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Financial Industry Crisis Spurs Reevaluation Of Risk Management

Nov 13, 2009 (10:11 AM EST)

Read the Original Article at http://www.informationweek.com/news/showArticle.jhtml?articleID=221601546


The financial industry crisis has turned the concept of financial risk management upside down. Following industry best practices, almost every financial company had been performing similar statistical algorithms on a trove of incomplete and incorrect data. Then, based on those analyses, they passed the risks from one balance sheet to another--risk management was considered only in terms of a company's own financial position. Now the main challenge is how to manage risks shared by the entire industry and the broader economy.

The tricky part of financial industry reform will be to retain the benefits of a strong, healthy financial sector. An appropriate analogy for financial reform can be found in the computer industry. Every year, we expect our computers to get faster, more powerful, and less expensive. Yet recently, the focus on computing performance has been tempered by the need to manage performance per watt. The cost and availability of electric power have become constraints in designing and managing data centers, and so chip designers have responded with new processors that measure, monitor, and control power consumption at a granular level. The chip designers didn't reduce the power usage by slashing functionality. Instead, they maintained and improved functionality even while reducing power through advanced chip logic.

We should hold similar expectations for the financial services industry. People need access to low-cost financial services, global investment opportunities, and expert advice on managing risk through insurance, financial management, and portfolio diversification. Businesses need to raise capital through financial instruments tailored to the needs of their investors, and employ rich capabilities in cash management. And, yes, financial services firms should have the freedom to create and market innovative financial instruments--including collateralized instruments, derivatives, and swaps--as long as they're managed through an infrastructure that both monitors and measures their health and enables these instruments to be safely unwound in the event of financial distress.

New technology platforms, industry standards, and common networks are part of making all that possible. The necessary ingredient is a common infrastructure for systemic risk management. Much like a microprocessor with power management features, the industry needs sophisticated circuitry to "cool itself down" when it starts to overheat, with careful monitoring and measurement of funds traveling through the financial network. Complex, customized financial instruments must be encoded using industry standards that allow automated review; the insurance industry, with its data standards for contracts, offers a model. Similarly, financial statements should be captured and disseminated using XBRL, an XML-based standard for financial reporting. Finally, as long as new products and services can be designed in a way that doesn't crash the system with the financial equivalent of a "blue screen of death," these innovations should be encouraged and welcomed into the system by industry insiders.

The industry's kludge-filled, error-prone, and unsafe financial engineering needs to be replaced with a more secure financial infrastructure that's been tested and debugged to the level of a major chip release. Regulatory oversight won't be simple, but it doesn't have to be. It just has to work, every single day and for every single transaction. That's the type of change with the potential to jump-start a global economy.

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Following are some real-world examples of how the industry can effectively transform itself through technology. First, we look at an example of how solving the risk management problem requires new sources of data, and the tools to turn that data into actionable information. Next, we examine how improved connectivity between banks and their corporate customers can improve safety and soundness by reducing liquidity risk. Finally, we examine best practices in the insurance industry for promoting the use of standards for complex financial instruments.




Data-Driven Decisions

More than 93% of banks require at least nine months to come up with new risk policies, and 64% need at least 12 to 18 months to change course, according to TowerGroup analyst Bobbie Britting. That's not fast enough in an economy where even someone with a high credit score and flawless payment history can enter bankruptcy at the drop of a pink slip.

Zoot Enterprises, a Bozeman, Mont., vendor of credit decision and loan origination technology tools, maintains that banks should be able to change course in about a week. Its tools let risk managers experiment with new data and new risk models.

Zoot is helping one of the top 10 U.S.-based banks implement a fast-moving process to let risk analysts include new information in their credit models. For instance, data about payments on cell phone accounts are "fantastically predictive," says Zoot marketing director Eric Lindeen. "If someone's five days late several months in a row, it's an indication that other accounts will also be bad soon."

Similarly, data about recent payments to utility companies, usage of payday loans, and various data from the public record also can provide clues as to a customer's changing financial status. Nevertheless, these sources haven't yet been incorporated into the most commonly used credit bureau scoring models. "They'll eventually incorporate it," Lindeen says, "but today there's useful data out there that you can't get from the bureaus."

This proliferation of risk-related data puts banks in the new position of having to negotiate access with multiple information providers. Then, once they have access to the data, risk analysts must develop their own hypotheses on what data contains predictive ability related to their own customers, test those hypotheses, and deploy their new risk models into the lending process.

Financial institutions that fail to incorporate this data into their risk models will have fewer options in the new economic climate. If stuck with a loan decision process from mid-2008, a lender may still not be able to discern between good risks and bad risks. Meantime, its best option may be to raise rates across the board just to be safe. This is especially true following passage of the Credit CARD Act of 2009, which prevents banks from raising interest rates on new accounts for 12 months. If you're really not sure if someone's a good risk, and you know you must maintain the person's interest rate for an entire year, the safest choice is to set the rate high.

However, a bank that knows how to differentiate risks will be able to offer lower interest rates to good risks, while letting competitors unwittingly take on the bad risks. That's the strategy of the top 10 bank working with Zoot. "They see the ability to build better, faster risk models as a competitive differentiator," Lindeen says.

