The last decade has ushered in a perfect storm of disruption for the financial services sector – arguably the most data-intensive sector of the global economy. As a result, companies in this sector are caught in a vice. They are squeezed on one side by highly dynamic compliance and regulatory requirements that demand ever-deeper levels of reporting. And they are squeezed on the other side by legacy platforms that increasingly cannot handle these demands in the timeframes required.
Meanwhile competitive pressures are mounting to use available data to bring more and better products and services to market via better customer segmentation analysis and optimal customer service. Rapidly rising data volumes and data types – mostly unstructured – have strained legacy systems to the limits. Instead of funding innovation, many IT budgets in this sector are funding ‘defensive’ applications for compliance and fraud detection. Also these aging systems are largely unable to aggregate, store and analyze data from customers accessing services from different access points, like smartphones and tablets. Then of course there are the different social media ‘sounding boards’ where customers offer up candid opinions on the services they receive, potentially invaluable information that legacy systems cannot process. That’s a perfect storm by any definition.
For financial services companies, the arrival and rapid development of Hadoop and Big Data analytics over this same past decade couldn’t be more timely. The ability of Hadoop platforms to store gigantic volumes of disparate data matches perfectly with new incumbent data streams. Meanwhile Big Data analytics solutions offer unprecedented opportunities to actually profit from compliance while keeping fraud at bay and enabling new revenue streams. Below are several use cases for Hadoop and Big Data analytics already in full swing.
Risk Management. Post-financial crisis regulations like Basel III posited liquidity reserve requirements forcing lenders to know precisely how much capital they need in reserve. Keep too much and you tie up capital unnecessarily, lowering profit. Keep to little and you run afoul with Basel III.
Hadoop allows lenders to tap into an ever-deepening pool of new data used to analyze credit risk, counter- or third party risk, and even geopolitical risk. Hadoop does this by utilizing simulations that use huge volumes of data and require massive parallel computing power, which you find in a typical Hadoop cluster.
And unlike with existing systems, the Hadoop platform will perform the analysis quickly enough to have lenders make informed decisions – as in when markets open. Legacy systems can take days to perform the same analysis, and at a much higher cost. The bottom line is Hadoop and Big Data analytics offers far deeper and far faster compliance analytics, and unrivalled scalability to deal with any new curve balls regulators can throw.
Sentiment Analysis. Whether it is through using tweets, Facebook, Yelp, Google Reviews or literally hundreds of other opinion outlets, customers are publicly stating their sentiments by the tens of millions. Collectively, these sentiments are vital to making product and service improvements and making better decisions overall.
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