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

Application of the Benford’s law to Social bots and Information Operations activities

Benford\u27s law shows the pattern of behavior in normal systems. It states that in natural systems digits\u27 frequency have a certain pattern such that the occurrence of first digits in numbers are unevenly distributed. In systems with natural behavior, numbers begin with a “1” are more common than numbers beginning with “9”. It implies that if the distribution of first digits deviate from the expected distribution, it is indicative of fraud. It has many applications in forensic accounting, stock markets, finding abnormal data in survey data, and natural science. We investigate whether social media bots and Information Operations activities are conformant to the Benford\u27s law. Our results showed that bots\u27 behavior adhere to Benford\u27s law, suggesting that using this law helps in detecting malicious online automated accounts and their activities on social media. However, activities related to Information Operations did not show consistency in regards to Benford\u27s law. Our findings shedlight on the importance of examining regular and anomalous online behavior to avoid malicious and contaminated content on social media

Auditing Symposium XIII: Proceedings of the 1996 Deloitte & Touche/University of Kansas Symposium on Auditing Problems

Meeting the challenge of technological change -- A standard setter\u27s perspective / James M. Sylph, Gregory P. Shields; Technological change -- A glass half empty or a glass half full: Discussion of Meeting the challenge of technological change, and Business and auditing impacts of new technologies / Urton Anderson; Opportunities for assurance services in the 21st century: A progress report of the Special Committee on Assurance Services / Richard Lea; Model of errors and irregularities as a general framework for risk-based audit planning / Jere R. Francis, Richard A. Grimlund; Discussion of A Model of errors and irregularities as a general framework for risk-based audit planning / Timothy B. Bell; Framing effects and output interference in a concurring partner review context: Theory and exploratory analysis / Karla M. Johnstone, Stanley F. Biggs, Jean C. Bedard; Discussant\u27s comments on Framing effects and output interference in a concurring partner review context: Theory and exploratory analysis / David Plumlee; Implementation and acceptance of expert systems by auditors / Maureen McGowan; Discussion of Opportunities for assurance services in the 21st century: A progress report of the Special Committee on Assurance Services / Katherine Schipper; CPAS/CCM experiences: Perspectives for AI/ES research in accounting / Miklos A. Vasarhelyi; Discussant comments on The CPAS/CCM experiences: Perspectives for AI/ES research in accounting / Eric Denna; Digital analysis and the reduction of auditor litigation risk / Mark Nigrini; Discussion of Digital analysis and the reduction of auditor litigation risk / James E. Searing; Institute of Internal Auditors: Business and auditing impacts of new technologies / Charles H. Le Grandhttps://egrove.olemiss.edu/dl_proceedings/1012/thumbnail.jp

A conceptual model for proactive detection of potential fraud enterprise systems: exploiting SAP audit trails to detect asset misappropriation

Fraud costs the Australian economy approximately $3 billion annually, and its frequency and financial impact continues to grow. Many organisations are poorly prepared to prevent and detect fraud. Fraud prevention is not perfect therefore fraud detection is crucial. Fraud detection strategies are intended to quickly and efficiently identify frauds that circumvent preventative measures so that an organisation can take appropriate corrective action. Enhancing the ability of organisations to detect potential fraud may have a positive impact on the economy. An effective model that facilitates proactive detection of potential fraud may potentially save costs and reduce the propensity of future fraud by early detection of suspicious user activities. Enterprise systems generate millions of transactions annually. While most of these are legal and routine transactions, a small number may be fraudulent. The enormous number of transactions makes it difficult to find these few instances among legitimate transactions. Without the availability of proactive fraud detection tools, investigating suspicious activities becomes overwhelming. This study explores and develops innovative methods for proactive detection of potential fraud in enterprise systems. The intention is to build a model for detection of potential fraud based on analysis of patterns or signatures building on theories and concepts of continuous fraud detection. This objective is addressed by answering the main question; can a generalised model for proactive detection of potential fraud in enterprise systems be developed? The study proposes a methodology for proactive detection of potential fraud that exploits audit trails in enterprise systems. The concept of proactive detection of otential fraud is demonstrated by developing a prototype. The prototype is a near real-time web based application that uses SAS for its analytics processes. The aim of the prototype is to confirm the feasibility of implementing proactive detection of potential fraud in practice. Verification of the prototype is achieved by performing a series of tests involving simulated activity, followed by a full scale case study with a large international manufacturing company. Validation is achieved by obtaining independent reviews from the case study senior staff, auditing practitioners and a panel of experts. Timing experiments confirm that the prototype is able to handle real data volumes from a real organisation without difficulty thereby providing evidence in support of enhancement of auditor productivity. This study makes a number of contributions to both the literature and auditing practice

Contabilidad forense y blanqueo de capitales: aplicación del aprendizaje automático en un proceso judicial español

