Part of a financial leader’s accounting strategy is working to mitigate a wide variety of risks. Mitigating risk is a data-driven exercise, recently augmented by increasingly intelligent AI machines empowered with cognitive technologies.

The key is data, the commodity driving the Analytics Economy. 

Never before has it been possible to collect, organize, and analyze so much information from so many sources so quickly, as it happens. 

Data can come from an infinite number of places, but the focus these days is on sensors, embedded by the millions, within the Internet of Things (IoT). When you combine raw IoT data with artificial intelligence applications based on predictive analytics, all manner of interesting facts come to light because AI is autodidactic (self-learning). The more you use it, the better the insights will become.

The Challenge of Risk Mitigation

It’s important to realize, however, that risk mitigation is a difficult task, even for a highly sophisticated program. In the final analysis, it doesn’t matter how complex a risk assessment model is; many risks are still difficult to evaluate for many reasons including they’re not well understood, the cause-and-effect relationships are highly intertwined, and sometimes the risk in question is “emerging.”

In the past, one of the problems with software-guided risk mitigation was that computers were limited to binary logic. In the manufacturing world, very few of the important questions we ask are straightforward enough to be settled by a binary “yes” or “no” answer. Inventory is a primary example of manufacturing risk mitigation. In a perfect world, with a perfect supply chain, every input would appear when needed, and not before. Instead, companies must prepare for the possibility of delays or disaster.

Finding Value in Fuzzy Logic

Before the advent of modern AI, the human brain was better than the computer brain at determining risk and assigning appropriate stock levels. That began to change in the 1960s, when mathematician Lofti Zadeh noted that human reasoning, unlike traditional computer logic, is not binary, but naturally categorizes possible events by degrees of likelihood. Asked to analyze a statement such as, “There is a risk that we won’t be able to procure widgets from our suppliers in NSW in August,” the traditional computer response is either 1 or 0, equating to TRUE or FALSE. This answer only functions to tell us what we already know, doing little to inform our procurement strategy.

A human, however, would classify the widget-shortage risk in shades of possibility, such as “somewhat true,” “very true,” “somewhat false,” or “very false.” These leveled responses facilitate the logic behind effective human decision making. In response to his observations, Zadeh created fuzzy logic, a system of categorization that gave computers the ability to reason a little bit more like humans and provide an increasingly accurate evaluation of uncertainty and risk. Although fuzzy logic techniques have largely given way to statistical probability models, the talking point is roughly the same: By facilitating non-binary categorization—that is, by allowing degrees of uncertainty between 0 and 1—AI’s analytical abilities are becoming increasingly human, simply faster and more thorough.

Gaining a Competitive Edge With AI

AI is particularly useful in the risk mitigation field when evaluating unstructured data. Aided by cognitive technologies such as natural language processing (NLP), AI can surface insights from unstructured data such as MS documents, email, social media, even mobile data. Considering that between 80 and 90 percent of enterprise data is currently unstructured, the implementation of cognitive analytics could provide companies with a decided competitive edge.

The beauty of AI is not just its ability to approximate the human thinking processes. Its most useful talent is found in its ability to learn. Already, we are becoming used to smartphones and websites that, over time, increasingly adapt their suggestions to what they’ve “learned” about our preferences. In the same way, cognitive computing techniques can be used as a part of our accounting strategy to identify and control risk.

Risk Mitigation With PositiveVision

Whether you’re already using AI or just diving in, information has never been more essential than now to ensuring profitable management of your business. If you’re looking to acquire and manage the right information to make timely decisions, it might be time to look at new accounting software systems to handle your data. That’s where PositiveVision comes in. Let us show you how to set your accounting strategy to maximize business analytics through AI to gain that competitive edge. Click here to contact an expert now.