F I N A N C E I N F OC U S Practical examples of how big data enables insight-driven decision making in the finance industry
Produced by Big Data Insight Group on behalf of EMC Greenplum and SAS
ABOUT EMC GREENPLUM:
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reenplum, a division of EMC, is driving the future of Big Data analytics with breakthrough products that harness the skills of data science teams to help global organizations realize the full promise of business agility and become data-driven, predictive enterprises. The division’s products include Greenplum Unified Analytics Platform, Greenplum Data Computing Appliance, Greenplum Analytics Lab, Greenplum Database, Greenplum HD and Greenplum Chorus. They embody the power of open systems, cloud computing, virtualization and social collaboration—enabling global organizations to gain greater insight and value from their data than ever before possible. Greenplum was acquired by EMC in July 2010, becoming the foundation of EMC’s Big Data Division. With technical and business leaders from large-scale computing companies like Amazon and Yahoo!, and database companies including Oracle, Informix, Teradata, Netezza, Microsoft and Vertica, Greenplum is tapping the best minds in the business of big data to deliver the next generation of data warehousing and analytics. www.emc.com
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ABOUT SAS:
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AS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 60,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®. SAS continues to solve some of the most complex domain and verticalspecific business issues in the world. Every level of SAS software – including SAS Business Solutions and SAS’ technology infrastructure (data management, analytics and business intelligence) – relies on highperformance analytics capabilities to turn mounds of big data into big opportunities for your business. For more information on SAS® Business Analytics software and services, visit: www.sas.com
F I N A N C E I N F OC U S I N T R O
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onditions for economic markets worldwide remain severely challenging. Now, the financial institutions that survived the recession of the late 2000s – the worst since the 1930s – can ill-afford to make the same mistakes again.
Therefore, the need for those in the finance sector to strategise accurately is greater than ever. They must find ways to analyse risk more efficiently, manage their portfolios more effectively and better engage with customers in order to stay ahead of the competition.
Meanwhile, cash-strapped governments need to focus on ways to increase their rightful tax revenues and to cut down on tax fraud, while also formulating effective fiscal stimulation plans to rejuvenate their ailing economies. Big data – and the associated tools, technologies and operational processes – offers viable solutions to all of these challenges.
C H A P T E R S
Introduction Greenplum and SAS Leading the field in big data NYSE Euronext Analysing risk in a constantly changing market in real-time IRS Saving money through fraud detection Zions Bancorporation Repairing relations with a fractious customer base Zions Bancorporation Setting economic policy based on science
Though applied broadly, big data refers to data which cannot be stored, managed or analysed using traditional database and IT tools. This is typically due to its volume (the size of the data set), its variety (including differing forms of structured, semi-structured and unstructured data) and its velocity (the speed and regularity at which the data is created, captured and analysed). The finance sector is already more advanced in its use of high performance analytics than many sectors. The firms that operate within it now have an opportunity to embrace big data as a means of creating sustainable practices within the harsh economic climate they face. Through practical, real-world examples featuring some of the world’s leading financial organisations, this report will outline some of the key areas in which big data can play a transformational role. These include:
• Real-time risk analysis in a constantly changing market • Saving money through fraud detection • Repairing relations with a fractious customer base • Setting economic policies based on science
The paper will also look at some of the challenges of successfully implementing a big data strategy, as well as establishing how the joint offerings of EMC Greenplum and SAS can help organisations overcome potential barriers and realise the full potential that big data has to offer. 3
EMC GREENPLUM AND SAS: LEADING THE FIELD IN BIG DATA
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n order to help organisations realise the huge potential of big data analytics, SAS and EMC Greenplum have forged a partnership. While each has its specialities and unique tools to offer, together the pair have created SAS High Performance Analytics for Greenplum, enabling SAS database users to bring big data into their analytical applications with highly accelerated and more accurate modelling, testing, and execution. With Greenplum’s massively parallel database solutions and SAS’ In-database Analysis, companies can create a platform to perform real-time analytics on huge, active, unstructured and disparate data sets. The result is in-depth insight delivered at an acceptable total cost of ownership, fast enough for businesses to make decisions within the constantly evolving market conditions they face.
with which we are working. Our work in the financial sector helps the banks know more about their customers and understand the world around them – these insights then go back to helping them to monetise their data and improve their services.” Barrie Neill, senior principal business solutions manager and banking industry specialist at SAS, adds: “A few years ago, SAS led innovation through information, centred around data warehousing, and pulling different sets of data together in order to perform predictive analysis. Now that those data sets have exploded in size and we can source data from so many places, in so many forms, the art of what’s possible has increased immeasurably.
