EMEA BANKING
Big data, big questions Widespread industry digitalisation means that banks are not only custodians of wealth, but of data too. How can they dovetail the storing and interpretation of this data with improvements to the client experience? Kerem Tomak, Divisional Head, Big Data & Advanced Analytics, Commerzbank discusses the industry’s progress so far Like any large corporate, a bank has access to enormous swathes of data. But data collection and, indeed, storage is only one part of the puzzle. To better understand their clients and ultimately improve their banking experience, financial institutions must find ways to analyse this data more effectively – and find meaning in the otherwise unintelligible.
more than 100 petabytes of data exist worldwide; in five-to-10 years, this will be considered “small”. The meaning of “data” has also fundamentally changed over the same period. It isn’t only ones and zeros arranged in rows and columns but other data sources including video and audio files. Even what (or who) can generate data is changing. For instance, today’s smartphone user is a constant data generator: digital interactions, such as SMS messaging, emails and tweets, create enormous batches of unstructured data.
This presents a challenge for banks: either we keep up with the exponential growth of data or risk being left behind. The scale of the task at hand is clear when we consider that 90% of all data in existence has been created So, how are banks rising to the in the past two years. 1 Equally, this challenge of harnessing big data? By applying modern analytical techniques is an unprecedented opportunity for learning and advancement. Therefore, to their digitalised infrastructure, the sooner that banks leverage its banks are building crucial data wealth of data in more creative ways, insights, which can, in turn, inform the better outcomes this will create how banks can better support for the end-client. individual clients – whether that be by detecting fraudulent transactions, Putting data to work reducing their exposure or by suggesting new modes of working. So, how can banks begin to make sense of their data and put it to Changing definitions work? This process, we believe, is part science and part art. Big data First, though, what does “big data” storage solutions are firstly required mean? At a basic level, it refers to gather the data. A modern example to data sets that are too large is Hadoop: a purpose-built solution or complex for traditional dataprocessing software to deal with. Yet, designed to collate various data types en masse, which helps to improve the volume, variety and velocity of the speed at which users can access, data included within this definition manipulate and clean data. has evolved considerably over the past 30 years: back then, 30 megabytes, or the equivalent today, of three minutes of high-quality video footage, would have been considered “big”, yet, today, the average smartphone in 2019 can easily store this amount of data 1,000 times over. To put this into perspective, today
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Then comes the art. Here, what’s important is knowing how to use the data. Banks can build algorithms that can interpret datasets in order to glean insights. In a way, the “art” is akin to making the data tell you the story that otherwise meaningless
streams of data cannot. This will help banks to interpret scenarios facing their clients in a new way − eventually allowing them to spot fraudulent activity, reduce risk and suggest new products. In fact, by using this model, Commerzbank has already found success – connecting approximately 70 different data systems with scope for future additions. Big questions A challenge for big data will likely be awareness. While we may not realise it, the application of big data analytics is already commonplace, and drives many of our online interactions. Each time we search online, we create data, leaving a digital footprint that companies can use to tailor advertisement choices and goods specifically to us – as well as to curate a deeper understanding of their customers’ needs. For the financial sector, the goal is no different. Better understanding clients’ pain points is fast becoming a prerequisite for success. At Commerzbank, we recently developed a data-based loan solution for the manufacturing sector. Known as a “pay-per-use loan”, it calculates the repayment schedule based on machine usage. When machine usage is low, repayments are also low. But if there is an uptick in production – which will likely mean a rise in turnover – the repayment rate increases. This data-driven approach allows the client to better manage its liquidity, and match income to outgoings. Big future Banks need not tackle big data alone and should, instead, leverage existing solutions to meet client demands. Certainly, fintechs can