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How banks can harness Big Data: StanChart information chief

How banks can harness Big Data: StanChart information chief

With the explosion in data sources, data science and open-source data technologies, banks have to invest in the right tools, Standard Chartered's group chief information officer has said.

'We have invested to build our own ‘data lake’ – a state-of-the-art platform that allows us to embrace the data revolution and depart from the traditional data warehouses that were functionally limited, expensive and slow to use,' Michael Gorriz said in a recent opinion piece.

StanChart's India team has worked out how big data analytics could be used to identify potential instances of money laundering, and address financial crime risk, he said. 

'With the rise in regulation since the 2008 financial crisis, we are also exploring solutions to improve reporting that meets the requirements of central banks.'

In the strategic review undertaken by group CEO Bill Winters in 2015, the bank decided to invest $3 billion in improving technology.

Alluding to the existing debate on service quality versus privacy, Gorriz said what is most important is that people have direct control over their data and choose what they make available. 

'Generally, people don't mind giving out data if they get something in return. As long as customers are given a choice, see the benefits and are asked for their agreement, they are more likely to share their data.

'Banks and other service providers have to tread a fine line between being helpful and being intrusive.'

Data quality

According to Gorriz, data quality is one of the biggest problems in the Big Data space.

'As a bank, we are focusing on the root of this problem. We are looking at open standards like FIBO (Financial Industry Business Ontology) to help us achieve this.

'There are also novel techniques in the areas of machine learning and AI that are accelerating the convergence of data models across disparate sources.'

Despite the prevalence of smart algorithms capable of using data to derive intelligent conclusions, he believes we remain years away from being able to be rely on machines to run our lives.

'A colleague described a situation in which he received a threatening call from a debt collection agency, only to find out later that the machine had matched him with the data of someone else with the same name. Clearly, banks and many institutions still require experts in data quality governance.'

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