Optimization applications in finance, securities, banking and insurance

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1 IBM Software IBM ILOG Optimization and Analytical Decision Support Solutions White Paper Optimization applications in finance, securities, banking and insurance

2 2 Optimization applications in finance, securities, banking and insurance Table of contents 2 Important differentiators 2 Competitive advantage in finance 3 Better decisions faster 3 Portfolio optimization and financial planning 4 Asset liability management 4 Derivatives pricing 4 Loan configuration and lending 5 Conclusion 5 Customer case studies Important differentiators The recent crisis in the financial sector lending, investment, securities, banking and insurance has focused executive attention on near-term issues of survival and recovery, especially in the area of risk management, while diverting attention from improving operational efficiency and building customer relationships. Yet, the two latter factors remain important competitive advantages for leading companies in this sector. Optimization technologies particularly IBM ILOG products have played an important role in enabling companies in the financial sector to make better decisions faster. As this white paper shows, IBM ILOG CPLEX Optimization Studio and IBM ILOG ODM Enterprise enable rapid development and deployment of applications that address the financial sectors most important competitive differentiators. Competitive advantage in finance IBM ILOG Optimization products provide tools that deliver significant competitive advantage for solving the most challenging problems encountered in finance. Three in particularly are well suited for optimization: Risk management: Recent events have caused many companies in the financial sector to revisit their risk management policies and the underlying analytical models that support them. Mathematical models remain at the heart of risk management, balancing the expected returns and correlations of market risk among securities. Current models account for investor preferences, sectoral risks, transaction costs and timing. Furthermore, as hedging with derivative securities has become commonplace, optimization models have played a key role in quantifying their value and implementing trading strategies. As the economic basis of risk management undergoes revision and evolution, reliable and fast optimization engines continue to provide the basis for effective risk management applications. Operational cost: Controlling transaction costs is critical in implementing profitable portfolio trading strategies. Optimization models can identify opportunities to group trades across multiple portfolios to take advantage of discounts or execute internal trades among portfolios within the same organization, avoiding the cost of going to the market. The ability to manage large numbers of trades automatically through an optimization application provides the basis for effective operational cost control applications. Product innovation: Firms in the finance sector have historically avoided commoditization by innovating to create new products and services. In the past, impressive growth has been achieved in mature markets. However, today s growth opportunities in those geographies are declining, and meaningful future expansion will come from new markets. The firms that will benefit in these emerging profit pools will be the ones that specialize in the areas valued by their clients while optimizing their global reach. Optimization can provide the basis to do both in unexpected ways.

3 IBM Software 3 Optimization tools do not remove inherent risks in financial markets, but they do allow users to optimally leverage resources while balancing profit considerations with risk. They are an inherent part of strategies for making more effective management decisions. Better decisions faster IBM ILOG Optimization products have been used effectively by many financial institutions in model development, empirical validation, benchmarking and comparative studies, while developed applications can easily interface with existing systems such as spreadsheets, databases, data visualization tools, statistical analysis packages, and existing frameworks and user interfaces. Optimization models Portfolio optimization Financial planning Asset-liability management Derivatives pricing Loan configuration and lending Optimization products IBM ILOG CPLEX Optimization Studio IBM ILOG ODM Enterprise Portfolio optimization and financial planning Mathematical programming algorithms implemented with CPLEX Optimization Studio are well suited for solving index tracking, portfolio rebalancing models, and portfolio planning models, including mean variance and factor models. Various side constraints modeled with linear programming (LP) and quadratic programming (QP) are often included in these classical portfolio optimization models. IBM ILOG Optimization tools are also effective at solving mixed integer programs (MIPs) induced by cardinality constraints, fixed costs, and all sorts of indivisible decisions that can occur when issuing a security, as well as fixed transaction costs and percentile risk measures (CVaR). IBM ILOG CPLEX Optimizers provides algorithms that are, in turn, ideal for processing discrete optimization (MILP, MIQP and MIQCP) models, and deal with these constraints while handling common risk measures. Nonlinear terms, including nonlinear transaction costs that have an impact on trading, benefit from the ability to model piecewise linear functions and special ordered set (SOS) formulations, which are both easily accessible with CPLEX Optimization Studio. Monte Carlo simulations have become the common method used by financial institutions to test the robustness of their risk models. Testing them against a large set of scenarios helps validate the underlying assumptions, building trust that they will fully and effectively capture risks. These simulations can then be solved with optimization, allowing the handling of hundreds of instruments and scenarios. ODM Enterprise is particularly well suited to these uses, with its scenario management and database capabilities. The flexibility of the IBM ILOG Optimization products allows resolutions to be embedded within iterative strategies and to interact with other systems to solve a series of scenarios, which can be provided by alternative scenario generation procedures, to test several modeling possibilities, optimize portfolio rebalancing at multiple points in time, analyze the consequences of portfolio composition in subsequent trading periods and other comparative operations.

