Thesis--Bonn. / Vita. Includes bibliographical references (p. vii-xiv).

Thesis, Rostock.

This thesis develops an analysis of the prevalence and determinants of financial fraud as identified in the Chinese listed firms over the period 1996 to 2007. First, theoretical analysis on the determinants of financial fraud, from its subjective, objective and conditional aspects, provides an understanding of why financial fraud happened as it did.  The conditional aspect (corporate governance mechanisms) is highlighted since it is controllable in reducing the probability of fraudulent reporting.  Data from the Chinese stock market is accessed, organised, and analyzed to support the analysis. Second, the prevalence and nature of fraud uncovered in the supervision of listed companies in Chinese stock exchanges is identified.  From data reported by the China Securities Regulatory Commission, the incidence and prevalence of cases of fraud identified through regulation is investigated. I show how fraudulent activity can be categorised, how its nature has evolved over time, how business sectors are differentially prone to fraud, and what modes of fraudulent activity have been recorded. Third, the key interest of this research lies in the investigation of the argument that companies are more, or less, prone to fraudulent reporting by reason of:  Their ownership structure; Their corporate governance characteristics; and/or Their numerical characteristics in financial reporting. 82 fraudulent financial statements from 40 listed companies identified by the China Securities Regulatory Commission are selected as the study sample, and 82 control peers are selected, to correspond to the study sample as closely as possible, regarding the assets size and industries.  Findings challenge the conventional arguments which have been supported based on data from western countries.  Conventional arguments show financial fraud is associated with weakness of governance in western companies (e.g. <i>Beasley et al., 2000</i>) and with patterns of ownership that would indicate reduced agency control by shareholders.  However, my finding reveals that in China ownership concentration is negatively associated with reported fraud; and as for some oft-discussed corporate governance characteristics (e.g. the supervisory board, audit committee, independent directors), the fraud firms and their non-fraud peers are not statistically distinct, suggesting that corporate governance mechanisms that are designed to reduce the probability of financial fraud fail to work in the Chinese market.  The negative results in this research contribute by updating our understanding of the determinants of financial statement fraud; the supervision of China’s equity markets; and whether it can be considered effective in uncovering financial fraud.

Thesis (M.M.S.)--Carleton University, 1992. / Accompanying material: 1 diskette in pocket (c.1: KAO; c.2: SONY). Also available in electronic format on the Internet.

Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 1997. / Includes bibliographical references. Also available on the Internet.

The use of analytical techniques for fraud detection and the design of fraud detection systems have been topics of several research projects in the past and have seen varying degrees of success in their practical implementation. In particular, several authors regard the use of credit risk scorecards for fraud detection as a useful analytical detection tool. However, research on analytical fraud detection for the South African insurance industry is limited. Furthermore, real world restrictions like the availability and quality of data elements, highly unbalanced datasets, interpretability challenges with complex analytical techniques and the evolving nature of insurance fraud contribute to the on-going challenge of detecting fraud successfully. Insurance organisations face financial instability from a global recession, tighter regulatory requirements and consolidation of the industry, which implore the need for a practical and effective fraud strategy. Given the volumes of structured and unstructured data available in data warehouses of insurance organisations, it would be sensible for an effective fraud strategy to take into account data-driven methods and incorporate analytical techniques into an overall fraud risk assessment system. Having said that, the complexity of the analytical techniques, coupled with the effort required to prepare the data to support it, should be carefully considered as some studies found that less complex algorithms produce equal or better results. Furthermore, an over reliance on analytical models can underestimate the underlying risk, as observed with credit risk at financial institutions during the financial crisis. An attractive property of the structure of the probabilistic weights-of-evidence (WOE) formulation for risk scorecard construction is its ability to handle data issues like missing values, outliers and rare cases. It is also transparent and flexible in allowing the re-adjustment of the bins based on expert knowledge or other business considerations. The approach proposed in the study is to construct fraud risk scorecards at entity level that incorporate sets of intrinsic and relational risk factors to support a robust fraud risk assessment. The study investigates the application of an integrated Suspicious Activity Assessment System (SAAS) empirically using real-world South African insurance data. The first case study uses a data sample of short-term insurance claims data and the second a data sample of life insurance claims data. Both case studies show promising results. The contributions of the study are summarised as follows: The study identified several challenges with the use of an analytical approach to fraud detection within the context of the South African insurance industry. The study proposes the development of fraud risk scorecards based on WOE measures for diagnostic fraud detection, within the context of the South African insurance industry, and the consideration of alternative algorithms to determine split points. To improve the discriminatory performance of the fraud risk scorecards, the study evaluated the use of analytical techniques, such as text mining, to identify risk factors. In order to identify risk factors from large sets of data, the study suggests the careful consideration of both the types of information as well as the types of statistical techniques in a fraud detection system. The types of information refer to the categories of input data available for analysis, translated into risk factors, and the types of statistical techniques refer to the constraints and assumptions of the underlying statistical techniques. In addition, the study advocates the use of an entity-focused approach to fraud detection, given that fraudulent activity typically occurs at an entity or group of entities level. / PhD, Operational Research, North-West University, Vaal Triangle Campus, 2011

