The pursuit of predictability

The pursuit of predictabilityInsight into financial institutionrisk through bits and bytes

AbstractAbstractThe global financial crisis has wreaked havoc on the world economy in general and the financialservices sector in particular. The number of failed U.S. financial institutions since 2007 hasexceeded 300, with many more still expected. The aftermath of the crisis has prompted increasedscrutiny of financial regulations, regulators, and the industry’s risk management in general.Extensive regulatory reform has been enacted with a goal to prevent a failure of this magnitude tooccur again. One of the dominant themes of the reform is greater use of information to helpregulators keep up with the complexities of the financial institutions and the dramatically increasingvolume and the velocity of transactions through the financial system.The key theme of regulatory reform relies on regulators being able to collect, aggregate, analyze,and share information. Accurate and timely information analysis will provide regulators with insightsinto the health of the financial system and its participants. The institutional knowledge and regulatoryexpertise that risk management professionals and regulators possess is critical to the assessment,and information analytics is a valuable means to augment traditional risk assessment methods toprovide quantifiable and insightful metrics.There are several different analytical models and techniques that can enable regulators to monitorfinancial institution health, assess systemic risk, detect fraud, and correlate a rich set of electronicinformation. These models have the potential to provide an early warning for emerging challengesthat could pose a threat to the stability of the global financial markets. The challenge for regulators isto build an enterprise class solution, which is scalable, flexible, and adaptable to the ever-changingfinancial services landscape. By laying out the steps and disciplines necessary to develop ananalytics program that is able to monitor systemwide risk, this paper provides practical insight intokey considerations that should precede any investment into information analytics.1.0 The case for analyticsThe global financial crisis that led to the great recession was triggered by the bursting of the U. S.housing bubble. The resulting losses are estimated to be in the trillions of U.S. dollars globally. Thecrisis exposed the complexity and interconnectedness of financial institutions and brought to theforefront the vulnerabilities of the financial markets as a result of a failure in a systemically importantfinancial institution. In hindsight, early warning signs of distress in the financial markets were evidentyears before the meltdown. Looking back, what seemed like disconnected events can now berecognized and connected.For Wall Street, the crisis has led to sweeping changes in terms of financial regulatory reform.Structural changes have been mandated to the way that financial markets operate. Theaccompanying regulatory structure has been revised to help monitor risk within financial institutionsand provide visibility into their interconnectedness with each other. A key enabler for the changes inthe regulatory landscape to be successful is a shift in how qualitative and quantitative information isviewed — a shift from hindsight to insight to foresight, a shift from understanding what happened towhat may happen, and a shift from slicing and dicing information to sensing and predictingoutcomes. In short,, a heavy reliance on collecting, aggregating, analyzing, and disseminatinginformation is critical to regulatory effectiveness. The pursuit of predictability Insight into financial institution risk through bits and bytes 1

AbstractRegulators of depository institutions have long looked at micro-prudential measures such as Tier 1and Tier 2 equity ratios, asset quality, liquidity, and management effectiveness to measure thehealth of an institution. The recent financial crisis has yet again exposed the fact that the collectivebehavior and health of financial institutions is what leads to a crisis. Interconnectedness does notrefer just to institutions that transact heavily with one another, but also to institutions whose activitiesand exposures are heavily correlated. As the role of regulators continues to evolve and expand frominstitutional supervision to systemic supervision, the importance of factoring in macro-prudentialinformation into the supervisory and risk assessment process is paramount.Supervision of the financial system must also closely consider the broader macro-economicpicture. Indicators that provide visibility into the state of the economy, market imbalances, and thecomposition of capital flows are important factors that may foretell the health of the overall financialsystem.Over the past decade, predictive modeling and analytical techniques have improved significantly.Advances in data aggregation, computing power, and electronic storage have prompted financialinstitutions to employ advanced analytics to glean insights from customer data. Advances in text-mining open the door for unstructured data to be added to the sample population. Combined, thesetechnological advancements can provide regulators with powerful tools for effective supervision. Adynamic and flexible analytical solution for institutional risk-monitoring can help identify risk early onand allow for supervisory action to be taken before a situation deteriorates further and when suchaction may be less costly.However, to monitor risk in an environment of the size, velocity, and complexity as the globalfinancial system, an analytics program cannot be viewed as a pure technical exercise that involvesdata and statistical models. Rather, there is a need for a deliberate approach involving multipledisciplines — technologists, economists, and examiners — that builds a flexible solution capable ofsupporting multiple iterations with minimal incremental effort within a portfolio or across the system.To this end, regulators should view analytics as an enterprise-class solution that has a clear missionand objective, and that is capable of evolving with insights within a dynamic marketplace.2.0 Using the bits and bytesThe ability for regulatory agencies to assess financial markets and the institutions that operate inthese markets relies on identifying and collecting the most relevant data, with precisegranularity, and in a timely manner. Standardized and organized data collection enables thedownstream ability to aggregate, analyze and even store data for future analysis. These sourcesinclude:Micro-prudential Information Funding and liquidity measures Asset quality Management effectivenessMacro-prudential Information Size, leverage, and interconnectedness of institutions Funding sources Asset valuation booms The pursuit of predictability Insight into financial institution risk through bits and bytes 2

