| Summary: | 博士 === 國立中興大學 === 農業經濟學系 === 90 === The credit departments of Framers’ Associations have been playing a very important role in the agricultural finance system in Taiwan. Currently, some credit departments are in serious financial problems, meanwhile, without efficient and effective financial inspections and operational assistance under financial investigation mechanism. As a result, a more effective financial forecasting model should be built for the reduction of operational crisis of credit departments. The above unsound situation was resulted from the imprecise fuzzy phenomenon on evaluating the operational performance of credit departments such as the scale of operational risks, the level of capital adequacy, the degree of financial crisis, etc. Mathematical parameters are not able to accurately explore these fuzzy phenomenon. At the same time, past researchers used Neural Network System which adopted only hard dichotomous classification method to create certain degree of forecast errors. This research adopted Self-Constructing Neural Fuzzy Inference Network System to forecast different A to E downward ranks of financial crisis for credit departments. The parameters of CAMELS are used as inputs and rank variables calculated from Factor Analysis are as outputs. Totally, 268 credit department samples during 1998 and 2000 are analyzed with excellent forecasted output results. The results showed that the liquidated 22 credit departments in the September of 2001 under government enforcement had 18 and 21 credit departments as D and E ranks in 1998 and 1999, respectively. If this research forecasted of financial crisis performed in 1998, then 18 and 4 out of the 22 liquidated credit departments would have been dealt with proper supervision mechanism three and two years ago, respectively. The solvency crisis and enforced consolidation would not happen.
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