Hadoop Big Data Analytics Use Cases: Financial Services Banking on Disruption

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8 min read

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. The question is, how does a financial services firm efficiently gather, store and analyze such free-form data from so many sources?

The answer is by leveraging the Hadoop platform and its capability to enable machine learning and natural language processing to extract the pith from enormous stores of sentiment-based data. For example a bank may find that two in ten of its customers post negative comments about it and feel that is acceptable. But further analysis might reveal that only one in ten competitor’s customers post such comments. That’s a call to action.

Financial firms are also leveraging sentiment analysis to peer more deeply into customer trends. This helps spot opportunities that harmonize with those trends, such as self-service mortgages or more customer self service, for example.

Fraud and Crime Prevention. From sniffing out money-laundering schemes to preventing an all-out security attack to protecting customer credit cards, fraud detection and prevention is as vital to financial services firms as is their core lending and investment business. The reason is simple. In today’s world of full-disclosure of security breaches, nothing less than an institution’s reputation and brand are on the line.

Perhaps the most formidable weapon at a bank’s disposal is the ability to create complex models of ‘normal’ behavior across a variety of activities. Doing so requires an ultra-robust, powerful and highly available platform that can:

  • Create customer personas that are self-adjusting as underlying business rules change
  • Detect relationships across different entities that could signal money laundering efforts or credit card fraud
  • Aggregate data from sources as diverse as terrorist watch lists and the near century-old Interpol
  • Integrate and interoperate with other global financial firms to mine an even deeper pool of fraud prevention data

Hadoop enables a far more in-depth degree of modeling, the result of the sheer speed of processing in a Hadoop cluster and the ability to work with virtually any type of data. The result is more aggressive fraud prevention and far fewer false positives along the way. One bank deployed an Apache Hadoop distribution to identify phishing activities in real time, thus markedly minimizing the impact. Using Big Data analytics, the bank can run far more detailed forensics, and with ease of management and peerless performance that deliver maximum ROI.

Comprehensive 360-Degree Customer View. Perhaps the biggest change in customer service for financial services firms in the digital age is that they may well never see their customers, for any service requested. Thus a full view of individual customers – a requirement for service personalization – is derived only from data. This view must take into account analysis of as much or more data than the competition if the investment in a 360-degree view is to pay any dividends. This data can come from emails, social media profiles, complaints, forums, and clickstream data, etc. ad nauseum.

A platform for analytics to tackle this job must combine data exploration and governance as well as access, integration and storage scalability to effectively use all this data and actually drive incremental revenue. This is done with more intelligent and robust campaigns; highly accurate cross selling; and of course better customer retention. Today only Hadoop provides this platform capability.

Conclusions. Financial services continue as one of the most risk-laden and dynamic of all business segments globally. The sheer volume of data this sector generates from customers, transactions, global trading, and many other sources would easily overwhelm traditional processing platforms. It can be accurately said that Hadoop is purpose-built for just such a data-rich and data-dependent environment. With its limitless scale, ability to handle data regardless of format, and ever-widening list of analytics solutions, this open-source platform is the shoulders on which revenue growth rests.

This blog post was published November 21, 2016.

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