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Decision Optimization Enables Financial Institutions to Manage Risk, Innovate and Improve Operational Efficiency

Across industries, the term “optimization” has multiple meanings depending on the context. IBM offers many optimization technologies, services and tools tailored to meet the specific needs of various industries. It does this by leveraging modeling via an optimization engine and applying mathematical techniques/algorithms to search for the best solution/alternative.

In industries characterized by heavy mainframe usage, such as banking and finance, it’s crucial to maintain customer data security, scalability and performance, and reduce hardware and software maintenance costs. Deploying optimization on the mainframe helps tackle these IT goals through simplified architecture that assists in automating and streamlining transactional decision making while lowering costs for both firms and customers.

“With infrastructure spending—on computing power, networks, storage, help desks and so on—historically accounting for 57 percent of overall IT expense, it’s likely that this is the largest component of an organization’s IT cost of goods,” notes Rubin Worldwide founder Howard Rubin in his August 2012 Wall Street and Technology article, “The Technology Economics of the Mainframe: Mainframe Computing Still Growing in Banking.”

Volatility 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. Improving operational efficiency and building customer relationships remain important competitive advantages for leading companies in this sector.

Technologies like IBM Decision Optimization play an important role in enabling financial organizations to make better decisions faster. These advanced analytic solutions enable rapid development and deployment of applications that address the financial sector’s most important competitive differentiators. Indeed, deploying mathematical optimization capabilities in mainframe environments helps facilitate competitive advantage in three key areas:

  1. Risk management
  2. Operational efficiency
  3. Customer-focused product innovation

Risk Management

According to a recent Deloitte risk-management practices survey, financial institutions are steadily increasing spending on risk management and compliance. "Knowing that a number of regulatory requirements remain in the queue, financial institutions have to be able to plan for future hurdles while enhancing their risk governance, analytical capabilities and data quality efforts today,” says Edward Hida, DTTL global lead, Risk and Capital Management Services. “Those that do will be well placed to steer a steady course though the ever-shifting risk management landscape.”

Clearly, it’s time for the financial sector to revisit 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. Models can account for investor preferences, sectorial risks, transaction costs and timing. Furthermore, as hedging with derivative securities has become commonplace, optimization models are essential in quantifying their value and implementing trading strategies. As the economic basis of risk management continues to evolve, reliable and fast optimization engines continue to provide the foundation of effective risk-management applications. What’s more? Financial institutions looking to expand or modernize mainframe-based risk-management capabilities can add decision-optimization capabilities for very little investment.

Operational Efficiency

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; thus, avoiding the cost of going to the market. The capability to manage large numbers of trades automatically through an optimization application provides the basis for effective operational cost-control applications.

Product Innovation

The finance industry has historically avoided commoditization by innovating to create new products and services. In the past, impressive growth has been achieved in mature markets. However, meaningful future expansion is emerging from new markets. Firms that specialize in the areas valued by their clients (eg. personalization) while optimizing their global reach benefit in these emerging profit pools.

Decision Optimization

Optimization provides the basis to manage these three challenges in innovative ways while respecting the sensitivities of business constraints by:

  • Maintaining the security of sensitive customer data
  • Reducing IT and software maintenance costs
  • Improving end-to-end IT system performance

In the context of commerce, decision optimization bridges the gap between analytics and execution to minimize risk vs. reward, implement effective operational cost controls and offer personalized, high-value services to customers. This capability allows organizations to make sense of, or contextualize, big data and put it in action so they can sustainably apply analytics to operational processes. With such capabilities, optimized decision planning and execution can help businesses:

  • Transform big data insights into prescribed actions by integrating analytics and optimization
  • Exceed customer expectations while improving operational effectiveness by integrating prescriptive analytics and analytic applications throughout the enterprise
  • Improve responsiveness to customer demands through collaborative planning and execution decision making
  • Improve competitive advantage with configurable analytic applications that support your business processes
  • Reduce overall decision planning and execution costs by automating and streamlining decisions across the global value chain

In banking and finance, decision optimization enables personalized interaction with customers through capabilities such as trade netting, collateral optimization, trade crossing, portfolio rebalancing, asset and liability management, ATM cash management, international liquidity, derivatives pricing, targeted marketing and tax planning. These capabilities are now available on the mainframe through IBM CPLEX* Optimizer for z/OS*.

Make Smarter Financial Decisions

The finance industry in particular has experienced enormous change in the past five years due to financial crises, requiring tighter capital allocation and more accurate risk measurement. To prevent future crises, governments have adopted new regulatory measures, imposing requirements and constraints on financial operations. These regulations require robust optimization-based solutions to support asset-management decisions that will drive higher return on assets and significant competitive gains.

Adding solutions powered by CPLEX decision optimization capabilities on System z enables reliable and secure deployment of high business value solutions that help reduce working capital requirements, support long-term investment decisions and support the management of day-to-day cash liquidity challenges and risks.

While optimization tools don’t remove inherent risks in financial markets, they do allow users to optimally leverage resources while balancing profit considerations with risk. They’re a crucial element of IT and operational strategies for making more effective management decisions.

Aimee Emery-Ortiz is worldwide product marketing manager, IBM Decision Optimization.

Ferenc Katai, Ph.D, is product manager of the IBM CPLEX Optimization Studio.

Sofiane Oussedik, Ph.D., is the ILOG optimization technical sales manager at IBM.

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