into routine business operations. they all incorporate past data about borrowers to predict and rank potential borrowers by the risk of default. these technologies have sharply reduced the cost of credit evaluation and improved the consistency, speed, and accuracy of credit decisions. credit - scoring technologies have served as the foundation for the development of our national markets for consumer and mortgage credit, allowing lenders to build highly diversified loan portfolios that substantially mitigate credit risk. their use also has expanded well beyond their original purpose of assessing credit risk. today they are used for assessing the risk - adjusted profitability of account relationships, for establishing the initial and ongoing credit limits available to borrowers, and for assisting in a range of activities in loan servicing, including fraud detection, delinquency intervention, and loss mitigation. these diverse applications have played a major role in promoting the efficiency and expanding the scope of our credit - delivery systems and allowing lenders to broaden the populations they are willing and able to serve profitably. the use of credit - scoring models, whether turnkey models purchased from providers or proprietary models developed in house, has taught bankers - - sometimes through costly experience - - the value of continually updating the database on which the model operates. indeed, one can speculate that some of the problems this year in subprime credit card losses may well represent an insufficiently long data series to score successfully such credits during a recession. the experience with credit - scoring models underlines the necessity of basing more - sophisticated quantitative approaches, approaches that seem to have served the banking system so well when applied initially, on a longer and larger database of loss experience. let me conclude by noting an often overlooked fact. the use of the more - sophisticated techniques i spoke of earlier, especially the various forms of derivatives, are, by construction, highly leveraged. they are thus prone to induce speculative excesses, not only in the u. s. financial system, but also through out the rest of the world. the greater potential for systemic risk can be contained by improvements in effective risk management in the private sector, including market discipline based on better public disclosure, and by improvements in bank supervision and regulation in the public sector. to be sure, as i have noted elsewhere, there is some level of risk that must be absorbed, as a last resort, by central banks if an economy is to obtain the full resource allocation benefits of financial intermediation. the supervisors of the industrial world have been working together for

changing risks and respond effectively to them. in this respect, developments in 1998 were key in alerting u. s. banks to mounting risk. after three or four years of economic expansion, loan growth, and rising profits, the asian crisis and the russian default sent a strong and timely message to u. s. banks to raise their credit standards and more actively manage their existing portfolios to limit risk exposures. i am aware of no other time when banks began so much before the cyclical peak to be highly sensitive to potential risks in either new or existing credits. that experience is indicative, i believe, of how better risk - management techniques can infuse the decisionmaking process with increased discipline and can focus attention on the need to balance risk and reward. we can have little doubt that we have seen a new response mechanism that has contributed to the health of the banking system, one that i trust will be more than transitory. to be sure, there were, and still are, substantial problems. large losses have been taken, and more are yet to be recognized. no risk - management system will ever be flawless, and i emphasize that banks have just begun the process of applying the new quantification techniques. indeed, quantification techniques require quantities - - numbers - - to work. the most recent credit cycle has created an abundant supply of exactly the kind of critical information that banks will need to improve their risk - management : information about default rates and the associated losses by borrower and loan type. now is the time to collect and maintain these default and loss data in a disciplined and uniform fashion. most banks missed that opportunity in the early 1990s, and some are going back at great cost to mine these data today. a decade ago, one might have been excused from undertaking such data collection efforts because of the technology then existing and the cost of data storage. these reasons are no longer justified. further, the collection of data on defaulting credits, both from past cycles and on a continuing basis, is required to link internal default and loss estimates with the minimum regulatory requirements under the new basel capital accord now being developed for the large internationally active banks. banks of all sizes are familiar with the importance of data in the quantification of risk - management tools. the simplest of these techniques, credit - scoring models, have been in wide use over the past ten to fifteen years by lenders, insurers of loans, and participants in secondary markets. the technologies have been integrated