Why Banks Should Build Their Future on a Data Platform

I’ve just finished visiting and speaking at some of the world’s most exciting financial services and technology events, including Sibos in Toronto and the Singapore FinTech Festival. No matter where you go, it’s clear that banks, insurers, and other financial institutions are struggling with the ever-growing volume and complexity of data. However, it’s also clear that they are excited about the potential of using data to drive sales, manage risk, and cut costs.

The solution to both managing data and maximizing its value is to create what I and others at Intel like to call a ‘data platform’: a new technology, business process, and cultural foundation for your business that’s ready for today and a very unpredictable tomorrow.


But why are organizations struggling with data? First, there is the rapid growth in the volume of information. This is being driven by everything from the rise in unstructured data such as voice calls to increases in the information that institutions must hold about their customers.

All this data is fast becoming a tsunami. For example, we’ve been talking to a bank in China that expects its customers to want to connect an additional five smart devices to their bank accounts within just a few years. Those new devices might include advanced cars with integrated toll payment mechanisms connected to the customers’ accounts. Given the bank has more than 300 million customers, this will mean adding 1.5 billion sources of data.

Some data is also becoming more sensitive. For instance, the arrival of Europe’s General Data Protection Regulation in 2018 will significantly raise the stakes—and the fines—for organizations that fail to properly protect customers’ personally identifiable information.


On the flipside, being able to gather, process, and act on data in real-time is now key to competitiveness in a world where customers want more personalized and responsive services from their financial providers. Smart operators are blending in artificial intelligence (AI) and machine learning to enhance their ability to meet these expectations.

A great example is OCBC Bank in Singapore. Speaking with me on a panel at the Singapore FinTech Festival, OCBC’s Donald MacDonald said the bank had increased its conversion rate for offers presented to credit card customers tenfold by using data in real time. Instead of sending blanket messages to customers based on traditional segmentation, OCBC now sends offers within seconds of a person swiping their credit card. Those offers are relevant to the customer based on their location, the shop they’re in, or another pertinent data point.

At the back end, Donald said OCBC was using AI to help analyze approximately 1,000 transactions a day that is suspected of breaching anti-money laundering rules. He said the bank could now review 800 of those transactions automatically, with the AI system investigating the transactions and generating reports on all of them in 30 seconds. By comparison, the bank’s specialized team previously took at least half an hour to investigate and report on each suspicious transaction. That’s an enormous productivity leap.


These kinds of gains are only possible by moving to a whole new platform—a data platform. The first step in creating this new foundation is to consider what customers want and need today, rather than what products and business units an organization needs to support.

Financial institutions should then consider what data is needed to meet those needs and the infrastructure required to obtain and process that data in real time. Most importantly, they should consider the organizational and decision-making changes that will be needed to move to such a customer- and data-centric operating model.

There is no doubt that change must start at the top and be driven by a leadership team that has a clear five-year vision for transforming the organization in a way that drives digitization and aligns its business groups, technology, data, and partnerships.

HPE promoting Intel Nervana AI Platform Fintech 2017

At Intel, we are pleased to provide our deep expertise in data strategy, highly scalable computing systems, networking, and storage to help financial organizations achieve these goals. This includes advising on and supplying AI, machine learning, blockchain, and other next-generation tools. These programs often start with solving risk and compliance challenges, then organizations discover that the same infrastructure and principles can be used to drive top-line revenue.

Realizing a transformation will also require new talent and skills within organizations—including developing or finding people with the business acumen to use the many insights that will arise from having more data and advanced data analytics capabilities.

As Richard Lowe from Singapore’s United Overseas Bank added during the panel discussion at the FinTech Festival: “We need more of data science with some sort of business acumen. I might be looking for unicorns here, but it’s so that the communication with the business people who understand their customers really well, and understand their products really well, can communicate effectively with these very highly technical and analytical minds. That’s the talent bridge (we need).”

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