Richard Reichgut, principal for the financial services industry at Greenplum, a division of EMC, says: “At Greenplum we work to foster an ecosystem that is not just about the data but also provides the applications to make sense and use of it.
“The In-database technology which SAS has now developed marries perfectly with Greenplum’s massively parallel compute platform, allowing companies to achieve these possibilities by analysing huge amounts of varied data within the critically short timeframes that business decisions demand. That’s paramount for the next wave of innovation.”
“SAS is doing a lot of work in the implementation of High Performance Analytics (HPA) and together we are seeing some fantastic results for the companies
So how are organisations leveraging Greenplum and SAS technology within the finance industry?
As this report will illustrate, this could be integral to the success of companies in the finance industry. Indeed, for many it could be pivotal to their very survival.
Greenplum Chorus
CSV, Excel
Greenplum DCA Greenplum Dia Module
Greenplum Database
Greenplum HD
Third-party Software (VMWare, Gemfire, SAS DI, Informatica, etc.) SAS HPA
Greenplum DC Modules Greenplum HD Module DCA Software
Isilon
External data sources
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Greenplum command center
Greenplum Third-party
Key products • SAS High Performance Analytics for Greenplum – Enabling big data within analytical applications, accelerating the pace of model development, testing, and execution. • SAS Scoring Accelerator for Greenplum – Highperformance model scoring and deployment with faster time to results • EMC Greenplum Unified Analytics Platform – driving the future of big data analytics with the industry’s first Unified Analytics Platform (UAP) that delivers: o Award winning massively parallel, shared nothing, high performance data load Greenplum Database for terabytes to Petabytes of structured data o Enterprise ready Apache-based Hadoop distribution, Greenplum HD, for the analysis and processing of unstructured data, combines the MPP database with the open source big data platform o Greenplum Chorus that acts as the collaboration and productivity layer for the data science team • EMC Greenplum Analytics Lab – Services, training, hardware and software for developing analytics insight and roadmaps • SAS Text Miner – Inmemory-boosted content analysis for large document collections and social media • SAS Fraud Frameworks for Government – Preventing fraud, waste, abuse and improper payments
ANALYSING RISK IN A CONSTANTLY CHANGING MARKET IN REAL-TIME
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ntegral to the success, or indeed survival, of any company operating in the financial services industry is the ability to understand, assess and act on risk analysis.
As Richard Reichgut of Greenplum states: “The question banks must ask is: How can they build an environment that will keep stressing their asset base, their investment decision, and their credit risk? Being able to gather information to see where they should be investing or divesting more effectively and efficiently is a very fundamental change; one that has been enabled by big data.”
NYSE Euronext – the world’s leading liquid exchange group, with ever-increasing data storage, compliance and analytical demands – offers a pertinent example. Operating the New York Stock Exchange, Euronext and NYSE Arca, the company’s near 4,000 listed companies represent over $20trn in total global market capitalisation. Daily data volumes for NYSE Euronext are increasing by 200 per cent each year, and the issuers, investors and financial institutions that rely on it expect transparent and low-latency data services. Therefore, the company chose Greenplum to provide a reliable and scalable analytics system that could manage up to two “Greenplum’s performance and scalability is altering us to evolve terabytes of new data per day. next-generation isolating applications that will hand over fresh
innovations to the US exchange marketplace. The ability to process The adoption of the massively parallel huge amounts of information on a near real-time basis has greatly database solutions not altered the value of data pulled off by NYSE Euronext in the US.” only allowed NYSE Euronext to manage Steve Hirsch, chief data officer for NYSE Euronext. and regain control over exploding data sets but, crucially, gain real-time insight from it. The speed of turning data to information is of the utmost importance and companies in the financial services industry must be able to react quickly to change and analyse how this impacts on risk levels. Continuous stress testing allows banks to predict these risks, taking into account current holdings, financing, and the macro economic factors impacting on the 5 investments they are looking to make. Moreover, risk does not just embody faltering companies or changes in the stock markets; it also includes changes to governance and compliance legislation. The need to remain compliant at all times and in all areas of the business is a huge pressure on the entire sector. Now more than ever financial institutions must learn how to use the breadth and depth of data available to satisfy more demanding regulators. As such, the ability to receive any legislative or compliance changes and implement amends accordingly, ensuring the company remains free from any legal repercussions, is an essential task. Big data technologies and processes can support that, through the analysis of massive, disparate and unstructured data sources in near real-time.
