Data Mining for Effective Risk Analysis in a Bank Intelligence Scenario.

We propose a data warehousing architecture for effective risk analysis in a banking scenario. The core of the archi- tecture consists in two data mining tools for improving the quality of consolidated data during the acquisition process. Specifically, we deal with schema reconciliation, i.e. seg- mentation of a string sequence according to fixed attribute schema. To this purpose we present the RecBoost method- ology which pursuits effective reconciliation via multiple stages of classification. In addition, we propose a hash- based technique for data reconciliation, i.e. the recognition of apparently different records that, as a matter of fact, refer to the same real-world entity.