RegionsAsia PacificBig Data: Hadoop, hype and hope

Big Data: Hadoop, hype and hope

Among the topics on the agenda at this week’s first Singapore Fintech Festival is an analysis of how financial institutions can maximise the benefits of big data.

While big data and data analytics may have been over-hyped, a panel of experts at this week’s inaugural Fintech Festival in Singapore offered valuable insights about how financial institutions can actually make it useful.

There might be complaints about data quality, but the real challenge lies in ensuring that all the data is accessed, according to former banker Navin Suri, now chief executive officer (CEO) of advisory firm Percipient Partners. Financial institutions need to overcome the three key challenges of perceptions: that it is too expensive to bring in all the data; it’s overly complex, and the speed of data coming in is too slow. “We need to handle data volume; quality comes later,” he stressed.

Standard Chartered Bank group chief information officer (CIO) Michael Gorriz noted that most data is accurate. The bank knows which mobile numbers are correct when customers use them for mobile banking, for example, and it has already verified demographic data. “Data is already correct.”

Challenges and opportunities

A key challenge with using data, noted Suri, is that even when fintechs are doing innovative things with data, the bank is not ready to integrate it. To overcome that issue, Gorriz’s bank is working on a three-stage process to make data more useful. Along with looking at the customer’s balance sheet, which is normally outdated, it is analysing payment patterns and as well as external data to get a true picture of the customer. “If you have more data, you might give him a bigger loan,” he suggested. “Big data can enable the economy.”

The most compelling application for data analytics is automation of repetitive tasks, Gorriz added. “We should think about whether these are right for the people or the bank. Put machines in there that can do the scanning, (and) they can do it more precisely.” The other key opportunity is investment management, where bigger data sets can help select the right investments.

Suri agreed that robo-curation platforms for wealth management offer tremendous opportunity, since they can deliver personalised information to each client. They still need to be developed further however, because “there isn’t enough financial market glossary available to be able to train a machine to read and send out materials.” Sufficiently robust glossaries of financial terms simply aren’t available, he explained.

The biggest challenge is simply the time typically required to use innovations from fintechs. “It takes six months for proof of concept (POC) to be approved, three months to set up, then sales,” said Suri. “It is nine to 12 months to break into a bank and start-ups run out of cash.” To succeed, he advised banks, don’t try something unless they’re really likely to buy.

Asked how financial firms and tech firms should work with each other, Gorriz replied: “we can learn a lot from fintechs because they bring a fresh way of thinking into it. What we have to do is be more adventurous. We have to find out what the customers really need.” The other area he cites is risk management. “Risk is something where he sees big data having a lot of value. Big data is giving answers that you did not have before.”

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