What is the real value of AI in finance?

Dubai

Most finance executives would agree to this statement that a company’s data is its most ‘valuable weapon’. After all, finance professionals have built whole careers on data and numbers. And yet, very few of today’s finance teams make the best use of the data they have.

Part of the problem is that there is just so much of it. Today, even small- to mid-size companies collect far more information than they can ever commercialise — and as more and more devices are connected to the internet of Things (IoT), the amount of data transmitted will grow exponentially.

By far, most of this data will be just noise. Only a fraction of it will ever be insightful and predictive. How do you sort the nuggets of gold from this mountain of sand?

That’s where artificial intelligence (AI) comes in — specifically, machine learning, said Arun Khehar, Senior Vice-President of Applications at Oracle ECEMEA.

In the past, he said that artificial intelligence was mostly rules-based. Business analysts would develop the rules, so that if the algorithms encountered a certain condition, they would automatically trigger a specific response.

Machine learning today compares the pattern to known, past instances of fraud to identify the likelihood of a new instance. Algorithms today help recognise potential fraudulent transactions and add those criteria to the fraud evaluation, learning from the data just uncovered.

According to R “Ray” Wang, principal analyst and CEO at Constellation Research, machine learning applications and analytics provide huge opportunities for customers to monetise their existing businesses and accelerate digital business.

Wang goes onto say: “Success requires a large corpus of data, strong expertise in data science, massive compute power, industry and domain expertise, and breadth of application solutions.”

Sounds like a tall order, Khehar said, but fortunately, finance teams can turn to established providers who have the ability to provide customised insights to help enhance business performance when combined with a company’s own data. This service is known as data as a service (DaaS).

“Drawing on DaaS, providers today are embedding adaptive intelligence — intelligence that combines big data with human knowledge and expertise — into their portfolio of cloud applications (customer experience, HR, finance, and supply chain),” he said.

These adaptive intelligent apps will learn from the data — providing recommendations like the most relevant offers for your customers; the best candidates to fill job openings; the best freight value for shipping, or the best payments terms for suppliers.

Khehar said that the algorithms continuously learn as they are used to providing up-to-date recommendations and offering users the best outcomes. The benefit of this approach to machine learning is that businesses don’t need to develop the algorithms themselves, as they are already available in the market.

“The wealth of data that was seen as a static-yet-key competitive differentiator is effectively a productive and dynamic asset that keeps on learning and keeps on giving. Imagine this; your most valuable weapon is now empowered to sift through sand and uncover nuggets of gold by itself and for itself,” he said.

To remain competitive today, he said that companies must access their data in real time to intelligently forecast and grow. Within the foreseeable future, every enterprise application will be a smart application that intuitively learns from interactions with an enterprise’s data.