SAVING MONEY THROUGH FRAUD DETECTION
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ank card fraud costs the US card payments industry an estimated $8.6bn per year. Thus, many analytics-based systems for spotting it have been developed. At Visa, for instance, the credit card fraud analysis team have created interrelated rules for examining and flagging fraudulent behaviour as it occurs. This means they are able to warn other businesses in the suspected areas to take extra caution.
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Financial services companies’ reported inaccurate or irrelevant data Data & the CFO: a love/hate relationship A Dynamic Markets report, commissioned by SAS, July 2012
Banks and credit card companies aren’t the only institutions concerned with fraud though. The US tax collection agency, the Internal Revenue Service (IRS), uses SAS Analytics in order to spot fraudulent tax schemes, negligent returns and missed payments. The IRS suspects that it is paying over $5bn per year in fraudulent tax return claims. Typically, the claimant files earnings under schemes that qualify for lower rates of tax or for specially arranged rebates. They then file for a refund of tax they never paid. Often, fraudsters steal other’s identities, or use those of children or the dead.
Although one million fraudulent tax claims are identified and prevented each year, the IRS fears it misses up to one and a half million more. Thus, it has built a complex analytical system, based on SAS’ expertise, called the Return Review Program (RRP). This scores each tax return that it receives, taking into account a range of structured and unstructured data sources from multiple different platforms, including business rules, anomaly detection, predictive modelling, and text analysis from sources such as call centres. The scoring model identifies patterns of known fraud while social network analysis uncovers hidden relationships between colluding fraudsters, or uncovers identity theft. The SAS capabilities built into the IRS package include SAS Text Miner, Framework for Government, SAS Social Network Analysis, In-database analysis, and the SAS Scoring Accelerator for Greenplum. The RRP also helps to identify which payments are late or incomplete. As such, it will help to reduce the $345bn that the IRS is unlawfully owed. Barrie Neill of SAS says: “The IRS is a great example of how a hugely complex organisation with a constant workflow covering huge amounts of data can use a multifaceted big data approach in order to solve problems. Using just one form of analysis is not enough for this organisation, they need to be able to cross-analyse different sets of data, in different forms, from different sources, and they need to be able to do it with an acceptable speed of response. Big data enables that.”
REPAIRING RELATIONS WITH A FRACTIOUS CUSTOMER BASE
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he current worldwide recession has resulted in a customer trust deficit – a breakdown in the relations between the banks and their customers. Not all banks engaged in the risky lending practices and artificial market inflation tactics that ultimately turned boom into bust, but the loss of reputation was industry-wide; it resulted in a need to create stronger, more personalised relationships with customers for all high street banks.