AbstractMacro-economic Economic growth and sectoral activity Inflation Interest and foreign exchange ratesRegulatory and compliance Call Reports, Thrift Financial Reports (TFRs) Micro-prudential information (supervisory examinations) Material Loss ReviewsOther sources Trade and industry repositories Market news and events External rating agenciesEach of the aforementioned sources can provide uncommon insight into the health of individualinstitutions and the broader financial system within which they operate. The analysis could servemultiple purposes, including comprehensive benchmarking, risk analysis, trend identification, andsystemic analysis. As an example, the illustration below shows how regulators would acquire,integrate, analyze, and eventually disseminate information. The information sources and resultinganalysis starts to build an institutional memory of the results and moves away from ad-hocanalytical solutions.Illustration 1: Information framework for analytics in the regulatory environmentAnalytics can be applied and be useful across different facets of the regulatory landscape. Makingraw data or the results of analysis available to interested organizations, academic or privateresearch, regulators can perform new ‖mesh‖ analytics combining the collective insights of thebroader community, which could result in analytical uses that go beyond expectations. Some The pursuit of predictability Insight into financial institution risk through bits and bytes 3

Abstractspecific applications of information analytics and the benefits such analysis would provide regulatorsinclude the following:1. Measuring the health of financial institutions: By providing regulators with an agile and 1 adaptable method to integrate a diverse set of information in addition to the traditional CAMELS rating, analytics can leverage non-traditional information sources to detect failure symptoms earlier in the business cycle. Troubled organizations may find themselves in a downward spiral if core funding sources evaporate when asset losses mount. Analytics can be used to forecast depositors and deposit accounts that are at risk for attrition. Many banks track the probability of customer churn for marketing purposes; this information can also be used to illuminate which liabilities are at risk. Availability of depositor attributes, such as account age, depositor tenure, and recent changes to account holdings, can be combined with macroeconomic information to model the probability that depositors will leave over the next 30, 60, or 90 days. An understanding of this detailed risk to funding can provide an early warning sign that an organization could soon face a liquidity crunch.2. Monitor systemic risk: By analyzing macro-prudential information, it is possible to understand and measure the interconnectedness, leverage, and collective behavior of financial institutions. This ability will give regulators a way to measure systemic risk, a key element of the Dodd-Frank Act. The emphasis in this case is on collecting and aggregating the right information sources.3. Detecting financial fraud: Analytical methods can be used to detect when an organization’s transactional data is more likely to contain artificial numbers, which can be used to detect fraudulent activity. Human interference is not random and rarely follows the expected natural distributions, allowing analytic tools such as Benford’s Law to locate potential trouble areas. Benford’s Law states that the distribution of digits coming from large datasets must follow a prescribed pattern. This principle has demonstrated the ability to search out and identify groups of accounts with suspiciously unnatural activity. Neural nets, Kohonen Networks, and other quantitative techniques can also be used to search for suspicious transactions, which may hint at the presence of fraud.4. Qualitative measurement: While the health of an organization and its capital ratios are clearly measurable through quantitative techniques, the quality of management is harder to quantify. Models can be used to analyze managerial quality through the financial statements. The model examines the financial end-state of an organization and translates inputs and outputs to determine the efficiency of its management. For example, there are models and research that support the possibility of using such inputs as the number of employees, salary expense, and noninterest expense and evaluating them against such outputs as core deposits, earning assets, and total interest income. The efficiency with which these inputs can be translated to outputs can measure management efficiency. Quantifying the management effectiveness allows correlations to be drawn between an organization’s financial soundness and the quality of its management. Management effectiveness can also be measured by using analytical techniques that can quantify and measure qualitative assessments, such as regulatory assessments, rating-agency ratings, equity-analyst assessments, and other qualitative reputational information.These examples provide a flavor of the various uses of these expanded analytics. It is also importantto understand that no single predictive model or analytical technique has proven to be a panacea toaccurately predict bank failures. Predicting bank failures will continue to rely on the experience ofbank examiners, but can be complemented by the science and rigor of analytics and modeling.1 Capital Adequacy, Asset Quality, Management Effectiveness, Earnings, Liquidity and Sensitivity to Market risk The pursuit of predictability Insight into financial institution risk through bits and bytes 4

