Summary
The valuation score collects control point results that are relevant to the valuation assertion. This score is calculated using the same weighted average algorithm as the MindBridge score. All of the control points that contribute to this score are equally weighted.
Control points in the valuation score
The following control points are included in the valuation score for AP and AR analyses.
|
Control point |
Type |
Weight |
Rationale |
|---|---|---|---|
| Statistical | 5 | Entries that don’t conform to the statistical curve of Benford’s Law increase the likelihood that entry IDs may be fabricated or don’t correspond to a valid “natural” transaction. | |
| Rules-based | 5 | Entries with values from a monetary value perspective are atypical for the ledger may increase the likelihood that these aren’t valid items or erroneously overstated. | |
| Rules-based | 5 | Related party transactions can be complex from a valuation perspective and inherently the risk that they may not be properly recorded is higher vs. entries with arms-length customers and vendors. | |
| Rules-based | 5 | Entries with values that trigger the Last 3 Digits control point may signal estimates vs. naturally occurring transactions and therefore increases the risk that they aren’t captured at an appropriate value. | |
| Rules-based | 5 | Invoices that are aged beyond 90 days outstanding (or a lower threshold, as can be customized) may signal an increased risk around valuation and these items not being collectible/valid obligation. | |
| Rules-based | 5 |
Entries with values that have one or more suspicious keywords may indicate a risk that the corresponding invoice or payment lacks commercial substance and/or is misstated. Examples: adjust, accrual, estimate, reduc*, etc. |
|
| Machine learning | 40 | Unusual amounts of entries for a certain vendor or customer may indicate that one or more entries don’t have commercial substance, or perhaps were posted in error. |
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