The utilisation of big data helps in this pursuit by providing insight into customer behaviour and assessing how well the company is interacting with said customers on an individual basis. This ensures: bespoke offers are created; a ‘right time’ focus on services is adopted; and all-round customer satisfaction is improved. US banking group Zions Bancorporation has adopted this strategy to good effect. Zions, which owns a range of high street commercial and corporate banking brands, has created a product recommendation engine called The Next Best Offer. This system uses Greenplum analytics tools to build an understanding of each customer and predict which products and services they are most likely to need or desire, either now or in the future. By analysing each customer’s individual current product portfolio and the related financial activities they undertake, bespoke marketing messages can be built and presented to the bank tellers in branch, at their point of contact with the customer. “If the recommendation engine helps us increase purchases-per-customer by only $10 a month, that’s a significant amount of new revenue,” says Clint Johnson, Zions’ director of data warehousing and analytics. The Next Best Offer is built upon a data-mining ‘modelling-to-scoring’ application which utilises in-
database analytics. This means the data does not have to be moved to a separate disk in order to be analysed, speeding up analysis time by huge proportions and allowing correct and up-to-date information to be served up live while a customer is in branch. Meanwhile, another leading bank in the US, which is a customer of SAS, has sought to increase its standing with customers by helping them to get better value from their every-day shopping experiences. Banks, of course, have a unique vision of their customers as they can see where all transactions on credit and debit cards are being made. With its customers’ permissions, the bank has begun to use this transaction data to secure discounts for its customers based on their regular spending habits with certain retailers.
“If the recommendation engine helps us increase purchases-per-customer by only $10 a month, that’s a significant amount of new revenue.” Clint Johnson, director of data warehousing and analytics for Zions Bancorporation
The bank’s reward scheme, launched early 2012, enables users to have the deals applied automatically when they use their debit or credit cards at the partaking retailers. The bank sees the scheme as beneficial to all parties: the customer gets a discount on a product they are interested in; retailers can attract more customers by effectively tailoring deals; and, for the bank, the scheme should promote heightened use of its cards. In this way, the company gains a distinct advantage over its competitors. Any financial benefit to the bank from the deals themselves is a bonus – what the bank really achieves is its card becoming the first choice among the consumer.
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SETTING ECONOMIC POLICY BASED ON SCIENCE
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here are many factors that influence the way banks, and indeed state bodies, set their interest rates. Initially, for their headline figures, they must analyse their market focus against the current economic climate in order to prioritise products or customer demographics. They can then set their rates accordingly.
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Reasons why financial services companies see corporate data as more important now than it was five years ago:
93% 71%
64% 57% 43%
Its role in compliance initiatives
The increased regulations relating to financial reporting
Its value to devising business strategy
Its value to helping retain customers
Its role in cost cutting during the recession
Data & the CFO: a love/hate relationship A Dynamic Markets report, commissioned by SAS, July 2012
When a bank decides whether or not to approve a customer with a certain financial product, and then sets the actual rate that they will offer on a case-by-case basis, the profit that the transaction is likely to generate over its whole term is a big part of the process, as of course is the customer’s financial history, as per their credit rating. Together, this enables the bank to put the risk associated with the product into context. However, the product in question is not the only consideration. The bank must also analyse the value that customer brings across their entire portfolio in order to reward high levels of customer service or to maximise up-sales potential in the longer term. Greenplum has also helped Zions Bancorporation in this department too. It has allowed Zions to create a profitability reporting system which analyses historical data in order to establish the value of each loan, investment or deposit account held by the customer to the bank. With the big data elements, including in-database functionality for rapid analytics, the bank is able to perform this investigation within an appropriate time for the result to be used within an application for a loan, on a case-by-case basis. Previously, using standard database tools, this would not be possible.
Clint Johnson of Zions says: “It used to take seven hours to reprocess a month’s worth of data. With Greenplum we can crunch through two years of history in seven and a half hours, so we can be more agile and responsive to changing economic conditions. We’ve also produced some stunning results in the net profitability of each customer account.” When unstructured data analysis tools are brought in, think tanks and analyst companies working on behalf of governments or the banking industry are able to analyse large swathes of the knowledge sphere in order to accurately assess current economic conditions. They can also predict what the effects of certain fiscal stimulation initiatives, such as lower interest rates or government guarantees for loans, will be. This allows them to set their policies accordingly.
CHALLENGES TO OPERATIONALISING INSIGHT There are various challenges that must be overcome in order for organisations to realise the full potential of big data.