AbstractWhen appropriately combined with financial institution knowledge and supervisory instincts,information analytics can be an important tool for effective supervision.In a vacuum, analytical techniques will not be as powerful as when used alongside other oversightmethods and evaluation tools, such as capital and funding ratios, asset quality assessments andmanagement effectiveness reviews. Bringing together powerful statistical techniques and well honedsubject matter knowledge, professionals such as technologists, economists, and examiners, canhelp aid both the regulators and the regulated.3.0 Piecing together an analytical solutionThe saying that ―Rome was not built in a day‖ is especially true when it comes to analytics. The keyis to start small, iterate with insight, and plan for complexity. Approach analytics as a mission criticalinitiative and build a center of excellence around the practice of analytics. Garnering the supportand commitment of senior leadership, and the requisite disciplines, is critical to the effectiveness ofthe program.Following are some practical steps that have been outlined to help organizations start piecing thebits and bytes together and develop a strategic analytical program:Clarity of purpose: Start by being specific with the hypotheses you intend to test or thefundamental questions that you need answered: How does asset (especially loan) quality change prior to failure? What are the trends in exposures by industry sector, consumer groups, as well as by loan purpose, across institutions? What are the primary determinants of loan losses during 2007–2010? What are the major trends with funding and liquidity?Source the “right” information: The source of information and the level of granularity of data areimportant for accurate and meaningful analytics. To answer the first question — asset quality is ameasure to determine the health of an institution and is typically computed by using past-duecalculations and adverse-calculation ratios. Attributes such as loan type, monthly loan balance,and loan status could demonstrate meaningful correlations with asset quality. These metrics canmost likely be found in core banking systems, which, for failed institutions, would be available withthe regulators. Start with a small set of institutions and test the initial hypothesis Regress and test the model by increasing the statistical population Analyze the data across different dimensions to uncover m eaningful correlations and eliminate false correlationsCorrelations such as the number of non-current loans over time, credit concentration by asset class,or demographic trends can provide insight into other factors that influence asset quality, which ma yhave been omitted in prior analysis and can result in the reduction of statistical biases.Funding and liquidity measures require analyses of funding sources such as deposit mix, depositor―stickiness‖ and funding tenor.Leverage existing information management, performance management, or advancedanalytic components, enhancing and tethering them to drive improved and more effective regulatorysupervision. The pursuit of predictability Insight into financial institution risk through bits and bytes 5

AbstractEnvision an enterprise-class solution: Starting small and focused does not mean creating an ad-hoc and fragmented solution. An important first step to creating an enterprise-class solution is thesetup of a purposeful governance organization that is capable of defining standards, policies, andprocedures to handle data exceptions, and address data quality issues. Defining standards andrules to handle exceptions significantly increases the accuracy of the model and reduces the cycletime between iterative test cycles.Spend time up-front to design a loosely coupled, flexible, and scalable architecture. T he ability tointroduce new scenarios and external factors enables the analytical solution to be agile whilereacting to changing market conditions.Develop a data visualization strategy: When dealing with large volumes of data it is important tounderstand the results with minimal amount of attention. By understanding and applying theprinciples of human perception, data visualization enables the effective dissemination of information.Having a strategy for how data and information can be visualized is important to eventually be ableto make actionable decisions based on the analytical results. People Deliver insightful and Process actionable results Information Sharing Analyze and interpret results Information Analytics Integrate and synthesize information Data Aggregation Identify and acquire information Data Collection TechnologyIllustration 2: Enterprise-class framework for information analyticsHow organizations look at risk needs to be clearly re-examined, given that what was considered bymany to be a seemingly safe and simple financial product like mortgages went on to destabilizefinancial markets across the world. Taking advantage of sophisticated analytical models andtechniques can provide regulators with an important tool to measure risk in a complex,interconnected, highly volatile, and constantly evolving financial marketplace. Beyond monitoringrisk, results from assessments of this nature can also be used for insurance pricing in response tochanging risk and market conditions by regulatory agencies like the FDIC and SIPC – an effectiveway to proactively control institutional risk. The pursuit of predictability Insight into financial institution risk through bits and bytes 6

