我國金融市場近年競爭激烈，部分金融機構發生經營不善問題，為保障存款戶與債權人權益，並建立健全金融環境，監理機關須對該等機構採取必要介入措施。立即糾正措施（Prompt Corrective Action, PCA）即為金融機構財務及業務惡化導致對存款人與債權人權益造成有損害之虞時，主管機關得介入干預之機制。此機制內容由國際銀行監理組織諮詢文件與美國、加拿大與日本立法例可知，乃監理機關以金融檢查確認金融機構之資本構成、經營與風險管理。若無法達成監理要求，則可對金融機構、管理階層與股東依不同程度之缺失進行正式行政處分、行政契約或非正式之行政指導以達監理目標。 回顧我國解決問題金融機構之歷史沿革，發現法制中本有立即糾正措施之建制。然而相關法規命令因明確性問題，造成監理機關無法運用之結果。2008年銀行法修正，即為解決此問題，並建立明確標準而與國際監理潮流接軌。綜觀此次修法尚應釐清者乃各種監理處分之基準與程序要求，並應考慮監理機關與金融機構與行政契約方式改善資本適足性之行政契約選項。此外行政指導之非正式糾正處分亦可作為監理工具。 本論文對立即糾正措施之發動，以行政法程序法中最低程序保障學理為出發，區別金融檢查、行政處分、行政契約與行政指導之不同行政行為特性，分別建議監理機關進行相關措施時之預告、相對人陳述或聽證與說明理由義務等程序重點。期使能成為我國監理機關發動立即糾正措施時之參考。

農漁會組織長久以來於農業發展扮演重要角色，其促進農業生產，增進農民福祉與繁榮農村經濟，對台灣早期之經濟發展貢獻許多。隨著經濟結構的變動，農會信用部面對產業結構的轉變及其他金融機構的激烈競爭，致使其經營陷入困境，經過金融重建基金處理了36家經營不善信用部，宣佈分級管理措施，及12萬農民大遊行，政府為徹底解決信用部諸多問題，於93年1月實施農業金融法，建立由行政院農業委員會一元化管理之農業金融體系，經過一連串之改革，相關財務指標顯示信用部之經營已逐漸改善中。 本研究主要探討我國農業金融體制與信用部面臨的問題，並參考日本農業金融改革之經驗，提出健全我國農業金融體制與監理之建議。 研究發現，農業金融改革後信用部之經營確實在改善中。然而，在80年代農業金融危機下遺留的問題尚未完全克服。對於我國農業金融發展，本研究從組織面、業務面與監理面進行探討，提出改革建議包括：全面檢討修訂農會法與漁會法、儘速恢復股金制、建置合併法規鼓勵合併、儘速處理經營不善之信用部並建構多元退場機制、加強農業金融體系連結與加速資訊共用平台之整合、強化對全國農業金庫與信用部之監理、落實金融監理加強實地檢查與場外監控措施、導入差異化管理與立即糾正措施等，農業金融機構有必要繼續改革，以健全農業金融體系，保障存款人權益，促進農漁村經濟發展。 / The Farmers’ and Fishermen’s Associations played an important role in the field of agricultural production. They helped agriculture develop, increased farmers’ and fishermen’s welfare, flourished the countryside and contributed a lot to the early progress of Taiwan economy. With the transition of economic structure, the whole environment became quite disadvantageous to credit departments of farmers’ and fishermens’ associations, which face the changes of the industrial structure and fierce competition from other financial institutions. With the experiences of the settlement of 36 problem credit departments by the Financial Restructuring Fund, announcement of differential supervision scheme and demonstration of 120,000 agriculturists, the government implemented the Agricultural Finance Act on 30th January 2004 and built an integrated agricultural finance system governed by the Council of Agriculture (COA) to solve many problems of credit departments. Through those reformations, the financial index showed that the condition of these credit departments has improved gradually. This study aimed to discuss those difficulties that our agricultural finance system and credit departments encountered and bring up suggestions to complete this system and the government’s supervision referring to the reformation of agricultural finance system in Japan. What our study found is that the operation of credit departments has undoubtedly improved after taking reformations to agricultural finance system in Taiwan. However, problems that the agricultural finance crisis left behind in 1980s have not been completely conquered yet. As to the prospect of our agricultural finance system, from the aspects of organizational structures, business activities and government’s supervision, we suggest the reforms include to examine and amend both the Farmers’ Association Law and the Fishermen’s Association Law from stem to stern, re-enforce paid-in capital system with all speed, draw up laws to encourage mergers, deal with problem credit departments and build up plenty selections to help them exit as soon as possible, strengthen the connection of agricultural finance system and the integration of information sharing stations, intensify our supervision towards the Agricultural Bank of Taiwan and credit departments, reinforce on-the-spot examination and off-site monitoring, bring differential supervision scheme and prompt-corrective action into practice and so on. Hence, we may achieve the goal to complete the agricultural finance system, protect the rights of depositors and prosper the rural villages’ and fishing villages’ economy.