4 4 Optimization applications in finance, securities, banking and insurance Asset liability management A typical problem in asset-liability management is determining which assets an investor should hold, and in what quantities, in each period in order to maximize expected wealth at the end of the planning horizon. The investor may be a financial institution such as a bank or insurance company, but many institutions also provide this service for their customers. Therefore, effective tools for asset-liability management provide a basis for competitive advantage. Problems in asset-liability management can be very hard to solve when they incorporate complex probability distribution functions that model the various uncertainties facing an investor, such as the expected returns from the assets and the expected liabilities. Typically, the more accurately a probability distribution function models the real world, the more complex and harder it is to solve. Mathematical problems that deal with uncertainty are referred to as stochastic programs and require very powerful mixed integer programming (MIP) optimization engines to be solved, as well as advanced modeling and solution strategies (for example, Bender s decomposition). Derivative pricing Derivative pricing has become a very important problem in finance, and many analytical methods (for example, Black- Scholes and Stochastic Volatility) have been developed to assist investors with their decision-making process. Lately, researchers have started using mathematical programming techniques to answer basic, as well as advanced, questions in derivative pricing. Mathematical programming provides more generality and flexibility than many of the specialized algorithms developed in the past to price individual types of derivatives, thus enabling innovation and reducing time to market for new derivative products. Mathematical programs solved with CPLEX Optimization Studio allow decisions to be incorporated over an extended planning horizon taking into account market volatility based on stochastic scenario trees, and can easily incorporate taxes, transaction costs, trade restrictions and other portfolio constraints, which are typically not straightforward with closed formula solutions. Loan configuration and lending Optimization tools can prove valuable in maximizing overall profit and customer acceptance in loan configuration and lending applications. Some of the key criteria for loan qualification include credit score, amount for the loan, payment terms and interest rates. Often the factors that lead a customer to accept a loan are perceived to be at odds with the factors that make the loan attractive to a lender. But in today s competitive market, where customers have many choices for financing, a rapid, yet flexible, response from a lender can be instrumental to winning business. Lenders also want to manage their loan portfolios to achieve specific targets for risk and return while satisfying regulatory and prudency requirements. There are potentially many other considerations that a loan company might want to consider in developing its loan portfolio, such as: Cardinality rules that specify the maximum number of loans in a portfolio Business rules that specify combinations of loans that can or cannot be included in the same portfolio The loan portfolio problem can be modeled as a LP, or in cases of more complicated constraints, as a MIP. Both of these problem types can be modeled and solved efficiently with CPLEX Optimization Studio.

5 IBM Software 5 IBM ILOG Optimization products can then be used to develop solutions based on a selection of constraints and parameters. The optimization tools will, for instance, take into account user-defined limits to allow the best solution to be offered for the customer and the financial institution. Credit configuration is then optimized and the needed time to analyze a customer demand before taking a decision is shortened. Conclusion Recent advances in computer processing power and IBM ILOG Optimization technology, allied with larger computer memory and data management systems, allow problems to be solved today that were intractable only a few years ago. As a consequence, the finance, securities, banking and insurance industries, with their rich heritage of developing quantitative and probabilistic models with large sets of data, can build practical optimization applications to optimally leverage resources while balancing profit considerations with risk. Customer case studies Bank asset management IBM WebSphere ILOG JRules is used to ensure that incoming market information from various sources is compliant with data quality standards and regulations. JRules provides additional agility with real-time use to monitor merger and acquisition data, changes in stock values and other critical information, enabling real-time updates to this bank s asset management reference data systems. Once the data has been qualified by JRules, IBM ILOG CPLEX Optimization Studio determines the optimum portfolio configuration based on predefined investment guidelines, or benchmarks, creating tax-efficient portfolios while meeting customers investment goals and risk profiles. CPLEX Optimization Studio also reduces the tracking error between benchmarks and a tailored portfolio. Account managers can then comply automatically with specific client requests, such as asset allocation, as well as regulations, further personalizing the bank s asset management offerings. Investment manager Optimization creates competitive advantage as part of innovative services that have saved customers hundreds of millions of dollars. Optimizing portfolio management helps one of the world s largest investment managers create competitive advantage and provide substantial savings to its clients. Three of the company s applications use CPLEX Optimization Studio: Trade crossing Several hundred million dollars in transaction cost savings have been achieved through an innovative approach to trade crossing in which the company uses ILOG Optimization software to match thousands of assets in buy orders against similar assets in sell orders, where the orders come from thousands of different funds under management. Leveraging a leading position as a top global manager of institutional assets, the trade-crossing application avoids market trades and related transaction costs (for both buyand sell-side trades). Because of the volume of trades and the complex set of business policies and regulatory guidelines, trade crossing could not be performed effectively or efficiently without using advanced optimization software.

