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The unlimited growth of supply chain finance data will inevitably lead to a situation in which it is increasingly difficult to access the desired information. Supply chain finance is often necessary to analyze large data sets, maintained over geographically bank, supplier and customer distributed sites by using cooperative context-aware distributed data mining (DDM) systems. The study of the existing approaches shows that no single solution fulfills all requirements identified for the cooperative context-aware DDM systems. One of the basic obstacles is the lack of context-aware and supporting some of the computational resources - such as data and information bases, computational models, compute power to execute these models, specialized data mining algorithms - required to develop a new compound is not available locally, but accessible via the global computing network infrastructure. This paper proposes a multi-agent-based architecture for supply chain finance cooperative distributed data mining systems. The use of multi-agent-systems (MAS) creates a framework which allows the inter-operation of a vast set of heterogeneous solutions to carry out the complex supply chain finance context-aware distributed data mining tasks, many data mining tasks to connect heterogeneous resources, as data sources, processing nodes and end user applications. It considers a data warehousing that supports context-aware OLAP queries, ensuring the interoperability of all data sources, and then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. Finally, we outline the implementation of a prototype for shoes manufacturing.