Detecting Patterns of Fraudulent Behavior in Forensic Accounting

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

Benford's Law Applies To Online Social Networks

Benford's Law states that the frequency of first digits of numbers in naturally occurring systems is not evenly distributed. Numbers beginning with a 1 occur roughly 30\% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. We consider social data from five major social networks: Facebook, Twitter, Google Plus, Pinterest, and Live Journal. We show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same holds for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follow the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.Comment: 9 pages, 2 figure

Fraud cycles

Fraud is an ancient crime and one that annually causes hundreds of billions of dollars in losses. We examine the behavioral patterns over time of different types of frauds, which illustrate cyclical frequencies. We develop an evolutionary theory that suggests cyclic behavior in frauds should be common.fraud, cycle, steady state

A study of Malaysian accounting education in higher-learning institutions: Is Malaysia preparing undergraduates for a tsunami of fraud?

The current syllabus lack sufficiency of fraud education in areas of auditing, fraud examination and forensic accounting in the current accounting curriculum. This paper seeks to analyze the relationship between final year accouting students’ perceived coverage of fraud education and the overall sufficiency of the three areas in higher-learning institutions. Learning objectives were used to determine the sufficiency of fraud education in current accounting curriculum and a brief comparison between different higher learning institutions in Malaysia, students with and without internship experience, and test of ethical conduct was performed. Findings include insufficient coverage in the areas of fraud examination and forensic accounting but not auditing and that the students’ perceived coverage of fraud education depends on the sufficiency of fraud examination and forensic accounting areas. The paper only tested the perception aspect of students and results may differ depending on student’s aptitude in learning. This study provides valuable input to redesigning the current accounting curriculum to expose students to fraud-based learning environment and also incorporating forensic accounting courses. It seeks to regain society’s confidence in the accounting profession through improved fraud detection. The research will add value to the accounting education offered to undergraduates as very little prior research has been done to provide insights in students’ (end-user) perception of fraud education