6 6 Optimization applications in finance, securities, banking and insurance Portfolio in-kinding A large number of clients opted to transition their assets to hundreds of the company s funds. However, transitioning to new strategies normally has potential for significant transaction costs. In this second application, portfolio in-kinding, the company used ILOG Optimization to transfer a large majority of portfolio values directly ( in-kind ) into their targeted funds, saving clients hundreds of millions in transaction costs. Fund rebalancing In addition to taking full advantage of both trade crossing and in-kinding to minimize the related transaction costs, the company uses ILOG Optimization in fund rebalancing, a huge task because the company manages hundreds of billions of dollars distributed into thousands of funds tracking over hundreds of indexes. The goal is to create optimal holdings of fund assets through appropriate trades, allowing fund managers to perform accurate index tracking while minimizing transaction costs. The high complexity of reconciling risk and return objectives, fund policies, and regulatory guidelines makes this process a differentiating factor in the investment industry. The application has saved hundreds of millions in transaction costs. Trade crossing and in-kinding are a major source of competitive advantage, as they allow managing the high complexity of reconciling risk and return objectives, fund policies and regulatory guidelines, and makes the fund rebalancing process a differentiating factor in the investment industry. Portfolio manager This company provides portfolio management solutions to institutions and wealthy individuals. It is widely recognized for its pioneering research in the field of tax-efficient investing. It uses an application that automates the process of determining the best mix of tax-efficient investments in a consistent and timely manner. CPLEX Optimization Studio enables the company to evaluate portfolios daily. The evaluation incorporates cash level, capital gains and losses, risk, investor needs, risk tolerance and time since last optimization. CPLEX Optimization Studio and the included Optimization Programming Language (OPL), simplifies the modeling process by quickly modeling a problem and converting it into code used by the IBM ILOG CPLEX Optimizers. With billions in assets under management, the firm specializes in combining index-based stock selection with active tax management within a privately managed account. The company is one of the few firms that report after-tax returns, and as a recognized leader, it works with the investment industry to create new standards in this area. As many of its competitors do not factor taxes when rebalancing portfolios, this affords the company a sizable advantage. The optimizer also improves the stability, quality and speed of analysis. Compared with passive management, the company estimates that its portfolio optimizer increases after-tax returns by up to 1.5 percent per year. It also accommodates a broader range of portfolios, achieves twofold growth and enlarges its customer base. It also allows faster responses to requests and better delivery of services to customers.

7 Notes

8 For more information To learn more about the Optimization Applications in Finance, Securities, Banking and Insurance, please contact your IBM representative or IBM Business Partner or visit the following website: ibm.com/software/websphere/industries/financial/ Additionally, financing solutions from IBM Global Financing can enable effective cash management, protection from technology obsolescence, improved total cost of ownership and return on investment. Also, our Global Asset Recovery Services help address environmental concerns with new, more energy-efficient solutions. For more information on IBM Global Financing, visit: ibm.com/financing Authors Jeremy Bloom IBM ILOG Optimization Sofianne Oussedik IBM ILOG Optimization Copyright IBM Corporation 2010 IBM Software Group Route 100 Somers, NY U.S.A. Produced in the United States of America September 2010 All Rights Reserved IBM, the IBM logo, ibm.com, CPLEX, ILOG and WebSphere are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol ( or ), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. A current list of IBM trademarks is available on the web at Copyright and trademark information at ibm.com/legal/copytrade.shtml Other company, product or service names may be trademarks or service marks of others. Please Recycle WSW14080-USEN-01

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