primer on AI Adoption

Mindbridge’s Gillian Fischer looks at how to lead complex change when adopting AI technologies.

We’ve all heard of artificial intelligence and you’ve probably encountered it today while using your phone or browsing the internet. While many individuals have started to embrace AI, the accounting and financial services industries are relatively behind. While 76% of banking CxOs agree (‘Realising the full value of AI in banking,’ accenture.com) that adopting AI will be critical to differentiate in the market, firms still struggle to understand, validate, and build out AI for their practice.

AI is transforming the accounting industry by allowing firms to process vast amounts of client data to report on behavior, trends, and anomalies that were previously undiscoverable. With clients looking for broader scope in navigating risks, ensuring compliance, and protecting reputation, AI makes it possible to ingest and analyze 100% of financial data and go beyond traditional rules to extract valuable insights.

A common example of how artificial intelligence algorithms are applied to accounting is the detection of material misstatements by auditors using ‘unsupervised learning.’

This set of AI techniques leverage the science of determining what is usual versus unusual to report on outliers in ledger data without bias or history, letting the data speak for itself.

In a 2017 KPMG survey report, ‘Audit 2025: The Future is Now,’ nearly 80 percent of respondents said that “auditors should use bigger samples and more sophisticated technologies for data gathering and analysis in their day-to-day work.”

By understanding the entirety of general ledger data and identifying outliers based on risk, rather than rules, AI identifies suspect transactions based on how they deviate from the data set, such as unusual payments or activities that would not normally be caught by traditional audit tests.

It also eliminates the back and forth with clients, as the complete ledger is available for the auditor to explore and dive into details as they wish, providing a richer picture of the client’s financials.

A recent fraud case with a consumer goods manufacturer provides an example. As explained on the ACFE Insights blog, an audit services firm in the United States used AI to analyze more than $2.8 million in fraudulent transactions.

Going beyond the capabilities of the auditor’s traditional tool, Microsoft Excel, AI was able to ingest and understand all of the data, reporting on transaction items of unusual amounts and accounts that warranted further investigation by the auditor.

The answer is definitively “no.” It cannot replace the experience and judgement of auditors, nor can it understand and manage the relationships between firms and clients. AI works alongside people, automating and accelerating large and complex data tasks and it assists with decision making when it comes to identifying misstatements and determining risk.

It is transformative technology and therefore any firm must carefully consider their adoption strategy and timelines. For auditors, AI warrants a re-evaluation of how audit planning, testing, and reporting are done, as well as understanding
the skills and competencies required.

As I’ll discuss in my talk at the Global Gathering Meeting, ‘How to Lead Complex Change when Adopting AI Technologies,’ bringing AI into any firm requires an investment into the people as well as the technology. From working with hundreds of firms worldwide, those that focus on change management and building data literacy tend to be more successful, rather than those that force new technology on unsuspecting staff.

These successful organizations typically perform the following steps:

1. Validate AI for their practices – understand the applications of AI to engagements and services, including the technology stack and staffing needs.

2. Build their AI adoption journey – considering the people and process impacts, and above all else, the change leadership, determine the phases of transformation.

This includes defining the success metrics, enablers such as training and communication, and timelines for
each phase.

3. Engage the initial team and build data competencies – build a small, but powerful, team of champions and build out the skills necessary for successful adoption.

4. Educate the team – to build a strong foundation across the firm, the entire team must understand the value that AI brings, through both educational resources and capturing and sharing insights and real use cases that align to the strategic objectives the firm.

AI adoption is not easy, but there is research-driven and experience-proven ways to smooth the process out for firms of all sizes and demographics. By attending my session, we’ll dive into more details on the above topics, see some real examples, and help you navigate your adoption journey by connecting it to business value, empowering your team, driving new behaviors, and building the necessary competencies within your firm.

Learn more about AI for audit at mindbridge.ai. MindBridge Ai is restoring confidence in financial data with Ai Auditor, the world’s only AI-powered auditing solution that leverages machine learning and AI techniques to augment human capacity and redefine reasonable risk assurance.

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