Unusual Patterns via Benford's Law
Table of Contents
Use Case:
Customer revenue transactions are a critical area for monitoring business activity and ensuring compliance with financial policies. The Unusual Patterns via Benford's Law risk analytic applies Benford's Law, a widely accepted statistical principle frequently used in forensic accounting and fraud detection. Benford's Law predicts that in naturally occurring datasets, lower digits (such as "1") are more likely to appear as the first digit than higher digits (such as "9"). This pattern holds true across various financial datasets, making it a powerful tool for identifying anomalies.
If customer transactions consistently deviate from this expected distribution—such as showing an unusually high frequency of amounts starting with "9"—it could indicate manipulation, like inflating or deflating transaction values. The Unusual Patterns via Benford's Law analytic flags this deviation from the expected distribution of first digits. Upon review, the compliance team may identify potential issues such as improper payments, manipulated sales values, or attempts to avoid approval thresholds, helping prevent further financial or compliance risk.
Benford's Law is a trusted method in financial analysis and is commonly used by auditors, regulators, and forensic analysts to detect fraud, errors, or manipulation in datasets, including sales transactions, financial statements, and transaction logs. This analytic allows users to choose between analyzing the first or the first two significant digits of transaction values, offering flexibility and greater precision in detecting deviations from the expected distribution. By leveraging Benford’s Law, organizations can uncover potential fraud or control circumvention in customer revenue data, ensuring the integrity of financial transactions and compliance with policies.
| Description | Identifies transactions linked to an entity that has a history of transactions with a measure that does not conform to Benford’s Law (also known as the law of anomalous numbers), which is a probability distribution for the first significant digits found valid for many real-life sets of numerical data, including financial transactions. |
| Domain(s) | Customer Revenue |
| Analysis Type | Indicator |
| Focus Area | Statistical |
| Score Methodology | Number of match occurrences, within the configured historical period of data, for the configured entity with non-conforming leading digits. |
Default Scoring Criteria
Importance: 3 (default)
Enabled: True (default)
No rules are configured by default. Rules must be set by user.
Rule Configuration
To configure a rule for this risk analysis:
1. From the Admin page, navigate to Risk Settings > Risk Analysis Settings > Unusual Patterns via Benford's Law (Customer Domain). Then, click Add A Rule.
2. Complete the following required rule fields:
| Rule Field | Description |
|---|---|
| Name | Name for the specific rule |
| Description | Description of the rule |
| Measure | Transaction Amount, USD Transaction Amount, Transaction Amount (Currency Cohort), Transaction Amount USD (Company Cohort), Transaction Amount (Company Country Cohort) |
| First Significant Digits | First Digit, or First Two Digits 1 to 365 Days |
| Context Period in Days | 1 to 365 Days |
| Entity | Subject |
| Confidence Interval | 90%, 95%, 99% |
| Minimum Transactions to Preform | 5,000 - 500,000 |
3. Click Add A Rule Component.
4. To set a component, select a Field and Search Type from the dropdowns.
The Value field will appear after you select a Field and Search Type.
See a description of Field, Search Type and Value below:
| Field | Search Type | Value Type |
|---|---|---|
| Company | Includes OR Excludes | List is populated from customer data* |
| Customer (Payer) | Includes OR Excludes | List is populated from customer data* |
| Customer Billing Line Item Amount USD |
Greater Than or Equal To OR Less Than or Equal To |
Dollar Amount |
| Customer Billing Line Cost USD |
Greater Than or Equal To OR Less Than or Equal To |
Dollar Amount |
| Customer Billing Line Item Text | Contains OR Does Not Contain | Free text |
| Has Free of Charge Item | Includes OR Excludes | Yes/No |
| Is Return | Includes OR Excludes | Yes/No |
| Material Type | Includes OR Excludes | List is populated from customer data* |
| Organization Unit | Includes OR Excludes | List is populated from customer data* |
| Original Transaction Amount USD |
Greater Than or Equal To OR Less Than or Equal To |
Dollar Amount |
| Subject (Payer Country) | Includes OR Excludes | Preset List |
| Subject Account Group | Includes OR Excludes | List is populated from customer data* |
| Subject Classification | Includes OR Excludes | List is populated from customer data* |
| Transaction Reason | Includes OR Excludes | List is populated from customer data* |
| Transaction Type | Includes OR Excludes | Preset List |
| Transaction Type (Detailed) | Includes OR Excludes | List is populated from customer data* |
*For values populated from customer data, value lists will not populate if data is not available.
Note: Where multiple rule components are set, they operate only in an “and” manner.
5. Click the Save Changes button at the bottom of the page to save your rule.
Unique Configuration
This analysis utilizes rules configured by the user, for which they can configure the following:
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Measure: the measure used to identify and parse the first significant digits after rule components are applied. Options are:
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First Significant Digits: the number of first significant digits to extract and compare to the expected probability according to Benford’s Law. Options:
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| Context Period in Days: the period of time, from the impact date, for which transactions are considered by the rule. Choose a context period from 180 to 1440 days (default is 365 days). |
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Entity: the entity for which to sum the occurrences of non-conforming transactions over the context period within the rule population. Options are:
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Confidence Interval: used to calculate a range around the expected frequency for the first significant digits. The higher the confidence interval, the tighter the range around the expected frequency. Options:
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Minimum Transactions to Perform: a value that serves as the minimum number of transactions returned by the rule components to perform the Benford’s Law check (rules that are too narrow and return fewer transactions are ignored). Options:
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Rule Components: specific restrictions by field to limit the transactions that are considered by the rule.
* Not available for financially sourced documents. |
| Scoring Criteria based on the number of occurrences found for non-conforming first significant digits (per entity configured) within the historical period of time for the rule components (Strong, Moderate, and Weak values can be set). |
| Automatic Criteria: transactions that meet this criteria will appear in Monitoring Review results regardless of Risk Engine Score. |
Exclusions
Items marked as “no longer valid” are excluded from the historical data.
You may also define exclusions that are applied after a risk result is determined for this analytic, including the following fields:
- Materiality (Customer Configured): transaction Amount USD is less than [$X,XXX].
- Subject Classification 2: a source-system classification that provides additional insight into the customer, such as customer segment, channel, or another designation used for business operations or risk monitoring.
- Values to exclude: [XXX, XXX, XXX]
- Transaction Type: the high-level classification of the transaction, providing a general understanding of its impact to the company. This is a harmonized value created by Case IQ in coordination with the customer.
- Values to exclude: [XXX, XXX, XXX]
- Transaction Type (Detailed): a more detailed transaction classification based on the original transaction type in the source system.
- Values to exclude: [XXX, XXX, XXX]