This PhD Dissertation adds two new results in detecting signs of financial fraud: (1) the application of automated learning techniques to internal accounting databases of companies to detect money laundering, and (2) the offer of information to the investigating authorities on how the money laundering network is organized, with the objective of orientating the judicial investigation towards those companies or physical persons who present signs of suspicious patterns. Thus, in the context of a real macro-case on money laundering in which the author has collaborated as forensic accountant, this study analyses the database available of the operations carried out between a core company and a set of 643 supplier companies, 26 of which had already been identified a priori by the Judicial Police as fraudulent. Faced with a well-founded suspicion that other suppliers within the network might have committed criminal acts, and in order to better manage the scarce police resources available, machine learning techniques are proposed with two different approaches to detect patterns of fraud. The first proposed approach is the implementation of Neural Network models to the expert-assisted work for the detection of fraud operations. For this purpose, based on machine learning techniques, the network structure used is that proposed by Hastie et al. (2008): The Back-Propagation Network. In the second approach, it is proposed a more ambitious procedure to pattern detection than the previous one, in which Benford's Law (Nigrini and Mittermaider, 1997), a tool to characterize accounting records of the commercial operations between the main company and its supplier, is combined with four models of classification: Ridge Logistic Regression (LG) (Le Cessie and van Houwelingen, 1992), Artificial Neural Networks (NN) (Hastie et al., 2008), Decision Tree C4.5 (DT) (Quinlan, 1993 and 1996) and Random Forest (RF) (Breiman, 2001). Overall, the Random Forest showed the best results with the SMOTE transformation, obtaining 96.15% of true negatives (TN Rate) and 94.98% of true positives (TP Rate). The classification capacity of this methodology is undoubtedly very high.Thus, the machine learning techniques proposed in this paper represent an efficient and objective new tool for detecting fraudulent patterns of behaviour for the investigation of money laundering offences, allowing police investigators to focus the limited economic and human resources available in the judicial processes on those companies under suspicion who present a pattern of behaviour similar to that of previously recognized fraudulent companies. This PhD Dissertation is structured in two parts. On the first part, composed of three Chapters, establishes the theoretical framework on which the research is based. The first Chapter outlines the concept of money laundering and studies the tendency of this crime in Spain. Chapter II describes the process of management and access to information prior to the application of the proposed techniques (Data Pre-processing). Next, Chapter III specifies the methodology applied based on machine learning techniques for the detection of money laundering pattern. The second part is devoted to the presentation of the judicial process and the analysis of the results. After the presentation of the judicial process and the description of the sample, on the Chapter IV are presented the results obtained in the application of the machine learning techniques proposed in the two approaches. The PhD Dissertation ends with the conclusions and with proposals for further research

First Digit Phenomenon in Number Generation Under Uncertainty: Through the Lens of Benford’s Law

Decision making under uncertainty has been investigated by looking for regularities due to the application of heuristics (Tversky & Kahneman, 1974). Contemporary society demands that we estimate numbers when making decisions, for instance, the value of an item, so regularities in the numbers people generate could help us understand how humans deal with unknown situations. Recent research (e.g., Burns, 2009) suggests that people could spontaneously exhibit a stronger bias towards the smaller leading digits (e.g., 1, 2) that approximates Benford’s law, a well-established phenomenon of the first digits aggregated from the naturally occurring datasets. Hence, it may also represent a potential regularity in how people produce unknown numbers. Therefore, the present study attempted to investigate the conditions under which the first digit phenomenon might occur under uncertainty by examining the degree of fit to Benford’s law with various forms of numerical responses, and more importantly, testing the existing speculations of why people might present such a bias when generating unknown values. The key elements of the designs were the statements of numerical questions and simple visual displays for estimations. As expected, the first digit phenomenon was stronger when generating non-arbitrary numbers, compared to the arbitrary numbers. The critical findings were the extension of Benford’s law to the estimation tasks with a peak of digit-5; the continued failure of the recognition hypothesis as a reliable explanation; and the supporting evidence of the Integration Hypothesis, which emphasises the attribute of processing multiple information for the occurrence of the first digit phenomenon in number generation. Building on and extending the results of the previous research conducted, the outcomes of this project can assist in understanding: 1) how numerical responses to unknown questions inform theories of numerical cognition and decision making, and 2) how the pattern of leading digits generated from humans might offer implications for the practices of Benford’s law in fraud detection

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Determinants of Forensic Accounting Techniques and Theories: An Empirical Investigation

 This study sought and investigated the determinants of forensic accounting techniques. The data analysed in this study were gathered from both primary and secondary sources. The 120 respondents were purposively selected, which includes forensic accountants, fraud auditors, bankers, forensic investigators, finance enthusiasts, fraud investigators, and those in academia. The data for this study were gathered electronically using an online questionnaire through Google Form. The Google Form analysis was adopted. Secondary data were the existing data, established by seasoned professionals and academics. The data were presented through pie charts, bar chats, and descriptions. The study shows that the nature of fraud under investigation which includes the level of crime perpetrated, how much involved, stages, complexity, and who is involved are the determinant of techniques to be applied to fraud examination. Also, other factors such as criminal evidence, the expertise and experience of the examiner, organisational policies, and the risks involved determine what techniques to be applied to forensic investigations. Data mining emerged as the most appropriate technique for fraud investigation, however, the combination of two or more techniques is advised for forensic accountants, forensic legal practitioners, and all other similar parties. This study recommends the need for stakeholders to engage, recruit, and employ the services of a forensic accountant to review, strengthen, reappraise records and internal control systems on a routine basis; Organisations should train employees on the dynamics and scope of financial crimes, the legal environment, fraud prevention, and ethical issues