Analytics: Broader Role, Deeper Insight in Today s Capital Markets

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1 Analytics: Broader Role, Deeper Insight in Today s Capital Markets Rodney Nelsestuen Financial Services Strategies and IT Investments March 2011 Executive Summary Analytics is an increasingly powerful business tool, one that is critical for competing successfully in today s capital markets industry. Its value is well proven in enabling better-quality high-speed trading decisions and outcomes. Today s analytics tools can be used across lines of business and within business functions to enrich insight and intelligence, monitor operations, evaluate customer relationships, and manage those relationships profitably. In addition, analytics can help firms provide market-leading advice both internally and to clients of all types. The value lies in offering these capabilities in real time and in the stream of business operations. In short, analytics provides the high-quality decision-making capability that will define the difference between winners and losers in tomorrow s industry. Analytics helps capital markets firms grow their business profitably. Both sell-side and buy-side firms can make better trading decisions by analyzing historical data with real-time market data against pending transactions to provide alerts to avoid unnecessary risks and seize opportunities. Alternatively, firms can use analytics to measure how profitable an institutional customer has been, is, and will be and provide insight to make that customer more profitable. Buy-side firms can gain new insights to the quality of recommendations they receive based on performance over time and within targeted investment strategies and time frames. Analytics helps a firm understand whether it is at risk of losing any of its most important clients as well as which clients the firm may wish will exit because of their lower profitability. As for operational risk, analyzing business operations can uncover risks so that firms can take appropriate actions to avert them, including changes to improve business processes. Analytics can provide clients with deeper insight. Through predictive analytics, sell-side firms can offer institutional clients better investment insights resulting in better decisions. On the buy side, investment firms can use analytics to identify unintended changes to investment style resulting from either overt actions of investment managers or by modeling changes in market dynamics that could impact current investment strategy. This report discusses historical and emerging uses of analytics, including how: Analytics is improving the traditional role of risk assessment and risk management by providing deeper insight to both the firm and its clients in real time. Leading technology vendors are expanding the role of analytics to deliver a broader range of business insights. Analytics will improve customer management and customer experience as a crucial element to creating and sustaining profitable, long-term relationships. TowerGroup Research is available on the Internet at Tower Group, Inc.

2 Beyond Risk Management: The Evolution of Analytics Technology Financial institutions face a redefinition of the capital markets business driven by an evolving global marketplace and more stringent regulatory requirements. These factors make it necessary to have a better understanding of the future impact of today s decisions. Moreover, the depth and breadth of applied analytics and the range of use cases for predictive analytics is increasing. Among the new use cases is the need for greater transparency into the probable outcomes of business decisions. Technologies such as Microsoft StreamInsight capture data in transit in the business before it is written to a database. This allows analytics to be applied in memory to offer on-the-spot, real-time insight to business decisions. Applying analytics within an existing database reduces the time needed for completing Monte Carlo calculations and other timeconsuming, analytical processes involving large data sets. Other well-known vendors across the size and service spectrum, including HP, IBM, and SAS, have also been investing in these lowlatency capabilities. These and other vendors are discussed below. The magnitude of regulatory change and regulators' and investors' demand for greater visibility and transparency into business operations will ultimately drive firms to invest in predictive analytics. The best firms will use this transparency to their own advantage. Predictive Analytics for Risk and Opportunity Management As illustrated in Exhibit 1, the role of analytics has evolved from static analysis of the outcomes of past decisions, to modeling the implications of decisions being contemplated but not yet made, to providing reliable statistical predictive insight across a broad spectrum of business issues. Exhibit 1 Analytics in Capital Markets: Evolution of Roles, Uses, and Value Role of Analytics Evaluative Analytics Risk Management Traditional Uses Monitor Business Performance Causal Analytics Manage Business Operations Predictive Analytics Grow the Business Emerging Uses Customer Intelligence Knowledge Management Business Intelligence Value of Analytics Improved data access Real-time insight Growth opportunities Customer relationship management Predictive value Competitive edge Source: TowerGroup 2

