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Anomaly Detection

Written by Michelle Henley

Updated at September 16th, 2024

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            Table of Contents

            Default Scoring Criteria Unique Configuration Exclusions
            Description Identifies anomalous transactions according to the transaction's value relative to a historical period of related transactions (1 year). Related transactions are those of the same type (e.g., purchase order, non-purchase order, downpayment) and nature (e.g., fixed asset, inventory, gl account group).
            Domain(s) Vendor
            Analysis Type Context Flag
            Focus Area Value
            Score Methodology The 'z-score' (number of standard deviations from the mean) for the transaction relative to its peer group. By definition, 68% of transactions are within 1 standard deviation of the mean, while 95% are within 2 standard deviations, and finally 99.7% of transactions are within 3 standard deviations. It is recommended to avoid having a criteria with less than 2.

            Default Scoring Criteria

            Importance: 5 (default)
            Enabled: True (default)

            Risk Result Default Value Notation
            Weak 2.5

            The 'z-score' (number of standard deviations from the mean) for the transaction relative to its peer group

             

             

            Moderate 3
            Strong 4
            Auto Not Set

            Unique Configuration

            • The historical period for anomaly context is 365 days 
            • Configured to detect anomalies for items based on the combination of nature (e.g., "PO Related," "Non-PO Related," "Downpayment," or "Other") and nature detailed ("Fixed Asset," "Inventory," or the expense account group). 
              • Options are: Expense Type, Expense Type (Detailed), Company, Payee Country
            • All values are log-transformed, which allows us to mitigate the undesirable effects of a “long tail” distribution. This enhances the occurrence of our anomaly detection
               

            Exclusions

            • Items less than $100 cannot receive a risk result
            outlier detection abnormality identification

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