System, software and method for examining a database in a forensic accounting environment

• US 20060173812A1
• Filed: 09/13/2005
• Published: 08/03/2006
• Est. Priority Date: 09/30/2004
• Status: Active Grant

• Current Assignee: Deloitte Development LLC
• Original Assignee: Deloitte Development LLC
• Inventors: Bahl, Yogesh, Looby, Joseph, Gavioli, Bruce
• Granted Patent: US 7,590,658 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06Q 40/12 Accounting
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

A system, software and method which enable the forensic examination of a database, particularly, a financial database such as a general ledger. The system, software and method leverage “n-” or multi-dimensional data interrogation analytics, particularly, online analytical processing (“OLAP”), to enable real-time data analysis of various dimensions of multi-dimensional data in the database and identification of records that are unusual and/or significant including from patterns or relationships among the data.

Claims

1. A computerized method for conducting a forensic examination of multi-dimensional data stored in a database, comprising the steps of:

providing multidimensional data in a database accessible to software having real-time data interrogation functionality, the software adapted to analyze different dimensions of the data and to identify at least one of patterns and relationships among the data,

categorizing the data into at least one preselected category,

loading at least one template defining at least one data analysis attribute preselected to indicate at least one of significant and anomalous data into the software,

effecting multi-dimensional data analysis on the data in real-time based on the at least one preselected template attribute,

creating at least one profiles defining at least one map to the database to facilitate navigation through the analyzed data,

navigating through the analyzed data in the direction of decreasing dimensions of the data based on the at least one profile, and identifying at least one aspect of the data that is at least one of significant and anomalous.

2. The method according to claim 1, further comprising the steps of cross-tabulating the at least one significant and anomalous aspect of the data and identifying associated fieldwork activity requirements.

3. The method according to claim 1, further comprising the step of automatically generating a report of the at least one significant and anomalous aspect of the data.

4. The method according to claim 1, further comprising the step of automatically generating a report of actionable recommendations based on the at least one significant and anomalous aspect of the data.

5. The method according to claim 1, further comprising the step of verifying at least one of accuracy and completeness of the data loaded in the database.

6. The method according to claim 1, further comprising the step of generating at least one of summary statistics and summary metrics on the data stored in the database.

7. The method according to claim 6, wherein the at least one of summary statistics and summary metrics includes at least one of (i) positive data values, (ii) negative data values, (iii) all data values, (iv) minimum data values, (v) maximum data values, (vi) nulls, (vii) averages of data values, and (viii) standard deviations of data values.

8. The method according to claim 1, wherein the data are financial data.

9. The method according to claim 8, wherein the financial data are general ledger data.

10. The method according to claim 9, wherein the step of categorizing the data into at least one preselected category includes breaking the data into at least one of income statement populations and balance sheet populations.

11. The method according to claim 10, further comprising the step of reconciling the data to an associated trial balance.

12. The method according to claim 10, wherein the step of effecting multi-dimensional data analysis on the data in real-time based on the at least one preselected template attribute is used to examine the data across the at least one of income statement populations and balance sheet populations.

13. The method according to claim 10, wherein the step of effecting multi-dimensional data analysis on the data in real-time based on the at least one preselected template attribute includes identifying at least one of patterns and relationships between the populations of the at least one of income statement populations and balance sheet populations.

14. The method according to claim 9, wherein the at least one template includes at least one of (i) reclassification and adjusting transaction entries, (ii) transaction type, (iii) duplicates, (iv) amounts that end in .00, (v) variance from Benford'"'"'s Law, (vi) transaction entry description, (vii) Z-score relationships, (viii) transaction entry user, and (ix) items occurring within a preselected time period before and after the closing of an accounting period.

15. The method according to claim 1, wherein the step of effecting multi-dimensional data analysis on the data in real-time based on the at least one preselected template attribute includes identifying at least one of patterns and relationships among the data.

16. The method according to claim 1, wherein the software is online analytical processing software.

17. The method according to claim 1, further comprising the step of automatically exporting the at least one profile to at least one report.

18. The method according to claim 1, wherein the at least one profile includes at least one data cube and at least one grid, the at least one data cube representing an n-dimensional view of the database, the at least one grid representing a two-dimensional view of records in the database.

19. The method according to claim 18, wherein the n-dimensional view of the database includes pages, rows, columns and measures.

20. The method according to claim 18, wherein the n-dimensional view of the database is adapted to be pivoted for analysis and filtered.

21. The method according to claim 18, wherein data in the at least one grid is at least one of grouped, sorted and filtered.

22. The method according to claim 18, wherein the step of navigating through the analyzed data in the direction of decreasing dimensions of the data based on the at least one profile includes selecting an analysis criterion in the at least one data cube and drilling down in an associated one of the at least one grid.

23. The method according to claim 1, wherein the at least one profile defines at least one three dimensional map.