Systems and methods for monitoring and detecting fraudulent uses of business applications
A systems and methods are described detect fraud in existing logs of raw data. There can be several disparate logs, each including data of disparate data types and generated by different and possibly unrelated software enterprise applications. The fraud management system aggregates and organizes the raw log data, extends the raw data with reference data, archives the data in a manner that facilitates efficient access and processing of the data, allows for investigation of potentially fraudulent usage scenarios, and uses the results of the investigation to identify patterns of data that correspond to correspond to high risk usage scenarios and/or process steps. In subsequent processing, archived data can be compared against the identified patterns corresponding to high risk usage scenarios to detect matches, and the invention thereby automatically detects high risk usage scenarios and issues appropriate alerts and reports.
1. A method for identifying a fraudulent use of an application, the method comprising:
obtaining an existing log of data generated from uses of an application, the existing log of data including a plurality of data fields;
indexing reference data by at least a first unique key and a second unique key, each unique key corresponding to a different data field;
extending the existing log of data with at least the reference data indexed by the first unique key and the reference data indexed by the second unique key;
identifying a location of a data field within the extended data;
indexing the data field and information specifying the location in a data structure;
identifying a signature pattern representative of potentially fraudulent behavior;
comparing at least a portion of the data structure to the signature pattern to identify the fraudulent use; and
outputting a representation reflecting a result of the comparison.
2. The method of claim 1 , wherein identifying a location of a data field includes identifying a location of at least one of a name field, a time field, a place field, an action type field, or an account identification field.
3. The method of claim 1 , wherein obtaining the existing log of data includes obtaining data associated with respective process steps performed by respective users during the uses of the application.
4. The method of claim 1 , wherein comparing at least a portion of the data structure comprises obtaining the data structure and a query related to a suspected fraudulent usage scenario of the application to an analyst, the data structure providing location information of data satisfying the query.
5. The method of claim 1 , wherein the signature pattern is a constraint, and comparing at least a portion of the data structure to the pattern includes searching for data that satisfies the constraint.
6. The method of claim 1 , wherein the signature pattern is a constraint including Boolean operations, and comparing at least a portion of the data structure to the pattern includes evaluating the Boolean expressions with respect to the indexed data.
7. The method of claim 1 , wherein the identified signature pattern comprises one of a set of active signatures or a pattern identified from the extended data.
8. A non-transitory computer-readable storage medium storing software that, when executed by at least one processor, causes the at least one processor to perform a method, the method comprising:
obtaining an existing log of data generated from uses of an application, the existing log of data including a plurality of data fields;
indexing reference data by at least a first unique key and a second unique key, each unique key corresponding to at least one of a different data field;
extending the existing log of data with at least the reference data indexed by the first unique key and the reference data indexed by the second unique key;
identifying a location of a data field within the extended data;
including the data field and information specifying the location in a data structure;
identifying a signature pattern representative of potentially fraudulent behavior;
comparing at least a portion of the data structure to the signature pattern to identify the fraudulent use; and
outputting a representation reflecting a result of the comparison.
9. The storage medium of claim 8 , wherein identifying a location of a data field includes identifying a location of at least one of a name field, a time field, a place field, an action type field, or an account identification field.
10. The storage medium of claim 8 , wherein obtaining the existing log of data includes obtaining data associated with respective process steps performed by respective users during the uses of the application.
11. The storage medium of claim 8 , wherein comparing at least a portion of the data structure comprises obtaining the data structure and a query related to a suspected fraudulent usage scenario of the application to an analyst, the data structure providing location information of data satisfying the query.
12. The storage medium of claim 8 , wherein the signature pattern is a constraint, and comparing at least a portion of the data structure to the pattern includes searching for data that satisfies the constraint.
13. The storage medium of claim 8 , wherein the signature pattern is a constraint including Boolean operations, and comparing at least a portion of the data structure to the pattern includes evaluating the Boolean expressions with respect to the indexed data.
14. The storage medium of claim 8 , wherein the identified signature pattern comprises one of a set of active signatures or a pattern identified from the extended data.
15. An electronic device comprising:
at least one processor; and
software stored on a storage medium, the software configured to cause the at least one processor to perform a method comprising:
obtaining an existing log of data generated from uses of an application, the existing log of data including a plurality of data fields;
indexing reference data by at least a first unique key and a second unique key, each unique key corresponding to a different data field;
extending the existing log of data with at least the reference data indexed by the first unique key and the reference data indexed by the second unique key;
identifying a location of a data field within the extended data;
including the data field and information specifying the location in a data structure;
identifying a signature pattern representative of potentially fraudulent behavior;
comparing at least a portion of the data structure to the signature pattern to identify the fraudulent use; and
outputting a representation reflecting a result of the comparison.
16. The device of claim 15 , wherein identifying a location of a data field includes identifying a location of at least one of a name field, a time field, a place field, an action type field, or an account identification field.
17. The device of claim 15 , wherein obtaining the existing log of data includes obtaining data associated with respective process steps performed by respective users during the uses of the application.
18. The device of claim 15 , wherein the signature pattern is a constraint, and comparing at least a portion of the data structure to the pattern includes searching for data that satisfies the constraint.
19. The device of claim 15 , wherein the signature pattern is a constraint including Boolean operations, and comparing at least a portion of the data structure to the pattern includes evaluating the Boolean expressions with respect to the indexed data.
20. The device of claim 15 , wherein the identified signature pattern comprises one of a set of active signatures or a pattern identified from the extended data.