Abstract: A method for determining the influence of attributes on a predicted outcome for a customer is described. The method includes computing for each dimension a score based on multiple applications of a non-linear statistical model on different combinations of other pieces of data with attribute values for the customer in that dimension, wherein each attribute was categorized into one of the plurality of dimensions based on a common characteristic for that dimension, wherein each dimension in the plurality of dimensions includes two or more of the attributes and attributes in each dimension share the common characteristic for that dimension, wherein each of the other pieces of data for each of the plurality of dimensions are attribute values independent of the dimension and the customer, wherein the first score for the customer for each of the plurality of dimensions indicates the influence of that dimension on the predicted outcome.

Abstract: Financial data including general ledger activity and underlying journal entries are examined to determine whether risks of material misstatement due to fraudulent financial reporting can be identified. The financial data is analyzed statistically and modeled over time, comparing actual data values with predicted data values to identify anomalies in the financial data. The anomalous financial data is then analyzed using clustering algorithms to identify common characteristics of the various transactions underlying the anomalies. The common characteristics are then compared with characteristics derived from data known to derive from fraudulent activity, and the common characteristics are reported, along with a weight or probability that the anomaly associated with the common characteristic is an identification of risks of material misstatement due to fraud.

Abstract: Financial data including general ledger balances and underlying journal entries are examined to determine whether risks of material misstatement due to fraudulent financial reporting can be identified. The financial data is analyzed statistically and modeled over time, comparing actual data values with predicted data values to identify anomalies in the financial data. The anomalous financial data is then analyzed using clustering algorithms to identify common characteristics of the various transactions underlying the anomalies. The common characteristics are then compared with characteristics derived from data known to derive from fraudulent activity, and the common characteristics are reported, along with a weight or probability that the anomaly associated with the common characteristic is an identification of risks of material misstatement due to fraud.