When you stop to think about it, our real-time world is amazing. We move money with a few taps on a phone, communicate with anyone, anywhere, anytime and process huge amounts of data in the blink of an eye. No wonder it’s getting tough for risk and control functions like compliance to keep up.
Data-first, real-time compliance is already on the agenda for many businesses as they respond to the world’s accelerating digitization. Over the next five years, Hans Jessen and I believe AI will become an essential tool to reach this goal. In fact, we’re willing to go further and say that we don’t believe digital compliance can be achieved without it.
Compliance’s digitization challenge
As the world speeds up, lagging data and insights are making it almost impossible for compliance teams to keep up with regulatory obligations and manage the risks of noncompliance.
Global regulators are adding to the pressure as they adopt a digital-first approach focused more on data (inputs) than on returns (outputs). The change is already proving challenging. Our recent tax and finance survey found that 76% of respondents were finding the volume, pace and complexity of global tax reforms difficult to manage.[1] The costs of complying with emerging digital tax administrations (DTAs) is also a concern. Surveyed companies expect to spend $11.1m on an average over the next five years complying with digital filing.
Compliance models are changing in response to this new environment. Compliance is moving upstream, built more proactively into design, operations or the supply chain. And we see growing interest in using technology to achieve real-time or near real-time compliance.
AI will be a compliance essential
Within this context, AI’s supercharged business intelligence will become an essential tool. Data-driven prediction will detect anomalies faster, and arm compliance and business leaders with valuable foresight on high-priority risks. Intelligent automation will be similarly transformative for assessing the impact of rapidly changing regulations and creating regulatory documentation.
Internal fraud offers a good example of AI’s predictive benefits. Because perpetrators often embezzle small amounts sporadically and try to cover their tracks, it’s a particularly difficult problem for companies to detect, predict and control. The cost is also high — expense fraud alone has been estimated to cost companies US$1.9b per year.[2]
AI’s analytic speed and predictive power means it can detect suspicious employee behavior patterns or transactions far more quickly and effectively than non-intelligent systems. It also generates fewer time-consuming false positives, as well as empowering compliance to forecast potential fraud before it happens.
On the automation side, AI’s manual time-saving is substantial. Financial institutions are already using AI to automate legal and compliance documentation, and natural language processing (NLP) is helping other large companies review lease contracts against changing accounting regulations.
