By Arun Khehar, Senior Vice President of Applications ECEMEA, Oracle
“A company’s data is its most valuable weapon,” according to Steve Miranda, Oracle’s executive vice president of applications development. Most of the finance executives I know would agree with this statement. 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 midsize companies collect far more information than they can ever commercialize—and as more and more devices will be connected to the Internet of Things, 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.
McKinsey defines machine learning as “based on algorithms that can learn from data without relying on rules-based programming.” In the past, 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.
My colleague Rich Clayton gives a simple example: your credit card company might detect a transaction under the amount of $5. Then, within 10 minutes, it detects another transaction for $200. The company’s systems might be programmed to identify that pattern as fraud. That is a business rule that a person programmed into the application.
Machine learning takes this a step further, comparing the pattern to known, past instances of fraud to identify the likelihood that the credit card was hacked or stolen. While running these comparisons, the algorithms might discover that fraudulent transactions are likely to (a) be processed in separate locations, and (b) take place in stores where the credit card holder has never shopped before. The algorithms would add those criteria to the fraud evaluation, learning from the data just uncovered.
“Machine learning applications and analytics provide huge opportunities for customers to monetize their existing businesses and accelerate digital business,” according to R “Ray” Wang, principal analyst and CEO at Constellation Research.
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.
Fortunately, finance teams have established providers that they can turn to for help.
As a company that started in the database business 40 years ago, Oracle has so much anonymized data that we can offer data-as-a-service (DaaS) to our customers. When combined with your own company’s data, we can provide customized insights to help enhance business performance.
Here’s how it works.
Drawing on Oracle DaaS, Oracle is embedding adaptive intelligence into its portfolio of cloud applications (customer experience, HR, finance, and supply chain). These Adaptive Intelligent apps will learn from our data and yours—providing recommendations for the most relevant offers to your customers, the best candidates to fill job openings, the best freight value for shipping, or the best payments terms to your suppliers.
The algorithms continuously learn as you use them, to provide up-to-date recommendations and that offer users the best outcomes.
For finance professionals in particular, Oracle Adaptive Intelligent Applications will amplify Oracle ERP Cloud to help them negotiate the best supplier terms and optimize cash flow—especially during high-volume periods such as end-of-quarter. These recommendations are presented in-context while your employees use Oracle ERP Cloud.
The benefit of this approach to machine learning is that you don’t need to develop the algorithms yourself. They are already available within Oracle Cloud Applications. All you need to do is turn them on.
This approach is the future of AI, according to Dave Schubmehl, research director of cognitive systems and content analytics for IDC.
“Within the foreseeable future, every enterprise application will be a smart application that intuitively learns from interactions with an enterprise’s data,” said Schubmehl. “Oracle’s new Adaptive Intelligent solutions take this value proposition a step further. They are set apart from others by allowing the intelligent applications to learn from billions of anonymized consumer and business profiles available from Oracle.”