Huge financial databases may contain terabytes of data and have truly industrial dimensions. Since the quality of conclusions drawn using the data depends primarily on the information quality, the data has to be to monitored using appropriate methods. This paper discusses statistical model-based methods, including process monitoring approaches, applied with respect to the aggregated data, as well as data-mining methods, operating at the level of the raw data. Both approaches utilize data redundancy to build statistical models.