The use of analytical techniques for fraud detection and the design of fraud detection systems have been topics of several research projects in the past and have seen varying degrees of success in their practical implementation. In particular, several authors regard the use of credit risk scorecards for fraud detection as a useful analytical detection tool. However, research on analytical fraud detection for the South African insurance industry is limited. Furthermore, real world restrictions like the availability and quality of data elements, highly unbalanced datasets, interpretability challenges with complex analytical techniques and the evolving nature of insurance fraud contribute to the on-going challenge of detecting fraud successfully. Insurance organisations face financial instability from a global recession, tighter regulatory requirements and consolidation of the industry, which implore the need for a practical and effective fraud strategy. Given the volumes of structured and unstructured data available in data warehouses of insurance organisations, it would be sensible for an effective fraud strategy to take into account data-driven methods and incorporate analytical techniques into an overall fraud risk assessment system. Having said that, the complexity of the analytical techniques, coupled with the effort required to prepare the data to support it, should be carefully considered as some studies found that less complex algorithms produce equal or better results. Furthermore, an over reliance on analytical models can underestimate the underlying risk, as observed with credit risk at financial institutions during the financial crisis. An attractive property of the structure of the probabilistic weights-of-evidence (WOE) formulation for risk scorecard construction is its ability to handle data issues like missing values, outliers and rare cases. It is also transparent and flexible in allowing the re-adjustment of the bins based on expert knowledge or other business considerations. The approach proposed in the study is to construct fraud risk scorecards at entity level that incorporate sets of intrinsic and relational risk factors to support a robust fraud risk assessment. The study investigates the application of an integrated Suspicious Activity Assessment System (SAAS) empirically using real-world South African insurance data. The first case study uses a data sample of short-term insurance claims data and the second a data sample of life insurance claims data. Both case studies show promising results. The contributions of the study are summarised as follows: The study identified several challenges with the use of an analytical approach to fraud detection within the context of the South African insurance industry. The study proposes the development of fraud risk scorecards based on WOE measures for diagnostic fraud detection, within the context of the South African insurance industry, and the consideration of alternative algorithms to determine split points. To improve the discriminatory performance of the fraud risk scorecards, the study evaluated the use of analytical techniques, such as text mining, to identify risk factors. In order to identify risk factors from large sets of data, the study suggests the careful consideration of both the types of information as well as the types of statistical techniques in a fraud detection system. The types of information refer to the categories of input data available for analysis, translated into risk factors, and the types of statistical techniques refer to the constraints and assumptions of the underlying statistical techniques. In addition, the study advocates the use of an entity-focused approach to fraud detection, given that fraudulent activity typically occurs at an entity or group of entities level. / PhD, Operational Research, North-West University, Vaal Triangle Campus, 2011

Thesis (doctoral)--Universität zu Köln.

