This paper reports the results of a general study conducted in cooperation with the Dutch ING Group to identify various fields of interaction between a financial institution and a technical university. A number of projects have been examined in terms of, amongst others, the length of the project and the possibility and term of measuring first results, in order to define the project(s) most suitable for an initial cooperation. It is found that in banking and insurance many possible fields of interaction with technology are available. Projects in these fields have a lot of potential for success, though not always fit for cooperation with technical universities, due to prioritization of issues or to a lack of of readiness for researching the possibilities of new techniques. This paper can give financial institutions as well as technical research institutes an insight into some of the possibilities of collective research.

Discusses some essential requirements for the introduction of computational intelligence techniques in the field of financial services, and reports on an investigation carried out concerning the possibilities and expected success of using fuzzy systems in some business chapters of the Dutch ING group. Based on this investigation, the subject of direct marketing is chosen for a pilot study of the application of data-driven modeling techniques using fuzzy clustering and iterative gain-charts refinement. This is compared with the present practice using statistical tools.

In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems.

A control strategy is presented for robots that do not have accurately known mechanical structures or have inaccuracies caused by bending, slip, or backlash. In the system, the manipulator's endpoint is monitored in a servo loop, so that inaccuracies in the structure can be compensated. An adaptive transformation from task to robot-oriented coordinates has been used online, without prior modeling or calculation. The strategy developed has been simulated for a two-degree-of-freedom robot. Results are compared with those obtained using the inverse Jacobian as part of the control system.>