3 Understanding the risk in a portfolio is only one step in today s analytics value chain. Greater insight comes from being able to do predictive modeling of the changes to the risk profile of that portfolio that are caused by adding or shedding certain assets or asset classes. Example: Analytics can leverage internal systems and application data, external market data, and macroeconomic trends to predict the impact of a systemic change on, say, a fixed income portfolio considering both its current state and new investments. This is an increasingly important capability, given recent changes to the risk profiles and factors impacting valuations of investments such as government bonds. Evaluating data on investment performance against those potential new investments and incorporating real-time market data can uncover areas in which the firm can increase profits. Example: The financial crisis of the past decade demonstrated that securities lending has risks that were previously unknown. Firms are seeking ways to increase profits without taking unknown risks. Predictive analytics, combined with historical data on the results of securities lending in good times and bad, can provide a statistical advantage through more reliable insight into the risks being undertaken for the fees earned and can predict the likelihood of risk events of various levels. Finally, analytics today provides not only internal insight for business intelligence and knowledge management but also better insight to customers, thus demonstrating value on both sides of a transaction or relationship. By continuously examining both internal and client portfolios, firms can identify investment trends that bear unacceptable levels of risk or those lacking sufficient return for the risk taken. Investment data and history, portfolio performance, market trends and market information can be analyzed using statistics to gain an edge in making the right decisions, whether conducting proprietary research or making recommendations to clients. Instead of focusing on internal operational matters or on external customer relationships, predictive analytics can serve both. Modern Analytics Expands the Value Chain The quality of decisions made differentiates winners from losers in today s industry. This is true whether the firm is buying, selling, or servicing relationships. Thus, the range of mutual benefits that analytics can provide to both buyer and seller in a given transaction or ongoing relationship is increasing. Emerging Value in Analytics Analytics leads to a better understanding of operational risk whether in a firm s own lines of business or for client institutions. Examples: Consider the many areas of operational risk that exist in collateral and custodial services. The data available in a myriad of internal and counterparty systems and applications can be analyzed to uncover performance levels, error rates, and costs based on historical results of collateral management. This level of analysis is only the beginning. 3

4 The future of analytics will include the ability to parse unstructured data from a variety of documents and agreements to identify risks that would otherwise be missed through mere human inspection. With that foundation, analytics can model changes to both documents and processes, which will reduce the rate of error in managing collateral and the risks of those errors repeating as improved business processes are put in place. In short, analytics can provide an early warning system to help investment companies adhere to their internal strategies or make effective changes to those strategies when necessary. Exhibit 2 depicts this expanding value chain. Exhibit 2 Expanding the Value Chain of Analytics Identify investor goals Uncover investment bias Identify investment risks Identify operational risks Measure customer profitability Find mutual opportunities Identify operational risk Profitability (both ways) Gain portfolio insights Identify decision processes Manage key relationships Firm Value Shared Value Client Value Source: TowerGroup As the rules of the business change and global regulation becomes more stringent, operational risk is taking center stage, especially in light of the need to allocate economic capital more accurately. Operational risk exists in the decisions firms undertake, the processes they employ, and the people who manage those processes. Firms must manage services fraught with such risks and understand where weaknesses might result in unacceptable losses or operational costs. Examples: Data gathered from historical back-office functions such as clearing and settlement services can be interrogated to discover the root causes of periodic failures in completion, quantify the historical cost, and predict the ongoing error rate (and its future cost) as business grows. Using the same information and proposing a change in business processes, analytics can model the change, forecast the likelihood of a reduction in errors, and project operational cost improvements. All of this information benefits the firm internally and helps the firm 4

5 deliver a better customer experience through timeliness and superior accuracy of clearing and settlement functions. Predictive analytics can play a key role in reducing operational risk in document-intensive functions. The task of managing operational risk includes making certain that the agreements and documents used across the company are valid and enforceable. Basic though it may seem, nonperformance on collateral promises and other legal aspects captured in unstructured data formats is sometimes a matter of judgment. That is, the terms of any agreement may be open to interpretation. Using analytics, the historical performance of counterparties to agreements as well as the veracity of the agreements themselves can be evaluated to determine whether they should be amended for clarity or force of law. This emerging use of analytics can shortcut the road to achieving better operational results. Document analysis of requests for proposals, contracts, and other legal documents as well as and other correspondence entails operational risk. Terms and conditions of legal agreements, contractual dependencies and interdependencies, or limitations of liability in counterparty agreements can be analyzed to assess the risk each of these agreements poses. After an investment manager understands such risks at the instrument level, the firm can apply analytics to a portfolio or segment of a portfolio to gain a broader view of enterprise risk. Identifying risks associated with information from custodians and third-party providers serves as an early warning system spotting gaps in business processes whether automated or not. This expands the firm s ability to manage risk for itself and its clients. Application of analytics to unstructured data includes analysis of communications in various forms of the social network. Although still a nascent skill, collecting and evaluating information from a variety of social media will allow a firm to identify nuances in market trends that are not immediately apparent in traditional market data. Attitudes, whether corporate or individual, can be discovered before going viral to enrich the relationship between buyer and seller. These softer indicators provide insights that may not be evident in the normal course of business. Understanding the Analytics Vendors' Capabilities The analytics vendor landscape continues to expand across all facets of the financial services industry. Thus, financial institutions must carefully evaluate the efficacy of various technology providers against the scope of their intended usage of analytics. An important role of analytics is to assess the likely impact of emerging trends and provide decision makers with the type of information that leads to first-mover advantages. Thus, it is critical to evaluate whether the firm itself has the ability to find and analyze the right amount, type, and quality of data or must rely on a solution provider to meet this need. Can the Vendor Provide Insight to All Stakeholders in the Institution? It does little good to have analytics as a one-off source of information. For example, in evaluating market data, vendors must be able to depict the outcomes of analysis in an actionable way. This requires rendering statistical insights through visualization and rich interfaces and displays that are tied to business processes. Unless an analytics application integrates insight with process, users will be unable to take action immediately in time-sensitive business functions or, worse, they may ignore the output altogether. The same principle applies to analytics used in investment decisions, tracking and forecasting performance, and providing insight on operational risk. 5

