on the other hand, we leave open the possibility that elements of the framework are subject to change. this is not a trivial matter. as a former banker, i sympathize with the challenges you face in deciding on investments and upgrades to your systems and personnel. when it comes to basel ii, we recognize that certain details relating to systems and processes will depend on what the final u. s. rule and guidance contain. accordingly, your primary supervisor is available to discuss your implementation efforts and we want to hear specifics about which elements of the proposal, from your perspective, will demand the greatest investments or appear to generate the greatest uncertainty. using that information, the agencies can then understand where to target resources to assist banks during the transition to basel ii. we certainly hope that many upgrades made for basel ii are those that would have been made anyway to strengthen enterprise - risk management. an additional challenge that you face in implementing an ama is that operational risk has only relatively recently risen to management and regulatory attention - not because it was previously unimportant, but because it has been so difficult to measure. as qis4 has demonstrated, we are still in the early stages of measuring this risk, and the challenges in gathering the relevant data and developing the models are still great. the main reason for the lack of data, as i am sure many of you know firsthand, is simply the fact that most banks have only recently started systematically collecting operational - loss data. while the importance of models and loss data in quantifying operational risk may be quite apparent at least to practitioners of the art - the importance of the qualitative aspects may be less so. in practice, though, these qualitative aspects are no less important to the successful operation of a business - as events continue to demonstrate. some qualitative factors, such as experience and judgment, affect what one does with model results. it is important that we not let models make the decisions, that we keep in mind that they are just tools, because in many cases it is management experience - aided by models to be sure - that helps to limit losses. some qualitative factors affect what the models and risk measures say, including the parameters and modeling assumptions used and the choices that relate to the characteristics of the underlying data and the historical period from which the data are drawn. i think you will hear a common theme today and tomorrow : value is added to the firm when operational - risk measurement is integrated with the business -

risk and performance are flawed. in understanding the drivers of good risk management, qualitative factors are a critical influence on the reliability and characteristics of the “ data ” used to evaluate risk and performance. in this broader sense, “ data integrity ” can refer not only to the consistency, accuracy and appropriateness of the information in the data base and model, but also to the processes that produce and utilize these measures. used this way, “ data integrity ” includes the quality of credit files, tracking of key customer characteristics, internal processes and controls, and even the training that supports them all. when one says “ data integrity ” in risk - management circles these days, most people think of the qualifying standards for the internal - ratings - based approaches to credit risk capital under basel ii. i think it is a broader concept, so let me spend a moment on that subject. the proposed timetable for us implementation of basel ii reflects the requirements of our rulemaking processes and the need for banks and supervisors to prepare for the introduction of the new standards. as you probably know, the us banking agencies envision formal release of the proposed regulations in mid - 2005, with parallel running of basel i and basel ii in 2007 and full implementation in 2008. even on that timetable, the regulatory community recognizes that substantial data limitations may prevent banks from developing viable and robust parameter estimates in the near term - even for probability of default in some cases. for this reason, both banks and their supervisors will have to wait while data accumulate before banks can estimate and validate parameter inputs in a reliable, robust manner. in the interim, banks and supervisors will have to rely heavily on qualitative validation approaches although not entirely. supervisors across countries are working together to address validation issues, and, i believe, will develop useful guidelines for banks and supervisors alike. in the early years, more weight may need to be placed on qualitative reviews of a bank ’ s internal policies and procedures, including its internal validation and documentation. but we expect that, soon after implementation, banks should have the ability to generate the needed parameters from actual data, and supervisors will want to see positive steps being taken by banking organizations to develop good databases to provide the sort of data integrity i am discussing. qualitative and quantitative benchmarking studies, which compare methodologies and parameter estimates across banks, will be important tools for validation and for encouraging the diffusion of best practices throughout the industry during both the initial, more qualitative and the later more quantitative