SMFG Capital Ratio Information (B) Quantifying Loss Frequency of Each Scenario a. Generation of “Average Frequency Table” for Domestic Business Offices To quantify loss frequency for domestic branches, we assume future loss frequency is similar to historical loss frequency. And we  generate an average frequency table, which is used to estimate future loss frequency. The average frequency table comprises rows  of total risk score and columns of total control score and the number of loss occurrences in a one-year period for each combination  of scores is given.  As risk and control assessment items are expected to have different loss occurrence contribution ratios, we analyze their loss  occurrence contribution ratios for each assessment item by executing a regression analysis and weight each assessment item.  Total risk assessment score and total control assessment score are calculated for each scenario taking into account the weight of  each assessment item described above. Then, the loss frequency of each scenario (the number of times the loss event described in  the scenario occurs during a one-year period) is estimated using the average frequency table. In order to estimate the probability of occurrence in terms of four loss amounts (¥100 million, ¥1 billion, ¥5 billion, and ¥10  billion) for each scenario, we use a log-normal distribution function for each scenario. In order to quantify the loss amount for each scenario, we generate loss distribution for each “business process & product” by using  the historical transaction data of SMBC. Specifically, we assume that the historical transaction volume follows a log-normal  distribution (distribution in which the logarithm of a variable follows the normal distribution) for each “business process & product”  and generate the loss-severity distribution.  Because we assume the log-normal distribution to each “business process & product,” in case one loss event occurs in a one-year  period, potential loss can be regarded as likewise arising from log-normal distribution. Therefore, in this case, we estimate the  probability of occurrence of four loss amounts by substituting each loss amount for the loss amount of log-normal distribution. In case that one loss event occurs in a one-year period, the method described above is followed. However, in case that several  numbers of loss events occur in a one-year period, it is conceivable that the events occurred independently of each other. Therefore,  the probability of occurrence of several loss events can be calculated by the probability of one loss event raised to the power of its loss  frequency. As we quantify the loss frequency for each scenario using the average frequency table for loss events over a one-year period, we  are able to estimate the probability of four loss amounts by the probability arising from the above log-normal distribution function,  raised to the power of loss frequency derived from the frequency table. After estimating the loss frequency in terms of the four loss amounts for each scenario, we sum results for each loss event type  and input them into the quantification model for SMFG (consolidated), SMBC (consolidated), and SMBC (nonconsolidated). SMFG  2011 203 (D) Estimating the Frequency of Occurrence of the “Low-Frequency and High-Severity” Events (C) Quantifying Loss Amount for Each Scenario b. Quantifying Loss Frequency of Each Scenario 2.0 2.4 2.8 3.2 3.6 4.0 5.5 4.5 5.5 3.5 4.5 2.5 3.5 2.40 1.5 2.5 0.5 1.5 0.5 Total Score Control Risk Average Frequency Table  (Example) (Times/Year)