Getting the most out of big data

IBM Software White Paper Financial Services Getting the most out of big data How banks can gain fresh customer insight with new big data capabilities

2 Getting the most out of big data Banks thrive on data: transactions, customer interactions, mortgage rate changes, risk assessments and investment portfolios. They analyze the data for trends, patterns and insights to help boost efficiency, ensure compliance or increase revenue. But not all of this data lives conveniently in a structured database or data warehouse. Sometimes it comes from real-time data feeds and social media. Or it may come from customer correspondence, written reports and spreadsheets. The challenge for the banking industry today is to combine and analyze these growing sources of structured and unstructured data, known as big data. Big data is often characterized as having three dimensions: volume, velocity and variety. A fourth V, veracity that is, the credibility of the data applies to the banking industry in particular. When properly analyzed, big data offers many potential benefits for insight in the banking industry. For example, big data helps banks assess customer interactions and experiences. By analyzing each potential source of data, from channel interactions to social media, banks can come to know and understand their customers both individually and demographically, enabling them to better serve their clients. In addition, banks can develop high-quality risk and fraud models by analyzing as much available data as possible to get deeper insight from transactions, events and correspondence. Risk management hinges not only on financial calculations, but also on written analysis of activities not generally reflected in transactional information. Fraud-detection efforts benefit from the ability to analyze streaming transactions in real time and quickly identify potentially illegal credit-card or banking activity. Banks can also optimize processes by analyzing available operational data in real time. The return on an investment in big data, then, becomes apparent in several ways. By applying real-time analytics capabilities to big data, banks can gain insight quickly. These insights help banks improve customer service and response and create targeted offers that can lead to higher acceptance rates helping to increase customer retention. Big data also helps banks comply with industry and government regulations by providing insight into transactions, events and correspondence and identifying where processes need improvement or where compliance might be lacking. Moreover, big data may enable financial institutions to anticipate and prevent fraud by incorporating a higher number of variables into the analysis. Big data in banking: Real-world scenarios In an era of intense competition and market volatility, banks need strategies for getting the most out of data for two key activities customer interaction and liquidity risk management. The following three scenarios show how big data impacts these important aspects of the financial services industry. Next best action To better serve individual customers, banks strive to understand the best way to anticipate their needs. That is, based on one action or event, what is the next best action to offer better service, and cross-sell or up-sell the customer? It could be as simple as suggesting a new car loan several years after the end of a previous loan or a home-equity line after the purchase of a house. It also could be as important as following up in person after receiving a complaint letter or seeing a social media post about a poor banking experience. A quick response can add short-term value to a new customer relationship or enhance a long-term one.

Financial Services 3 We were able to dramatically reduce our system support and have achieved over 200 percent efficiency.we have achieved risk-based savings by decreasing our exposure and have found correlations that we couldn t have before. We can now look further and deeper into our data. Emile Werr Vice President Global Data Services NYSE Euronext

4 Getting the most out of big data Banks encounter several challenges while trying to build relationships and improve customer service. Frequently, they cannot cost-effectively capture, analyze and store the large amounts of incoming data, and customer-facing employees do not have access to the right data at the right time. However, to anticipate and execute timely, personalized interactions, banks need deep insight into the customer experience, both on an individual basis and in an aggregated format to accommodate demographic behavior. For instance, by analyzing the behavior of the top 10 percent of account holders by amount, banks can determine other services that group might want or specific services they can use in an up-sell offer for the next percentile of account holders. Banks also need to aggregate unstructured data, such as social media, email and customer service notes, to quickly respond to issues before they negatively impact customer relationships. With this kind of insight, banks can optimize operational models that predict what the next best action should be. Big data offers inherent payoffs for next best action scenarios. A big data infrastructure that enables near-realtime, customized responses helps banks increase customer satisfaction with each interaction which over time helps improve up-sell, cross-sell and retention efforts and incrementally build a customer s strategic lifetime value, profitability and loyalty. Using these business results, banks can deliver next best action recommendations across their lines of business and into their channels and touch points in real time. Beyond the customer: Other applications of big data in the financial services industry Big data offers insight into more than just customer behavior. Banks have deployed IBM solutions for big data to analyze and model data streams for exceptional real-time decision support. Analytics on data in motion help banks track financial resources and predict, detect and respond to fraud in real time. Organizations can also use these insights to optimize operational processes, which helps increase efficiency, lower costs and boost responsiveness. Not only does big data help banks analyze what has already happened, it can help them discern what is likely to happen through predictive analytics. By incorporating sources of data that may not have been previously leveraged, such as customer behavior and contact center free-form notes, banks can help improve the accuracy of predictions leading to clearer insight and more confident decision making. Customer insight and retention The ability to save a customer relationship is just as important as the ability to up-sell or cross-sell. It is generally less expensive to service a current customer than to acquire a new one, so banks must identify which customers are most at risk of terminating their relationships. Deep customer insight helps reveal what customer service initiatives or proactive offers work best to avoid churn.

