SMFG Capital Ratio Information (B) Estimation of Loss Frequency Distribution (C) Risk Capital Conversion Factor  γ (D) Calculation of Required Capital B. Verification of the Quantification Model (A) Verification of Quantification Accuracy The Poisson distribution (probability distribution often used in estimating the number of occurrences of rare events) is used for  generating the loss frequency distribution. To estimate the Poisson distribution, it is necessary to estimate the average number of  annual losses, but in this model, we do not simply take the annual average of all cases of losses for the entire period (several fiscal  years) but instead, estimate the annual average number of loss cases for each fiscal year individually. Through this approach, we are  able to take account of the deviations in the historical incidence of losses for different periods and are able to estimate loss cases that  may occur in the future more appropriately. We calculate 99.0% VaR from the estimated aggregated loss distribution, and then multiply the risk capital conversion factor γ  (gamma) in order to compute 99.9% VaR. By introducing  γ  it is unnecessary to estimate 99.9% VaR directly which can be  estimated with lower accuracy, and it provides with stable estimation results by estimating 99.0% VaR which can be estimated with  higher accuracy. The factor γ  means the ratio between 99.9% VaR and 99.0% VaR. In other words, it is the risk profile of the loss distribution  and an indicator for the characteristics of the tail part of the distribution. The risk profile of the loss distribution is different for each  loss event type, by which the calculation is performed. In addition, we have verified statistically that it could differ among SMFG  (consolidated), SMBC (consolidated), and SMBC (nonconsolidated). To reflect their characteristics, we set a different value of  γ  for  each entity. There is a tendency for  γ  to become smaller, etc., when there is a distribution of large expected losses or when the tail of  the distribution is highly dense. When setting γ  initially, we conduct an analysis, taking into account the possibility of changes in the risk profiles of many types  of loss distributions, and set values that maintain the stability and the conservativeness of capital. In addition, we assess changes  in the risk profiles of the most recent loss distributions, including the present one, and, when changes are above a certain level,  we conduct a review of the  γ  values. This makes it possible to keep values of  γ  appropriate to changes in the risk profile of the loss  distribution and calculate stable values of required capital. We calculate required capital by multiplying the 99.9% VaR calculated in the previous section by the multiplier for each loss  event type that has been determined based on the number of breaches in back testing. As will be mentioned later, back testing  is conducted periodically, and, when realized risk is found to be greater than the risks estimated with the quantification model (back  testing excess), we take necessary steps, such as multiplying by the multiplier determined through prior analysis, to maintain the  conservativeness of required capital estimates. We then add the required capital amounts calculated for each loss event type to compute the required capital for SMFG  (consolidated), SMBC (consolidated), and SMBC (nonconsolidated). Please note that in calculating required capital, we do not subtract expected losses. We conduct a range of sensitivity and verification tests to ensure that the measurement results of the quantification model are  appropriate (quantification accuracy) and to confirm that our model is capable of measuring the amounts corresponding to the  maximum losses from operational risk that may be incurred for a one year holding period, with a one-sided 99.9 percentile confidence  interval. In the following paragraphs, we would like to explain the methods for assessing the quantification accuracy of our  measurements and the framework we have in place for regular verifications. We have confirmed the reliability of the quantification model through a verification process from various perspectives. Specifically,  we obtain a quantitative grasp of the possibilities for variation in measurement results that may arise from preconditions or  assumptions made at the time the models were designed. In particular, we assess the possibilities for underestimating required  capital and the possible magnitude of such underestimates. Then, in our periodic verification framework, which is described below,  we make analyses of how to compensate for such underestimates. We apply our understanding of the possibilities for  underestimation to the multiplier derived from back testing, and, if the accuracy of the quantification model deteriorates, we  introduce a framework for making adjustments in the multiplier to avoid underestimating the amount of required capital. SMFG  2011 207