Hadoop: the game-changer in banking

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185263688At most banks, data is stored in separate databases and data warehouses. Customer data is stored in marketing databases, fraud analyses are done on transactional data, and risk data is stored in risk data warehouses. Oftentimes even liquidity, credit, market, and operational risk data is stored separately as well.

Bringing together regular, structured data from multiple data sources is the biggest challenge for companies in any industry. But leaving valuable data outside of the decision making cycle due to the limitations of existing data warehouses is not an option going forward.

What if…

Imagine what banking life would be like if you could access all the banks’ data and ask any question you could think of – and get the answers, fast. Wouldn’t that make risk reporting, fighting fraud, and taking the best action for your customers so much easier? Not to mention management decisions: which new products are we going to launch, what does our business need, which way should we go? It’s actually not that far away.

Disruptive technology

Most banks are constantly leaving potentially valuable data on the table and that’s often because of the high costs associated with staging it in enterprise data warehouses, according to Mike Gualtieri of Forrester Research. However, there is a disruptive trend occurring: data is growing very fast, and at the same time, technology is moving forward quickly as well. The disruption lies in the fact that as technology is moving forward, the costs are getting lower. This means we can – and should – disconnect the growth from the costs, which gives banks the opportunity to easily and cost effectively store all their historic data – and create value from it.

Changing the game in banking

Hadoop is one well-known example of this disruptive trend. What does Hadoop bring to the table where all that valuable data is still waiting to be analyzed?

There are many ways that banks can use Hadoop. I’ll name a few that I think are most relevant:

  • Fraud and financial crimes, such as money laundering, are two of the most costly challenges in the industry. With the added storage and analytics available through Hadoop, banks would be capable of analyzing at point of sale, authorization and transaction data for picking up unusual behavior patterns. This way, banks can gain significant value by using Hadoop.
  • Regulators are expecting banks to produce aggregated risk data that is complete by any kind of required grouping, and financial institutions need to improve their risk data aggregation capabilities to meet the regulatory demands. With Hadoop, banks are able to analyze all their data in one place. This enables a risk data architecture approach that can help banks overcome their existing dilemma of consolidating and aggregating risk data for risk reporting purposes.
  • Many financial institutions are struggling with their customer data. They want to be able to know their customers, improve their customer segmentation and analyze their customer churn, sentiment and customer experience – and more. Hadoop presents an incredible opportunity for banks to on-board all of their data and get the best insights by making more of the unstructured data they already have, like complaint letters, credit applications and other sources of customer feedback and interaction.

And these are just a few examples. Want to tackle BCBS239 challenges, create intra-day reports, or combine risk, fraud, and customer data to explore new trends? With Hadoop, you can.

Empower your data-driven bank

With SAS, you can take full advantage of the distributed capabilities that Hadoop gives you, in one single environment. A combined SAS and Hadoop solution enables you to deliver all aspects of the analytical lifecycle: from data management, through exploration, visualization, and model development, to deployment and execution. Empower your data-driven banking organization with Hadoop and take full advantage of the distributed storage and processing. Make decisions based on all of your data. The question is not “What if we could?” but “When are you going to” take all the valuable data off that table and use it?

Want to learn more? Register for a free Hadoop Webinar.

JohnF

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About Author

Anne Belder

Digital Marketing Specialist

Anne Belder has worked at SAS since February 2013, where she specializes in Digital Marketing in the Middle East, Africa, and Turkey.

1 Comment

  1. Alejandro Godinez on

    Let's not forget the sistemátic risk, this is the most catastrophic risk. (When it comes Banks)
    Regarding data, we must say that nowadays unstructured data is almost 80% of total data and nobody (for sure) can tell us if unstructured data can become in structured data (as a dependent and independent variables) in order to carry out Predictive Analytics.

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