Financial Services 5 To achieve this, banks have to identify attitudes or clues that lead to customer attrition. It is crucial to identify high-value customers and understand their needs, because they are frequently the most profitable clients and are the ones most likely to maintain a long-term relationship. The right big data solution helps pinpoint these customers preferences and measure customer profitability accurately. In fact, by analyzing more data from multiple sources, big data can help uncover new trends and additional customer insights in other critical areas. For instance, it can help identify trends and patterns to create the foundation for a customer retention campaign. And by enriching predictive models with more granular data, banks can make targeted offers that customers would be more likely to accept. With high-quality data and detailed analysis, banks can create highly targeted micro-segmentation that facilitates personalized offers and drives high customer satisfaction. IBM big data platform in action: Global stock exchange Even as stock transaction volume increases, stock exchanges are still responsible for identifying patterns of illegal or potentially fraudulent trading. To detect patterns that could indicate questionable trading activity amid a soaring number of transactions, securities markets not only need processing power, but also the flexibility to incorporate new algorithms that can spot new risks emerging before they can do damage. One prominent global stock exchange found it increasingly difficult to meet its mandate in a timely manner. Its surveillance experts were constrained in their ability to run detection algorithms, often having to spend time breaking up and fine-tuning their queries instead of developing and testing ever-more powerful and insight-producing algorithms to better detect questionable trading activity. To help expedite its investigations, the exchange used the IBM platform for big data to develop powerful market surveillance tools to detect patterns within a number of daily trades that can exceed 15 billion. The exchange s new market surveillance platform both sped up and simplified the processes its experts used to analyze patterns within these trades. With less-complex processes and more powerful analytics engines, the exchange gained the flexibility to design and test increasingly insightful algorithms that can spot, for example, new methods of insider trading or the telltale signs of market manipulation. Overall, the platform produced a variety of benefits: Reduced the time required to run market surveillance algorithms by more than 99 percent Decreased the number of IT resources required to support the solution by more than 35 percent Improved the ability of compliance personnel to detect suspicious patterns of trading activity and to take investigative action early The result: as the exchange s trading volume grows and becomes more globalized, its analytics capabilities help ensure a level playing field for all investors.

