1. Financial services industry–Data processing. 2. Financial services industry–Information technology. 3. Financial institutions–Management. I. Title. II. Title: Financial institution advantage and the optimization of information processing.
HG173.K397 2015
332.10285–dc23
2014041589
Introduction
At its most basic level, a financial institution is composed of four things: a brand, a collection of personnel, some physical assets, and analytic (information) assets. The last category includes things like data, data processing capabilities, statistical models of various kinds, and other analytic and reporting capabilities. This categorical breakdown is simplistic, and not exactly clean. For example, there is an overlap between physical assets and data processing capabilities: Are the computers themselves physical assets or information assets? Overlap also exists between personnel and analytic methods: Does a buy or sell decision stem from an analytic method or from a person who makes buy and sell decisions? In spite of this lack of clarity, using this categorization – even in its most simplistic form – can help to frame the crucial underlying competitive issues facing financial institutions today. These issues can be summarized as follows:
1. If you have a strong brand, great, try to preserve it. If not, try to build one. But how?
2. If you have great personnel, great, try to retain them. If not, try to attract them. But how?
3. Physical assets are highly fungible, depreciate rapidly, and matter little, except insofar as they contribute to brand strength and the ability to attract and retain talent.
4. Information assets, actively and effectively managed, create competitive advantages and improved financial results. This helps to build brand strength and attract top talent.
Under this simple view, a financial institution that wants to be more competitive and more successful needs to focus assiduously on more effective management of information assets, including data acquisition and information processing. The goal of this book is not to describe the ideal state for any particular aspect of any business process within an actual financial institution. Rather, its goal is to suggest a prioritization of certain capabilities as critical strategic core competencies, provide some thoughts about better (if not best) practices, and to suggest a set of mechanisms for self-evaluation. In other words, how does an institution evaluate its information processing capability and take practical steps toward improving it?
Nearly every month the media report cases of major blunders by financial institutions in trading, reporting of financial information, and mishandling of customer information, along with censures from regulators caused by failures in data management or information processing. While these high-profile events may be signaling something about the capabilities of specific firms or about the average level of capability within the industry as a whole (raising concerns about the potential frequency of future costly gaffes), the underlying issue is not about the cost of isolated blunders. Instead, it is about the efficiency and effectiveness of the tens of thousands of tasks that financial institutions need to perform every day in order to earn their right to exist. The deeper question that ought to be asked by investors, managers, and other market participants is how well can these institutions develop, market, and manage financial products and services relative to their peers, given that these activities are critically dependent on information processing capabilities?
Importantly, financial institutions need not only be concerned about direct competition from more capable peers. They also need to be concerned about encroachment from more capable firms in tangential or even unrelated industries. One obvious threat is from firms whose core competency is squarely in Big Data management and information processing generally. These would include firms like Amazon, Yahoo, and Google, but even firms with other closely related strengths, such as logistics, can be threats to financial institutions that fall behind. For a powerful example, see “Wal-Mart Dives Deeper in Banking,” Wall Street Journal, April 18, 2014. To cite another example, Facebook now boasts more than 1.3 billion customers (it reported it had 20 million in 2007 and 200 million in 2009), and it is said that the company has more information about its customer base than any firm in history. How difficult would it be for Facebook, assuming it was committed to that strategy, to launch Facebank? And how might that development further change the competitive landscape for financial services? The answer to the former question may already be known. According to American Banker, an Accenture-conducted survey in March of 2014 of 3,846 bank customers in North America revealed that:
Almost half (46 percent) of consumers aged 18 to 34 said that if PayPal offered banking services, they would want to use them. About 40 percent said the same about Google and 37 percent favored Amazon… AlixPartners asked 1,249 smartphone and tablet-using customers at the end of 2013 which providers they would most want to use for a digital wallet (defined as a tool that stores payment card numbers and loyalty, gift card, reward, coupon, and discount information). Close to half (46 percent) would want one from PayPal, 19 percent from Google. Half (50 percent) said they would want their primary bank to provide the service.1
At the same time, online microcredit and peer-to-peer lending platforms, which are also capable of eating into bank market share, have been growing and multiplying at a rapid rate. There were reportedly 33 of such platforms that were active in 2010, up from one in 2005 (the first true peer-to-peer online lending platform was Zopa). Summarized by Bachman et al. (2011),
In this kind of lending model the mediation of financial institutions is not required…P2P lending is a way to receive a loan without a financial institution involved in the decision process and might also be a possibility to receive better conditions than in the traditional banking system.2
In 2012 the peer-to-peer online lending industry volume was over $50 million in new loans per month, and in mid-2012 total loan volume passed the $1 billion mark.3 At what level of volume and transaction size, or at what expansion of transactor scope, might these platforms be in a position to seriously encroach upon traditional financial institutions? And more to the point, what things are these traditional institutions not doing today that is helping to foster the growth of these financial services alternatives?
It is a somewhat puzzling irony that in the financial services sector, corporate leaders who are otherwise bold and self-confident, and whose success is founded on their ability to make daring, large, long-range decisions for the firm seem all too often unable or unwilling to make similarly bold and similarly important decisions about how their firm's information processing assets are designed, deployed, and utilized. Antiquated and patchwork data systems, along with obsolete and feature-starved process applications, can seriously undermine the competitive position of a financial institution. In many cases in which bold, long-range planning decisions are desperately needed, institutions fail to prioritize major improvements to their information processing capabilities, and this failure to prioritize can be the primary constraint on progress toward a more holistic and capable information processing infrastructure. Evidently, as we will argue, in this industry so dependent on superior information processing, institutions seem to be weak in assessing where the current investment trade-offs are, where they are headed, and how fast they are changing. One symptom of this state of affairs is the enormous difficulty and expense that firms have experienced in trying to meet post-crisis regulatory requirements, such as Comprehensive Capital Analysis and Review (CCAR). Replacing antiquated legacy capabilities and taking a more deliberate and holistic approach to information processing means not only restructuring or replacing physical data processing and analytic resources, it also means creating an organizational structure to match that modernized business model. This means that the overall strategic direction must be identified, that the underlying physical infrastructure must be aligned with that vision, and that a plan to match personnel with that model must be developed and communicated throughout the organization.
This book seeks to provide context, as well as analytic and anecdotal support,