Big Data in Financial Services

Data has become the most precious commodity as we enter the fourth industrial revolution powered by Artificial Intelligence. The foundation of the this edition of industrial revolution has been built on the increased automation & use of computers in the third era post 1970s. Technological progress and Financial markets development has gone hand in hand over the past few decades. The increased digitization of financial markets has highlighted the importance of data – 2.5 quintillion bytes of data is being produced every day & the pace is only accelerating with the advent of Internet of Things (IoT), smart city initiatives, data driven markets & the complete automation of every sector of the globalized economy. The chart below shows the Big data revenue growth Worldwide. The current projected trajectory clearly points towards an exponential growth in the coming years.

It begs the question then – Should the financial institutes become data companies since they have such a wealth of data collected from their customers? I mean it would make more sense for them to provide data analytics rather than following the current model of interest or fee based income. Let’s dig a little further and explore a few examples on how big data can affect the financial industry.

Hedge Funds have traditionally been marked as alternative investment tools that employ active investment strategies for accredited investors. Their eventual aim is to maximize returns for their clients by engaging in both domestic & international markets by using quantitative skills & hiring a lot of Math scientists. They have been increasingly leveraging external and new alternative data to make better investment decisions. For example – geolocation is used to estimate the number of people entering the shopping malls to predict the growth in sales ahead of the public quarterly reports.
Insurance industry has also seen a move from current flat-fee model of standard coverage to usage based insurance which is more suited to your lifestyle needs, thus turning into a pay-as-you-go service. This has been made possible by the increased use of technology, like trackers in your car or your cell phone usage. The data from these devices is analyzed to change the underwriting decision model & come up with the most cost-effective plan which suits the customers’ needs.
Banking is utilizing the data collected via the monitoring of the spending patterns of a consumer and using predictive analysis to infer if a larger purchase will be made. Depending on the size of the purchase the bank can then decide which financial product should be offered to the client. So rather than offering standard run of the mill solution the banks can offer the client a product that serves their exact needs.

All these examples are indicative of the fact that data has been used by financial services companies, but not to optimize the solutions presented to the clients but more as a by-product of the whole process. With the evolution of Big data, however, it is going to be used more as a core asset rather than a collection of numbers & patterns tucked away in files. The surge in production, collection & analysis of data has also given rise to new roles like Chief Data Officer, Data Analyst, Data Scientist etc. while bringing about new challenges for the Regulators regarding the usage of such data.

Going forward the financial institutions will need to consider the following to enhance of the value of data:

Volume – As said before the combination of behavior tracking & the Internet of Things keeps on increasing the amount of data points available to analyze

Velocity – The speed at which the data is being created marks the need for real-time analytics and the challenge for additional storage capacity

Variety – Provides the ability to handle structured/unstructured data from different sources

As this data transformation gathers pace, FinTech startups are playing a key role in providing the necessary tools to gather, analyse & process the data held by the financial institutions. Current use cases include credit risk profiling, converting quarterly reports into investment advice & complete audit risk review. With time more complex analytic tools based on AI, Machine Learning & Deep Leaning are evolving, which will provide smarter, more efficient & customized solutions based on all the previous factors aforementioned.

And while all these analysis have always been performed by banks & financial institutions, the availability of big data and advanced analytical tools has made it possible to perform these calculations in seconds instead of hours without any trade-off on accuracy. This in turn can hopefully restore the trust & profitability levels of the banks while for the consumers it creates a new form of digital finance which provides customized solutions to match their needs & lifestyle.

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