Abstract: A technique for detecting anomalous values in a small set of financial metrics makes use of context data that is determined based upon the characteristics of the target company being evaluated. Context data is selected to represent the historical values of the financial metric for the target company or the simultaneous performance of peer companies. Using the context data, an anomaly score for the financial metric is calculated representing the degree to which the value of the financial metric is an outlier among the context data. This can be done using an exceptional statistical technique. The anomaly score can be used to evaluate the risks associated with business transactions related to the target company.

Abstract: A method of identifying a set of entities based on a pattern of interest is provided. The method includes identifying a reference entity and identifying one or more alert categories indicative of a pattern of interest in the reference entity over a time period of interest. The method further comprises determining a matching percentage of the pattern of interest exhibited by the reference entity, in one or more entities comprising the set of entities based on the one or more alert categories. The method further comprises identifying one or more of the entities comprising the set of entities that exhibit one or more of the patterns of interest exhibited by the reference entity, based on the matching percentage.

Abstract: A method for constructing data patterns of interest is provided. The method includes creating one or more alert clause expressions and evaluating the one or more alert clause expressions based on a parameter of interest and a plurality of conditions. The method further includes combining the one or more alert clause expressions in a selected manner to generate an alert signal. The one or more data patterns of interest are described by the one or more alert clause expressions and the alert signal.