Over the last few years, the financial industry has created several new and increasingly sophisticated products.

Derivative and securitization products, as well as options, have emerged as major tools for risk management, funding, and balance sheet management. Securitization is now a major funding source for credit card, auto, and other consumer businesses.

In addition, the industry has developed new means of better understanding the products and their markets. For instance, the concept of value-at-risk, or VAR, has emerged as a major new tool for measuring market risk.

Though these and other events are well known and widely discussed, developments now in their infancy will probably gain prominence.

While financial products are designed foremost to achieve economic objectives, the capabilities of these sophisticated products can be extended to accomplish broader strategic objectives as well.

As an example, the government used securitization to further the strategic objective of promoting homeownership. The Government National Mortgage Association, Federal National Mortgage Association, and Federal Home Loan Mortgage Corp. were the vehicles and securitization provided a mechanism for channeling funds in the desired direction - in this case from investors to homebuyers. Similarly, in a Harvard Business Review article, Peter Tufano illustrated how the Tennessee Valley Authority used call options to even out its electric power supply operations amid uncertain demand.

Over time, corporations may move to develop specialists who can keep abreast of strategic business issues and the capabilities of financial products to deal with those issues.

Efforts are already under way to apply value-at-risk to credit risk management, and it is conceivable that any quantifiable risk will eventually be measurable in those terms. While VAR is certainly not foolproof and does not necessary replace traditional measurement techniques, it offers the possibility of assessing market and other kinds of risks under a single measurement unit.

This may have a major influence on the ways businesses operate. For instance, value-at-risk might be used to compare risks of a market- sensitive product to that of a credit-sensitive product. Capital charges may be allocated to different business units with very different functions, on the basis of the VAR measure. Moreover, efforts to accurately determine the parameters for calculation of VAR will lead to better understanding of these markets.

But increasingly complex products mean equally or even more complex models for pricing, analyzing, or reporting. And with complexity comes the possibility of errors. The financial industry is already rife with stories of institutions taking losses due to errors in their models.

Indeed, the importance of model examination has already been recognized by the regulatory agencies and by the risk management groups of various institutions. While the primary emphasis so far has been on accuracy of calculations, the scope of the model examination will grow beyond computational aspects.

The process will determine the fit between the model and the business. For instance, a model used for analyzing an auto loan portfolio for a potential securitization may not necessarily fill the bill if used to analyze a lease portfolio. The results may not be interpreted or understood correctly. The erroneous decisions resulting from such misinterpretations can be costly to a business.

The finance industry is likely to see the emergence of a profession somewhat analogous to the profession of business analyst in the systems world. These analysts facilitate the information technology systems development process by translating the language of businesspeople into the language of systems analysts or computer programmers.

These professionals may play a significant role in product development, auditing, and examination efforts or just in the day-to-day management process. The qualitative translators will need not to be adept at mathematical techniques, but they will need to understand the business significance of quantitative concepts or quantitative implications of business concepts.

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