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Pooling Information

As part of a smarter financial infrastructure, banks and their customers will need to communicate with one another in real time through a secure, standardized messaging platform. That's part of the value proposition of SWIFT (Society for Worldwide Interbank Financial Telecommunication), a member-owned cooperative based in La Hulpe, Belgium, that lets more than 8,830 banking organizations in more than 209 countries exchange standardized financial messages. SWIFT's dedicated IP network is completely separated from ordinary Internet traffic to ensure quality of service and information security.

During a credit crisis, cash is king. Because it has become harder to draw upon lines of credit from other banks, financial institutions have had to draw upon their own resources to meet funding obligations. To do so, both bankers and their corporate customers need to know exactly how much cash they have, worldwide.

This is harder than it sounds. "Just like a corporate treasury, the treasury function at a bank may be managed at each of its branches in different countries," says Wim Raymaekers, head of banking market at SWIFT. "Building a view of liquidity across these various accounts is not trivial."

In some cases it can take years for a bank to capture and translate all of the various data feeds from individual branches. "Branch by branch and system by system, they have to work on the mapping and conversion process for each separate format and procedure," Raymaekers explains.

Through SWIFT, far-flung branches can exchange precise details about their cash positions and sweep funds from one to another to meet intraday payment obligations. To take advantage of this information, banks deploy liquidity management software that helps them develop strategies for both short- and long-term funding requirements. Unlike many other applications run through a shared-service model, liquidity positions are kept close to the vest. "I'm not so sure they'd want this data hosted somewhere else," Raymaekers notes. "It might show them that within two weeks they may have a serious liquidity issue."

Similarly, corporate treasurers can use SWIFT to gather information about funds held at multiple banks. One example is T-Mobile, which had maintained 15 separate systems from subsidiaries in four countries. Now the subsidiaries use a single international finance portal, and the payments go out over SWIFT, Raymaekers says. "They've rationalized and centralized their treasury systems and increased connectivity into local banks," he says.

When corporate customers can connect to multiple banks, and when banks can connect to multiple branches across the world, the broader financial system gets smarter about the location and availability of funds. This structure reduces the risk that a company subsidiary or a bank branch will fail to meet its financial obligations when it could have been easily helped by a sibling entity. At the same time, banks and their corporate customers both benefit from the reduction in cost and operational risk that comes with decommissioning legacy systems.

The Vision The Challenges
  • Expand financial inclusion by making available low-cost, high-quality financial services to a broader population
  • Foster wealth creation via accurate assessment of risk, which leads to greater availability of credit, liquidity, and investment capital
  • Regain trust in the global financial system by instituting safeguards and controls to ensure capital adequacy, customer protection, and sound asset valuations
  • Identify, collect, and process new sources of data to support risk management
  • Standardize complex financial instruments for automated processing and oversight
  • Connect financial institutions and companies worldwide for greater capital liquidity and information exchange to facilitate trade and investment




Standardizing Complexity

One of the major challenges in financial industry reform will be to develop the means to capture data about complex financial instruments that don't fit into standard, exchange-traded formats. For example, as opposed to exchange-traded derivatives such as puts or calls on common stock, over-the-counter derivatives are negotiated between trading partners using telephone, fax, and e-mail, and they include idiosyncratic terms and conditions. For automated oversight and risk management to take hold in the securities industry, these types of contracts will have to be covered by standards.

For this effort, the securities industry would be well advised to look to the insurance business for guidance. "The insurance industry is probably one of the most difficult industries to automate," says John Kellington, senior VP of ACORD, an industry standards organization. "We sell a legal agreement that has to hold up in court 99.9999% of the time, and each policy that's sold is unlike the policy before it."

Whether from issues specific to cost or risk, insurance policies are highly customized, despite high volumes. As a result, the industry standards for capturing this data have been developed using data-rich formats that encompass a wide and extensible range of possible contract terms. These rich standards provide a good counterpoint to other areas of the financial services industry. "In the banking industry, the purchaser of a share of a commoditized mortgage product didn't really know what was in that derivative product," Kellington says. "They had a bunch of mortgages but didn't know the specifics of each mortgage, or whether it was high risk or not. The transparency wasn't there."

John Kellington, senior VP of ACORD
ACORD's Kellington: In the banking sector 'the transparency wasn't there.'
By contrast, when a reinsurance company provides supplementary coverage for a group of policies originated by another insurance company, detailed data about the underlying policies is always included along with the deal. "The reinsurer asks for information about each policy and can rely upon clear standards that describe the risks of each policy, how they were priced, and how limits were set," Kellington says.

Through its standards-making efforts, ACORD aims to capture the full spectrum of insurance contracts, including life, health, property and casualty, and commercial lines. The ACORD Framework includes a series of reference models that help insurers fit their policies into industry standards, thus enabling the safe and efficient sharing of risk between insurance providers.

Through stronger controls over data collection, improved networking among industry participants, and greater use of standards across a wider range of financial instruments, the future of the financial services industry can be assured in a way that enables a bright future for the rest of the economy. It's high time for the industry's circuits to get an upgrade.

Ivan Schneider has 10 years of experience writing on financial services technology matters, and was formerly executive editor of TechWeb's Bank Systems & Technology.

Illustration by Getty Images

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