Working Together to Build a Big Data Future
By: Alice Bonasio
Tags: Fintech, Artificial Intelligence, AI, Data Science, Big Data

To leverage Big Data and build an effective Artificial Intelligence infrastructure, enterprises must embrace collaboration.

In a digital economy, the rule of thumb tends to be that the smarter your use of data and technology, the more of a competitive edge your business has. A recent report by Teradata, based on over 260 interviews conducted by research firm Vanson Bourne with senior IT and business decision makers, found that there is widespread enthusiasm for adoption of AI, with 80 percent of enterprises reporting that they were already investing in the technology in some capacity, and over 30% planning to expand their investment in the are over the next 36 months.

“C-level executives — namely CIOs and CTOs, maintain they are committed to AI in their enterprise, because of the expected ROI over the next 10 years.” The report therefore concludes that executives will accept those challenges as the long-term benefits clearly outweigh near-term pain. In fact their analysis showed that over a five-year forecast, organisations effectively expected to double their money when investing in AI: For every $1 spent on AI technologies, organisations predicted a return on investment of $1.23 over three years, $1.99 in five years, and $2.87 over a ten-year period.

The Economist — in a phrase that has since become rather cliché — put forward the idea that “data is the new oil,” and World Economic Forum has now designated Big Data as a new kind of economic asset, just like currency or gold.

A study by the MIT Center for Digital Business confirms that data-driven businesses do indeed have the edge. It surveyed 330 leading U.S. Businesses and found that companies that focused strongly on data-driven decision-making had an average of four percentage points higher productivity and six percentage points higher profits.

Yet as these results indicate, it would perhaps be more accurate to say that data is in fact the new oxygen. Whereas it is still true that businesses that best leverage data and AI will gain a significant competitive advantage, it is probably fair to say that those that fail to make their organisations data-centric will eventually not be able to survive at all in the digital age.

While most people agree on the essential role that Artificial Intelligence and Data play in their organisation’s success, there are significant challenges. The overwhelming majority of business leaders surveyed in the Teradata report anticipated major barriers for adoption within their organisation, with roadblocks ranging from an inadequate IT infrastructure to shortage of in-house talent.

In their book The Sentient Enterprise, The evolution of Business Decision Making — launched at the Teradata Partners’s Conference last month — Oliver Ratzesberger (Teradata’s Chief Product Officer) and Mohanbir Sawhney from the McCormick foundation talk about how agility is key to getting the enterprise to the sentient point where it can analyse data and make decisions in real time.

The crux of the problem enterprises face lies not in the difficulty of gathering data, but in extracting insights and then turning these into actionable processes. The reality is that we live in a time of data overload, and companies can easily find themselves trapped in reactive mode, spending most of their time sifting through mountains of data and making decisions only when problems emerge, rather than anticipating them.

To tackle these problems, enterprises need access to key talent and infrastructure so as to enable the leveraging of Artificial Intelligence and Big Data. Increasingly, this is not being done “in house” but rather in partnerships with dedicated providers. These go beyond the traditional SaaS and becomes much more of a “Platform as a Service” model that incorporates complex customization and consultancy services.

Teradata’s Think Big Analytics team, for example, worked with Danske Bank to create a fraud-detection platform that uses machine leaning to analyse tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. Together, they built a framework within the bank’s existing infrastructure, crating advanced machine learning models to detect fraud within millions of transactions per year, and in peak times, many hundreds of thousands per minute:

“Using AI, we’ve already reduced false positives by 50% and as such have been able to reallocate half the fraud detection unit to higher value responsibilities,” explains Nadeem Gulzar, Head of Advanced Analytics, Danske Bank. “There is evidence that criminals are becoming savvier by the day; employing sophisticated machine learning techniques to attack, so it’s critical to use advanced techniques, such as machine learning to catch them.”

“All banks need a scalable, advanced analytics platform, as well as a roadmap and strategy for digitalization to bring data science into the organization.” says Mads Ingwar, Client Services Director at Think Big Analytics. “For online transactions, credit cards and mobile payments, banks need a real-time solution — the platform we developed with Danske Bank scores transactions in less than 300 milliseconds. It means that when customers are standing in the supermarket buying groceries, the system can provide immediately actionable insight. This type of solution is one we’ll begin to see throughout organizations in the financial services industry,” he concludes.

Fintech company Verifi, also collaborates with banks and merchants to connect the multi-layered datastreams and combat fraud. The work the company does with is based on optimising data transmissions using APIs (Application Programming Interfaces). This is much more efficient because whereas legacy systems rely on processing large numbers of files sent in bulk, APIs can process data in real-time.

The Verifi system collates APIs from the merchant shopping cart, customer relationship management (CRM) system, shipping data system, and others, and provides the merchant with better information to handle charges disputed by consumers, allowing them to often resolve the issue directly rather than have the bank issue a chargeback. Merchants benefit as they’re able to control the message to the consumer, and banks are happy because the sale remains and they reduce their operating costs. It the sort of technology enabled, data-driven system that is a true win-win.

One of the problems that such systems help tackle is so-called “friendly fraud,” a term used to describe a situation when a customer experiences “buyer’s remorse” and “tries it on” by putting in a claim directly with their bank or card issuer for a refund, when the sale did in fact legitimately occur. Julie Conroy, Research Director of the Aite Group says that in the U.S. friendly fraud and chargebacks would likely near $130 million, in Q1 2017 alone.

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