本研究分析我國49家本國銀行、55家信用合作社、287家農會信用部及27家漁會信用部等四類金融機構之經營現況，並參照各國金融預警制度運作方式，選取適合的財務比率，運用SAS統計軟體及Z-score、Logistic等模型，分別找出造成各類金融機構經營失敗之顯著相關財務比率，評估各類金融機構之經營效率、失敗機率與模型之正確區別率，以建立預測金融機構失敗機率之預警模型。研究之樣本資料分別為：本國銀行49家、2001年第2季~2003年底共計11季25項財務比率，信用合作社55家、1998年底~2003年底共計21季26項財務比率，農會信用部287家1998年底~2003年底共計21季25項財務比率，漁會信用部27家1998年底~2003年底共計21季25項財務比率。 本研究之結論為： 一、彙整Z-Score模型對各類金融機構具有顯著性之財務變數，本國銀行有6項、信用合作社有7項、農會信用部有6項，漁會信用部有4項。 二、彙整Logistic模型對各類金融機構具有顯著性之財務變數，本國銀行、信用合作社各有6項，農會信用部有5項，漁會信用部有4項。 三、金融預警模型中，Logistic模型較Z-Score模型有較高的正確區別率。 / This research analyzes 49 domestic banks, 55 credit cooperative unions, 287 credit department of farmer associations and 27 credit department of fisherman associations above four kind of financial institution´s management situation, and refers the operation ways of various countries financial early warning system, selects suitable financial ratios , utilizes SAS statistics software and Z-score, Logistic models, it identifies the root cause of bankruptcy thus reveals finance of ratio the correlation, appraises management efficiency, the defeat probability each kind of financial institution if the correct difference rate. It appraises each kind of financial institution´s management efficiency, defeats probability and correct difference rate. It establishes early warning model that forecasts financial institutions failure rate. The research model and period: used 49 domestic banks from 2001 in 2nd season to the end of 2003 total 11 seasons and 25 items of finance ratio、55 credit cooperative associations from the end of 1998 to the end of 2003 total 21 seasons and 26 items of finance ratio、287 credit department of farmer associations and 27 credit department of fisherman associations from the end of 1998 to the end of 2003 total 21 seasons which used respectively 25 items of finance ratio. The conclusion of this research are: Firstly, it collects the entire Z-Score model to have significant financial indicator to each kind of financial institution, the domestic banks have 6 items, the credit cooperative associations have 7 items, the credit department of farmer associations have 6 items, and the credit department of fisherman associations have 4 items. Secondly, it collects the entire Logistic model to have significant financial indicator to each kind of financial institution, the domestic banks and the credit cooperative associations have 6 items respectively, the credit department of farmer associations have 5 items, and the credit department of fisherman associations have 4 items. Thirdly, in the financial early warning model, when comparing Z-Score with Logistic model , the latter appears to have a higher correct difference rate.