Presenting information
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ffective presentation of insight from analytics to relevant areas of an organisation is critical to the success of a big data project. Therefore, mastering the art of visualisation should be a key concern. Strategic, business-focussed employees require insight to be delivered in a dynamic, interactive and engaging format which allows them to make their own journey of discovery through the information. The form your visualisations take and the level of depth must be tailored to the personnel you are serving with information and to the business question in hand. This is key for achieving buy-in.
Speed and breadth
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n order for analytics to take a key role within business operations, the speed at which insight can be generated and the breadth of discovery is pivotal. High-value modellers and business analysts need months with their incumbent technology to create a single validated, productionready predictive model, including training, validating and operationalising the model from concept to realisation. If insight is to drive every element of decision making, this is not efficient enough. However, by adding highperformance analytics to the model development process, productivity increases ten-fold, allowing insight to be created in a timely enough manner to warrant asking questions. Furthermore, it allows hundreds of iterations to be performed simultaneously which SAS states will yield models that are up to 70 times more accurate.
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reenplum and SAS solutions enable valid insight-based decision making. But to execute it into strategy, you need suitable skills within your organisation. While new technologies increase the speeds at which you can work to applicable levels, it is people that usually cause the bottlenecks. Furthermore, data does not form information autonomously. The interpretation of people is still required to extract the insight, in-line with the business strategy. A collaborative approach with a cross functional team is key. Many organisations choose to manage this by creating an ‘insight centre of excellence’ which brings together different departments and controls the information flow. This centre can engage with the data science community to benefit from shared peer-based learning and best practice tips. The centralised insight activity also helps to ensure projects are scalable and repeatable.
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BIG DATA – ESSENTIAL NOT JUST TO THRIVE, BUT TO SURVIVE
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ig data has arrived and, although still new to many companies, there is little time to allow doubt or a lack of knowledge to prohibit the adoption of what will become not only a means of thriving in the financial services sector, but what could be integral to the very survival of some organisations. The application of the holistic and far-reaching insights that big data analytics can offer in real-time, can fundamentally change the way organisations operate. Whether it is reacting to changes in the market, detecting fraud, predicting risk, improving customer satisfaction, or going above and beyond competitors, big data can add value to almost any area of a financial services company. Some of the examples and case studies explored in this report show that big data is far more than market hype; it is something that is already having a massive impact on the banking industry and its leading players. Those who fail to take advantage of the technologies and services that are now available to them will be at the mercy of their competitors that ride the wave of big data to become more precise, informed and rapidly adaptable companies.
As Greenplum’s Richard Reichgut asserts: “Big data is essential for the very survival of companies in the financial services industry. I think the firms that really embrace it, particularly in these unstable and unpredictable economic times, are the ones who are going to be able to evaluate and assess the evolving landscape, quickly adapt, and change their strategy accordingly and that’s incredibly important for their competitive advantage.” Barrie Neill of SAS adds: “With high performance analytics, queries that used to take weeks can now be answered in minutes. That’s why it’s such a huge game changer for financial services companies. What used to be left to gut feeling can now be determined by science. The intelligence we can now have at our fingertips allows us to react faster, with more precision, than ever before. Big data is the best tool at our disposal for bringing the finance industry and the world’s fallen economies back to health, stronger than they ever were before the global downturn.”
Big Data Insight Group is the UK’s first independent business and IT focussed big data and advanced analytics community.
Our members are typically senior stakeholders and key influencers from SME to blue chip enterprises, incorporating both the public and private sectors. Our remit is to assist them in overcoming the challenges and realising the true potential of big data. We do this through a number of channels, including forums, executive dinners, masterclasses, independent research reports and online editorial. Our activities feature a mixture of peer-based learning, insight from best practice leaders, networking and sourcing solutions. Big Data Insight Group is a sister community of The Cloud Circle (cloud computing) and Obis Omni (business intelligence and performance management). To become a member of Big Data Insight Group and receive the latest research plus invitations to events, please contact: info@bigdatainsightgroup.com
Report editors: Mark Young mark.young@bigdatainsightgroup.com Dominic Pollard dom.pollard@bigdatainsightgroup.com
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Produced by Big Data Insight Group on behalf of EMC Greenplum and SAS