AbstractConclusionWe all know for certain that this is not the last financial crisis the world will witness and theemergence of new organizations, products, and transactions will pose new risks in the future. Inpreparing to identify and deal with new risks, regulators will have to walk across a complexlandscape with fragmented sources of information where political and technological challenges withsourcing, standardizing, and aggregating information for the purposes of analytics will persist.However, regulatory agencies also have an opportunity to take advantage of the existing winds ofchange and leverage the technology and tools at their disposal. Developing an enterprise-classinformation analytics solution that will help monitor today’s risk and be flexible to tomorrow’s threatscould bring regulators one step closer to the pursuit of predictability.Authors/Contributors Ali Bandukwalla Mike Greene Kevin Bingham Alex McLuckie Vishal Kapur Edward Hida The pursuit of predictability Insight into financial institution risk through bits and bytes 7

AbstractReferences1. SIFMA Systemic Risk Information Study SIFMA, Deloitte & Touche2. Financial Crises and Bank Failures: A Review of Prediction Methods By Yuliya Demyanyk and Iftekhar Hasan3. Bank Failure Prediction: A Two-Step Survival Time Approach By Michael Halling, Department of Finance, University of Vienna4. Can the Equity Markets Help Predict Bank Failures? By Timothy J. Curry, Peter J. Elmer, Gary S. Fissel5. Capitalization of the Bank Insurance Fund By Kevin P. Sheehan, Financial Economist, Federal Deposit Insurance Corporation6. Restoring American Financial Stability — Discussion Draft Senate Committee on Banking, Housing, and Urban Affairs, Chairman Chris Dodd (D-CT)7. Bank Failure Prediction Using DEA to Measure Management Quality By Richard S. Barr, Thomas F. Siems8. Predicting bank failures using a simple dynamic hazard model By Rebel A. Cole, Departments of Real Estate and Finance, DePaul University9. Bank Failure Prediction Using Modified Minimum Deviation Model By Ali Argun Karacabey, Ankara University, Faculty of Political Sciences, Turkey10. How market power influences bank failures: Evidence from Russia Bank of Finland, BOFIT Institute for Economies in Transition11. The SCOR System of Off-Site Monitoring: Its Objectives, Functioning, and Performance By Charles Collier, Sean Forbush, Daniel A. Nuxoll, and John O’Keefe*12. Sources of Historical Banking Panics: A Markov Switching Approach By Kathleen M. McDill, Kevin P. Sheehan13. Risk-neutralizing statistical distributions: With an application to pricing reinsurance contracts on FDIC losses. FDIC Center for Financial Research Working Paper By Dilip B. Madan, Haluk Unal14. Bank Management, 6th edition. Timothy W. Koch and S. Scott MacDonald15. Lessons for the future: Ideas and rules for the world in the aftermath of the storm, Part I Guido Tabellini, Professor of Economics at Bocconi University, Milan, Italy The pursuit of predictability Insight into financial institution risk through bits and bytes 8

ContactsJoni SwedlundLead Client Service PartnerDeloitte Consulting LLP+1 202 220 2680jswedlund@deloitte.comEdward HidaPartnerDeloitte & Touche LLP+1 212 436 4854ehida@deloitte.comAshish MidhaPrincipalDeloitte Consulting LLP+1 212 618 4175amidha@deloitte.comVishal KapurSenior ManagerDeloitte Consulting LLP+1 202 758 1306vkapur@deloitte.comThis publication contains general information only and is based on the experiences and research of Deloitte practitioners.Deloitte is not, by means of this publication, rendering business, financial, investment, or other professional advice or services.This publication is not a substitute for such professional advice or services, nor should it be used as a bas is for any decision oraction that may affect your business. Before making any decision or taking any action that may affect your business, you shou ldconsult a qualified professional advisor. Deloitte, its affiliates, and related entities shall not be res ponsible for any loss sustainedby any person who relies on this publication.About DeloitteDeloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and itsnetwork of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/about for adetailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please seewww.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries.Copyright © 2011 Deloitte Development LLC. All rights reserved.Member of Deloitte Touche Tohmatsu Limited

The pursuit of predictability

by Deloitte Analytics on Nov 22, 2011

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