6 Does the Vendor Know Your Business? Domain expertise (i.e., business knowledge) is critical to understanding whether the right data has been gathered and analyzed. In fact, domain expertise, including business and operational knowledge as well as a deep understanding of compliance needs, is another key difference between vendors abilities to create value through analytics. Numerix is one example of a company with focused domain expertise leveraging predictive analytics. Numerix provides a range of analytics capabilities that helps traders to understand pricing, valuation, and risk across multiple asset classes. Pricing products include Excel add-ins that provide pretrade pricing analytics to improve decisions and outcomes of trades. The vendor's risk tools allow for intraday portfolio valuations of over-the-counter derivatives along with intraday credit valuation adjustments based on actual and projected changes in the marketplace. In more traditional banking and insurance, its asset/liability products allow the user to vary economic scenarios to understand their impact in different business units, in valuing and managing specific assets such as variable annuities, and in hedging life insurance products. Large, diverse technology providers whose foundations are in installed systems for data management have recently acquired analytics applications (e.g., IBM acquired SPSS), while other firms, like Microsoft, have chosen to invest heavily in their partner ecosystem, not wedding to any one solution but rather leveraging its expertise at the foundation layer and exposing it through partners such as Numerix and Portrait Software mentioned elsewhere in this paper. Other solutions range from online, on-demand services that are free or nearly free such as Google analytics, to more traditional heavyweight solutions with proprietary data models as provided by SAS, Algorithmics, and RiskMetrics. Many, if not most, institutions still run and manage their own analytics strategy, but in the future, more institutions will embrace hosted services such as Sybase s server/data/analytics offerings, appliances using a combination of onsite data and remote management, and even cloud-based offerings developed on platforms such as Microsoft Azure. As mentioned previously, firms will continue to serve the real-time needs of business insight by leveraging products that achieve real-time data capture and analysis as data is encountered, rather than only after it has gone to an auxiliary database. The latter approaches increase the options available in accessing analytical capabilities and may change the cost model from fixed to variable pricing. Analytics in Rethinking Customer Relationship Management (CRM) With real-time access to more granular, specific data, capital markets firms can both achieve better insight into internal operations and offer better advice to institutional clients, making them more profitable as clients while improving results for those clients. This represents a level of customer relationship management (CRM) not possible in most parts of the financial services industry. CRM Trends Because of the relationship between seller and buyer in an institutional investment business, CRM is symbiotic, both parties having a shared stake in success. Whereas Exhibit 2 identified the value chain emerging from the increased use of analytics, Exhibit 3 demonstrates this value in establishing holistic CRM capabilities leading to improved profitability for buyer and seller alike. 6

7 Exhibit 3 Analytics and Client Relationships: Holistic Customer Relationship Management (CRM) Profitability Opportunity Model decision impact Discover client return on investment Achieve operational excellence Gain competitive advantage Compliance Discover untapped business opportunities Quantify client value Measure and manage operational risk Risk Management Traditional role Risk versus return Discrete risk areas Holistic, firmwide risk management Identify exposures Manage compliance effort Reduce compliance cost Improve results Reduce oversight Extending the value of analytics in capital markets means covering the spectrum of risk, compliance, opportunity, and profitability Source: TowerGroup Risk Management. As captured in Exhibit 3, the newer elements of analytics can be used stepwise to aggregate risk in discrete areas of the business and thus provide firmwide views of risk, a capability that will allow firms on all sides of a transaction to fulfill their chosen strategies. Compliance. The magnitude of change in the regulatory environment has made compliance a higher-value activity, and it will remain so over the next two to three years. Analytics will become part of the compliance toolkit for evaluating compliance status and pointing out weaknesses in processes and controls. Moreover, regulators will be looking for better information and insight to compliance standards for capital allocation and liquidity. Predictive analytics will allow firms to project liquidity risks under more scenarios and to test contingency plans. Capital allocation can be accomplished at a finer-grained level, which increases the confidence regulators will have in the firm s capital position. Opportunity. Identifying new business opportunities will be critical to capital allocation and liquidity management, especially as operational risk becomes a major element of regulatory oversight. Analytics can illuminate the impact of changes in market conditions on the company s investments and their ultimate liquidity. As in compliance, analytics can be used to project capital needs under various scenarios. Again, the efficient use of capital can be accomplished without compromising viability. Profitability. Another new frontier in the analytics value chain is the use of analytics to demonstrate which clients are profitable, which ones meet a firm s requirements for risk and return, and which do not. On the client side, analytics will provide insight to the quality of outcomes that recommendations for investment and investment strategies yield. The ability to interrogate historical data to make future fact-based decisions provides a competitive edge. 7

