傳統的資產負債管理(Asset-Liability Management，ALM)研究大多強調數量分析方法，並未考慮資料來源的問題。然而在銀行實務上，資產負債管理人員卻必須根據現有內外部資料來釐定資產負債組合的整体政策。在決策支援系統中，模式整合的功能包含模式之組合及連結等，可用以整合數量分析模式與相關資料。本研究運用人工智慧技術來探討資產負債管理中模式整合之問題。藉此可以明瞭ALM的分析流程，以作為銀行人員訓練之參考。另一方面由於應用黑板架構發展系統，也可以提供一個有彈性的整合環境，以反應使用者需求及資料異動狀況，亦可彈性新增、刪除及修改模式整合過程中的資料結構與知識內涵，以為未來連接理論技巧與實務環境之參考。

　　The computer support for Asset Liability Management (ALM) in the literature emphasizes on the mathematical analysis and does not address the data source problems. In the practical banking environment, however, ALM decisions are made based on the dynamic internal and external data changes. Therefore, an ideal ALM decision support system has to consider the integration of data sources and mathematical analysis. Traditional Decision Support Systems (DSS) rely on the expert's assistance to understand the problem and formulate or integrate appropriate models. There is a growing recognition that incorporates Artificial Intelligence techniques (Al) into the DSS can enhance the acceptance of these decision aids by management.

　　This paper intends to develop an Intelligent Decision Support System (TDSS) and addresses the model integration concept for the ALM. In the paper, model integration is defined as a series of processes from which important decision making information is inferred through automatic data model mapping and mathematical model conversion. The investigation of model integration concept helps the ALM analysis process understanding which can be useful for baaldng personnel training. On the other hand, the IDSS provides a flexible integration environment in which the system can flexibly response to the user's analysis request with the updated data situations. Since the blackboard architecture used for the system development supports the modularization structure, its inherent maintainability aLows a flexible update of the domain knowledge and data structure, and can therefore serve as a testbed to evaluate the potential integration approaches of various ALM data and mathematical models.