IBM Grid Offering for Analytics Acceleration: Customer Insight in Banking
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Grid Computing IBM Grid Offering for Analytics Acceleration: Customer Insight in Banking customers. Often, banks may pur- chase lists and acquire external data to improve their models. This data, although useful, can be expensive to acquire and will require additional resources for cleansing, integration and analysis. Because acquiring new customers is more expensive than keeping existing ones, and because customer bases are under constant attack from com- petitors, customer retention is a key issue. Banks must be able to predict The challenges facing customer which customers are likely to leave, Highlights insight in banking what incentives would urge them to stay and when to stop investing in an In an environment of intense global ■ Helps improve the use of compute unprofitable customer. As well, banks competition, banks today are faced and information resources across are trying to expand existing cus- with higher customer attrition caused an organization tomer relationships and create cross- by increased competition. They are sell or upsell opportunities without ■ Accelerates time to results and under pressure to acquire new cus- further driving attrition. increases the number of modeling tomers and retain existing ones in the iterations achievable within a most cost-effective manner possible. To be successful, then, banks must given timeframe understand and predict customer However, new customer acquisition is needs by combining products and ■ Offers an affordable, flexible and difficult, with extremely low response services and applying them consis- resilient IT environment for fast, rates. To reduce customer acquisition tently through appropriate distribution large-scale analysis costs, banks must create efficient models to determine which customers ■ Enables secured and immediate are high value and what marketing access to derivative information strategies will best draw those desired Grid Computing channels and networks. In addition, between winning or losing a cus- IBM has created an integrated grid- banks must attract customers that tomer. To overcome these barriers, based technology and service will maintain their profitability while banks require a technology infra- offering that can enhance a bank’s reducing their risk. However, to apply structure that can accelerate cus- competitiveness by accelerating these strategies, they must sift tomer insight applications in a customer insight applications. A grid- through tremendous volumes of resilient, scalable and cost-effective enabled infrastructure reduces the customer data that may be stored in manner. cycle time for executing statistical disparate data formats and located models, leading to more sophisti- in repositories scattered across the IBM Grid Offering for Analytics cated analyses and increased enterprise. Acceleration: Customer Insight in accuracy of customer acquisition, Banking retention, cross-sell and upsell by Coupled with the difficulty in handling The IBM Grid Offering for Analytics enabling a larger number of modeling customer data, banks may discover Acceleration provides an efficient, iterations in a shorter timeframe. that their IT infrastructures for cus- scalable and standards-based solu- tomer insight are siloed and ineffi- tion to the most pressing issues fac- The IBM offering provides a cost- cient. Because of resource limitations ing banks today. IBM, working with effective model for acquiring, de- and inefficient use of available com- key partner SAS, the first enterprise ploying and managing resources. pute power, these infrastructures business intelligence vendor to join Because the existing infrastructure have a limited ability to run multiple the Global Grid Forum, built a new that supports customer insight does affinity analyses within a needed grid offering for customer insight in not need to be replaced, firms can timeframe—which can be real time. the banking industry. The SAS® leverage their existing capital invest- Their limitations prevent banks from Credit Scoring application, part of ments, optimizing the efficiency of obtaining timely, accurate models SAS Banking Intelligence Solutions, their customer insight business and that can make the difference showed that complex credit scoring applications while migrating to a applications can be grid-enabled. higher performance, lower cost, stan- such as compute resources, data Use case: gaining a competitive dards-based infrastructure. In addi- storage and filing systems to create a edge through target modeling tion, this grid infrastructure can be single, unified system and to address IBM and SAS developed a means to used for compliance, fraud detection fluctuating workload requirements. extend the value of the SAS Credit and other risk management functions. Scoring application to the grid envi- As required by an opportunity man- ronment, as demonstrated in a real- The IBM Grid Offering for Analytics ager’s analyses, the grid makes addi- world scenario involving a financial Acceleration allows businesses to run tional compute capacity available on services institution that offers a full their existing analytics software, a full-time or part-time basis. It helps range of banking products and serv- whether custom-built management banks leverage available, under- ices to its member customers, includ- systems, best-of-breed commercial utilized compute capacity within their ing wireless and Internet banking, applications or a combination thereof. existing IT infrastructures, thus help- loans, credit card services and sav- The offering’s open, flexible infra- ing them to reach end results far ings and investment services. Having structure supports a wide range of more rapidly than within conventional already acquired more than 70 per- packaged and custom analytical computer environments. Compared to cent of its established market in its applications, including SAS applica- a non-grid solution, the required com- geographic area, the institution tion software. pute resources are fewer and easier wanted to expand its membership to manage—contributing to reduced charter to the larger community. The IBM offering centers around grid total cost of ownership (TCO). The However, to compete effectively in computing, an architectural approach IBM offering also provides a level of this new market space, the financial that enables distributed computing resilience to help guarantee workload services institution needed to under- over the Internet, an intranet, a virtual execution. stand how to attract new prospective private network or some combination members and tailor profitable prod- thereof. This approach can help ucts to meet their needs. aggregate disparate IT elements Grid Computing IBM first assessed the business The institution’s customer insight IBM ^ BladeCenter was inte- processes, available customer infor- application was enabled to leverage grated with the grid by GridServer mation and IT infrastructure of the computational services by using to create an on demand, dynamic financial services institution. The the DataSynapse GridServer™ ap- provisioning capability that could results of the assessment determined plication programming interface assure the right resources were avail- that the financial services institution (API) to connect and parallelize ap- able at the right time to meet the run- had a wealth of information about its plication service requests to scale time servicing requirements of the current customer base, but needed to effectively when distributed across an risk applications. In addition, the acquire additional customer informa- available infrastructure. The solution IBM WebSphere® Application Server tion from a third party. The institution implemented the policy-driven man- was integrated with GridServer and needed to organize this data so that it agement functions of GridServer to Avaki Data Grid to provide portals for could be accessed using business handle application workload servic- grid users. These portals were devel- intelligence tools that generate target ing, resiliency and service provision- oped using the WebSphere Studio modeling applications. IBM further ing. The grid infrastructure used Application Developer tools. determined that running the target UNIX® systems—Sun Solaris and modeling applications in a grid envi- IBM AIX®-based servers—along Improved productivity and efficiency ronment would give the financial insti- with the Microsoft® Windows® through a multistage implementation tution a greater degree of flexibility for operating system, Linux-based Banks can take their existing virtual fine-tuning the models, because it IBM ^™ BladeCenter™ pool of compute resources for cus- would be able to run these models on servers and Linux-based workstations tomer insight and gradually grow it demand. to satisfy the performance, scalability into an integrated, highly efficient grid and systems management integration system. As shown in Figure 1, an requirements. existing customer insight infrastruc- ture may comprise disparate islands Figure 1. Customer insight infrastructure before applying the grid Grid Computing of statisticians, administrators and Statisticians can access these all applications can become com- heterogeneous compute and storage resources through a single, common pletely grid-enabled; all compute and resources, along with various ver- interface. In addition, a single admin- storage resources across the enter- sions of a customer insight applica- istrator can manage the grid through prise become one large resource tion. Additionally,