8 Demand for greater breadth and depth of CRM technologies is only one aspect of the need for increased sophistication in managing customer relationships. CRM technologies must provide nuanced insight such as understanding the impact of a specific marketing campaign on business growth rates. Firms commonly apply such marketing campaigns to solve business challenges such as customer cross-sell and retention. Vendors such as Portrait Software are able to take analysis beyond the prediction of likely future customer behavior to the prediction of change that can be effected in that behavior through a particular marketing effort. The company's Uplift software leverages predictive analytics to measure the incremental impact of a marketing effort on specific market segments and down to individual customers. Portrait Software measures and predicts the magnitude of the influence a campaign has on a customer s behavior. This is an important distinction for several reasons. First, one can best optimize return on investment of marketing by focusing campaigns on those customers who are actually persuadable that is, customers whose behavior can be changed. Second, eliminating those customers who will take action without a marketing campaign, those who never act, and those who may react negatively can save marketing dollars while protecting customers from ad fatigue. Finally, having understood both the positive and the negative impact of an approach to the marketplace, targeting at a much finer-grained level will ensure that the right approach is used in the right segments. By predicting not only future customer behavior but also the actual change a marketing campaign can have on customers' future behavior, organizations reduce costs and simultaneously improve campaign results. Next Steps to Improved Business Results with Analytics Opportunities to leverage analytics are found on both sides of every transaction. While investment firms are looking for the right recommendations, sell-side firms are looking for a risk/return that satisfies their stated objectives. To take advantage of the power of analytics, firms should take the following steps. First, determine the extent to which analytics is currently used in the firm, and then identify both gaps and opportunities to leverage analytics more holistically. Consider applying analytics to: Analyze unstructured data from both internal and external sources to reduce risk, especially operational risk Evaluate and improve compliance to regulations to reduce the cost of compliance and find ways to automate these must-do functions Next, evaluate current decision-making processes across the firm. Consider how to expand the use of analytics to improve: Customer relationships through a deeper understanding of client needs and drivers Customer profitability by understanding which customers have the most potential and which customers may not have value for the firm over the long term Quality of investment recommendations by incorporating more and new types of data to better inform decisions. Performance on the trading desk by providing more reliable, real-time insight to decision makers Operational risk across the front, middle, and back offices by discovering gaps and weaknesses in processes that could lead to costly delays and errors 8

9 Finally, explore vendor solutions and approaches to analytics to determine the level of support needed, including: The responsibility for data management (determining whether it will be an internal function, delivered by the vendor, or a hybrid model) The ability of the vendor solution to integrate into day-to-day business processes so as to ensure execution on analytics-driven insight The range of delivery options including in-house, managed, or cloud-based options Conclusion The complexity of the marketplace, the growing role of compliance in defining and defending business strategy, and the need to differentiate themselves from the competition will make leading firms rethink the use of analytics, going beyond risk management to using predictive analytics in decision-making across the spectrum of investment decisions, business operations, and customer relationships. Although most firms find themselves either focused on performance and risk management or focused on client relationships and customer experience, predictive analytics can identify, model, and predict better outcomes for both purposes. In short, the comprehensive nature of the contribution analytics can make in today s industry is rapidly becoming fundamental to managing nearly every aspect of the business. Microsoft commissioned TowerGroup to conduct independent research and analysis of the role and emerging trends of analytics in capital markets firms. The content of this report is the product of TowerGroup and is based on independent, unbiased research not tied to any vendor product or solution. Although every effort has been taken to verify the accuracy of this information, neither TowerGroup nor the sponsor of this report can accept any responsibility or liability for reliance by any person on this research or any of the information, opinions, or conclusions set out in the report. 9