6 Getting the most out of big data Risk management One of the key metrics banks monitor on a daily basis is their intraday liquidity; it strongly correlates to their economic stability and growth potential. To track this metric as quickly and efficiently as possible that is, with minimal human intervention banks are increasingly taking advantage of technical approaches that incorporate a breadth of data and automated collection techniques. By capturing, storing and analyzing the massive amounts of data necessary to feed regulatory reports, risk dashboards and early-warning systems, as well as to generate the analytics required to quantify and predict liquidity risk, banks can clearly gauge their ongoing stability. Bringing together all of the appropriate information to create highly optimized risk models is a challenge that involves identifying and integrating large amounts of unstructured risk data in a variety of forms and data sources, and then making it available to complex, high-volume computational processes. The potential payoff: fast and secure risk analytics that enable efficient management of cash and collateral used to support intraday payment and settlement obligations. Equally important is the ability to use these same precepts to identify other types of risk, including credit-card fraud and loan exposure. IBM solutions for big data IBM has developed an extensive collection of tools to help banks cut big data challenges down to size: IBM InfoSphere Data Explorer: Discovery and navigation software (previously known as the Vivisimo Velocity Platform) that provides real-time access and fusion of big data with rich and varied data from enterprise applications for greater insight and ROI. IBM InfoSphere BigInsights : An enterprise-ready Apache Hadoop based system with sophisticated text analytics, visualization, performance, security and administrative features for managing and analyzing massive volumes of structured and unstructured data. IBM InfoSphere Streams: In-motion streaming analytics software that enables continuous analysis of massive volumes of streaming data with sub-millisecond response times, helping to improve your organization s level of insight and decision making, as well as promoting real-time response to events as they happen. IBM Netezza : High-performance data warehouse appliances that are purpose-built to make advanced analytics on exploding data volumes simple, fast and accessible; uses advanced analytics to deliver deep insights in minutes on petabyte-scale volumes of relational data. IBM InfoSphere Warehouse: Comprehensive data warehouse software platform that delivers access to structured and unstructured information in real time; supports operational analytics and applications with up-to-theminute insights. IBM InfoSphere Information Server: A complete collection of data integration and data quality capabilities that help ensure delivery of trusted information; enables organizations to understand, cleanse, transform and deliver trusted information to critical business initiatives by integrating big data across enterprise IT systems. IBM InfoSphere Master Data Management: Creates trusted views of master data about customers, products and more, and provides a centralized data source that promotes accuracy and data quality to help improve your applications and business processes. Not all of these tools are required for every big data installation, but they cover the spectrum of potential needs for most banks.

Financial Services 7 Harness big data for exceptional insight With the rapidly increasing importance and volume of structured and unstructured data, the ability to store, analyze, search and understand this resource becomes even more integral to a bank s day-to-day operations. By deploying IBM solutions for big data, banks can capture large volumes of data from a variety of data sources and types relating to their customers, including transactions, correspondence, social media, voice recordings, web clicks and transactional information across multiple channels. As they strive to increase customer intimacy through mobile solutions, banks will receive more data relating to geo-location and presence, which may uncover even greater customer insight. In essence, big data helps banks combine qualitative and quantitative information beyond transactions to give them a clear, insightful view of their customers at the individual and group levels. Such insight is essential for improving customer service and customer retention, and supporting a range of ongoing strategic initiatives such as fraud prevention, as well as proactive campaign and resource planning. The IBM platform for big data enables banks to leverage new and optimized technology within a larger ecosystem, combining an organization s existing assets that deliver value and differentiated services with new assets that deliver stronger or new capabilities. It also helps simplify and accelerate the delivery of analytics, allowing IT departments to move workloads to the most appropriate location and to further optimize the analytical architecture. The right data and data analysis tools can create a strong competitive advantage in a highly competitive industry. Using these tools, banks can analyze data in real time to gain crucial insight without storing excessive data in databases or data warehouses. IBM big data solutions help banks understand which data is most important and which data contributes to the precise insight necessary to attract and retain customers, improve their financial standing and heighten their ability to respond to industry changes and fast-moving competitors.

For more information To learn more about IBM solutions for big data and the financial industry, please contact your IBM representative or IBM Business Partner, or visit: ibm.com/bigdata ibm.com/software/data/infosphere/bigdata-analytics.html Copyright IBM Corporation 2012 IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America October 2012 IBM, the IBM logo, ibm.com, BigInsights and InfoSphere are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at Copyright and trademark information at ibm.com/legal/ copytrade.shtml Netezza is a trademark or registered trademark of IBM International Group B.V., an IBM Company. VELOCITY and VIVISIMO are trademarks or registered trademarks of Vivisimo, an IBM Company. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. THE INFORMATION IN THIS DOCUMENT IS PROVIDED AS IS WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON- INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the client is in compliance with any law or regulation. Statements regarding IBM s future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only. Please Recycle IMW14653-USEN-00