Fraud detection in ERP systems using scenario matching

ERP systems generally implement controls to prevent certain common kinds of fraud. In addition however, there is an imperative need for detection of more sophisticated patterns of fraudulent activity as evidenced by the legal requirement for company audits and the common incidence of fraud. This paper describes the design and implementation of a framework for detecting patterns of fraudulent activity in ERP systems. We include the description of six fraud scenarios and the process of specifying and detecting the occurrence of those scenarios in ERP user log data using the prototype software which we have developed. The test results for detecting these scenarios in log data have been verified and confirm the success of our approach which can be generalized to ERP systems in general.

Intergroup relations and Muslims’ mental health in Western societies

This review article proposes that theories and research of intergroup contact, prejudice, and acculturation, enhance understanding of the current intercultural relations between Muslims and non-Muslims in Western societies, such as in Australia. The actual and perceived prejudice that many Muslims studying, working, and living in the West have been experiencing following the 2001 terrorist attacks, adds an additional layer of stress to the psychosocial adjustment of Muslim immigrants and sojourners, affecting their cross-cultural adaptation and mental health. Stephan and colleagues’ Integrated Threat Theory argues that the perceived threat experienced by all parties, explains the acts of prejudice. Berry’s acculturation framework highlights that adaptive acculturation is determined by congruent host nation policies and practices and immigrant acculturation strategies. Implications for multicultural policy, intercultural training, and mental health practice, and suggestions for future research, are discussed.

Bengali handwritten character recognition using modified syntactic method

Several approaches have been proposed to recognize handwritten Bengali characters using different curve fitting algorithms and curvature analysis. In this paper, a new algorithm (Curve-fitting Algorithm) to identify various strokes of a handwritten character is developed. The curve-fitting algorithm helps recognizing various strokes of different patterns (line, quadratic curve) precisely. This reduces the error elimination burden heavily. Implementation of this Modified Syntactic Method demonstrates significant improvement in the recognition of Bengali handwritten characters.

Continuous biometric authentication : can it be more practical?

Continuous biometric authentication schemes (CBAS) are built around the biometrics supplied by user behavioural characteristics and continuously check the identity of the user throughout the session. The current literature for CBAS primarily focuses on the accuracy of the system in order to reduce false alarms. However, these attempts do not consider various issues that might affect practicality in real world applications and continuous authentication scenarios. One of the main issues is that the presented CBAS are based on several samples of training data either of both intruder and valid users or only the valid users' profile. This means that historical profiles for either the legitimate users or possible attackers should be available or collected before prediction time. However, in some cases it is impractical to gain the biometric data of the user in advance (before detection time). Another issue is the variability of the behaviour of the user between the registered profile obtained during enrollment, and the profile from the testing phase. The aim of this paper is to identify the limitations in current CBAS in order to make them more practical for real world applications. Also, the paper discusses a new application for CBAS not requiring any training data either from intruders or from valid users.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

An interview with Dr. Benjamin Isakhan : on the alternative histories of democracy

Dr. Isakahn is currently a research associate with the Centre for Dialogue at La Trobe University in Australia. His latest works include several forthcoming books: Democracy in Iraq is a monograph soon to be released; whilst The Edinburgh Companion to the History of Democracy and The Secret History of Democracy, both done in concert with Stephen Stockwell, are edited collections. His most recent articles include “Targeting the Symbolic Dimension of Baathist Iraq,” “Measuring Islam in Australia” and “Manufacturing Consent in Iraq.” For further information regarding Dr. Isakhan and his works, please visit his website, www.benjaminisakhan.com.

Detecting collusive fraud in enterprise resource planning systems

In today's technological age, fraud has become more complicated, and increasingly more difficult to detect, especially when it is collusive in nature. Different fraud surveys showed that the median loss from collusive fraud is much greater than fraud perpetrated by a single person. Despite its prevalence and potentially devastating effects, collusion is commonly overlooked as an organizational risk. Internal auditors often fail to proactively consider collusion in their fraud assessment and detection efforts. In this paper, we consider fraud scenarios with collusion. We present six potentially collusive fraudulent behaviors and show their detection process in an ERP system. We have enhanced our fraud detection framework to utilize aggregation of different sources of logs in order to detect communication and have further enhanced it to render it system-agnostic thus achieving portability and making it generally applicable to all ERP systems.