Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
9-2003
Abstract
Often evidence from a single case does not reveal any suspicious patterns to aid investigations in forensic accounting and other forensic fields. In contrast, correlation of sets of evidence from several cases with suitable background knowledge may reveal suspicious patterns. Link Discovery (LD) has recently emerged as a promising new area for such tasks. Currently LD mostly relies on deterministic graphical techniques. Other relevant techniques are Bayesian probabilistic and causal networks. These techniques need further development to handle rare events. This paper combines first-order logic (FOL) and probabilistic semantic inference (PSI) to address this challenge. Previous research has shown this approach is computationally efficient and complete for statistically significant patterns. This paper shows that a modified method can be successful for discovering rare patterns. The method is illustrated with an example of discovery of suspicious patterns.
Recommended Citation
Kovalerchuk B., Vityaev E. (2003) Detecting Patterns of Fraudulent Behavior in Forensic Accounting. In: Palade V., Howlett R.J., Jain L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science, vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_69
Journal
KES 2003: Knowledge-Based Intelligent Information and Engineering Systems
Rights
© Springer-Verlag Berlin Heidelberg 2003
Included in
Computer Sciences Commons, Data Science Commons, Forensic Science and Technology Commons
Comments
This is a post-peer-review, pre-copyedit version of an article published in KES 2003: Knowledge-Based Intelligent Information and Engineering Systems. The final authenticated version is available online at: https://doi.org/10.1007/978-3-540-45224-9_69