Ph.D. (Computer Science) / Thanks to several advancements in communication technologies, the world today is a highly connected society promoting business transformations that highlight improved efficiency [1]. Unfortunately, systems developed for an increasingly connected world are also subject to increases in change, complexity and risk – the same connectedness that makes lives easier also signifies that any negative influences can be more difficult to handle and contain [2]. Multi-agent systems have been touted as ideal solutions to realising the required complexities across wide and varied problem domains that range from manufacturing [3] to eco-system management [4] to construction [5]. In an increasingly connected world, complex problems may require that various multi-agent systems work together in order to accomplish larger, overarching objectives. A fraud detection system, for example, could comprise a number of multi-agent systems, each designated to fulfil a very specific and important fraud detection task. The success of the fraud detection system will then depend on each of the various multi-agent systems’ abilities to achieve allocated goals and thus, contribute towards efforts to detect fraud accurately. Depending on factors that include objective and environment type, fraud detection tasks may entail working with numerous disparate systems [6] – it is possible that agent designs that are different from the rest of the fraud detection system must be implemented.Such inconsistency between multi-agent systems could potentially lead to conflicting goals, thereby jeopardising the resolution of the fraud detection system’s overall objectives. A further complication that may arise is the continuously changing financial services landscape – fraud detection systems must not only contend with the creativity of fraudsters, but should also be acutely aware of when day-to-day processes have changed due to recent innovations or technological advancements in the domain. Existing fraud detection methodologies may therefore need to be updated frequently in order to remain sufficiently informed of current developments. An agent-based fraud detection model was thus developed to assist anti-fraud professionals in the classification of day-to-day financial transactions. The proposed model comprises a number of multi-agent systems, each incorporated to add a particular aspect of the criminal justice process in investigating incidences of potential crime. By having agents emulate the various tasks that are involved in dealing with a crime, it is anticipated that the resulting fraud detection system will be able to achieve similar successes from applying the same procedure. In order to successfully develop the fraud detection model, an architecture for implementing a collaborative community of multi-agent subsystems for a dynamic environment was also developed. The architecture is intended to allow each multi-agent subsystem member to adapt to changes in the environment while ensuring that teamwork links are maintained amongst the different subsystems.

This study addresses the impact of countermeasures in the control and prevention of online fraud in Saudi Arabia and the influence of the environmental context. Combatting online fraud is facilitated when the public is fully educated and is aware of its types and of the prevention methods available. People are reliant on the Internet; the possibility of being breached by hackers and fraudsters is growing, especially as socialising, online shopping and banking are carried out through personal computers or mobile devices. Online fraud has been described as an epidemic that has spread to most online activities. Its prevalence has been noted to be in regions where there is high adoption of e-commerce, and, along with it, large online financial transactions. The argument is therefore the measures taken are either are inadequate or have failed to effectively address all the issues because of the organisational and environmental context of the country. This research aims to examine online fraud perceptions and the countermeasures designed and used by financial institutions in Saudi Arabia to control and prevent online fraud in its environmental context, to examine the effectiveness/impact of the countermeasures and to examine the factors that may affect/influence the impact of the countermeasures. The qualitative method approach was chosen to ensure balanced coverage of the subject matter. The nature of the research requires a broader, in-depth, examination of the experiences of the participants from their own perspective. Meanwhile levels of awareness are low, because of lack of knowledge and training, a lack of government sensitisation and the religious inclinations of the population. The findings also confirm the efforts of organisations to put in place countermeasures using various technological means, coupled with procedural controls and checks. The measures create obstacles to most customers, who find it cumbersome to engage in online activities because of those procedures and checks. The findings also show two types of regulations: government and organisational rules, with different foci and purposes, which are mostly centred on the monitoring of Internet operations and operational guidelines. The enforcement of rules in the light of prosecuting offenders has also been minimal and passive. The countermeasures of most banks/organisations mostly focus on prevention and detection. However, the findings suggest that the activities in each component and their interrelationships have a collective impact on combatting online fraud. The success of any effort or approach to combat fraudulent activities therefore depends on the activities of the four countermeasure components.