The three objectives of this dissertation were to develop a question type model for predicting linguistic features of responses to interview questions, create a tool for linguistic analysis of documents, and use lexical bundle analysis to identify linguistic differences between fraudulent and non-fraudulent financial reports. First, The Moffitt Question Type Model (MQTM) was developed to aid in predicting linguistic features of responses to questions. It focuses on three context independent features of questions: tense (past vs. present vs. future), perspective (introspective vs. extrospective), and abstractness (concrete vs. conjectural). The MQTM was tested on responses to real-world pre-polygraph examination questions in which guilty (n = 27) and innocent (n = 20) interviewees were interviewed. The responses were grouped according to question type and the linguistic cues from each groups' transcripts were compared using independent samples t-tests with the following results: future tense questions elicited more future tense words than either past or present tense questions and present tense questions elicited more present tense words than past tense questions; introspective questions elicited more cognitive process words and affective words than extrospective questions; and conjectural questions elicited more auxiliary verbs, tentativeness words, and cognitive process words than concrete questions. Second, a tool for linguistic analysis of text documents, Structured Programming for Linguistic Cue Extraction (SPLICE), was developed to help researchers and software developers compute linguistic values for dictionary-based cues and cues that require natural language processing techniques. SPLICE implements a GUI interface for researchers and an API for developers. Finally, an analysis of 560 lexical bundles detected linguistic differences between 101 fraudulent and 101 non-fraudulent 10-K filings. Phrases such as "the fair value of," and "goodwill and other intangible assets" were used at a much higher rate in fraudulent 10-Ks. A principal component analysis reduced the number of variables to 88 orthogonal components which were used in a discriminant analysis that classified the documents with 71% accuracy. Findings in this dissertation suggest the MQTM could be used to predict features of interviewee responses in most contexts and that lexical bundle analysis is a viable tool for discriminating between fraudulent and non-fraudulent text.

The third general standard of fieldwork requires auditors to maintain a skeptical mindset with regards to the collection and critical assessment of audit evidence. While professional skepticism is frequently referenced by professional standards, a lack of precision in defining the concept presumably leads to variation in how skepticism is exercised in practice. Drawing on theories from the fields of psychology, economics and organizational justice, this dissertation considers different perspectives of what constitutes sufficient professional skepticism and examines how those perspectives differ between audit practitioners and regulators. First, I consider competing perspectives of professional skepticism – neutral versus presumptive doubt – and whether asking auditors to adopt alternative perspectives of skepticism may have implications for audit efficiency and effectiveness. While, too little skepticism may endanger audit effectiveness and lead to audit failure or enforcement action, too much skepticism may arguably lead to unnecessary costs and inefficiency. Second, I consider whether the nature of the auditor-client relationship threatens an auditor’s ability to maintain an attitude of professional skepticism. For example, theoretical perspectives from the fields of psychology and economics suggest that auditors may, consciously or unconsciously, be less skeptical of clients with whom they have developed close, positive working relationships or financial dependencies. More specifically, I consider whether skeptical behavior is impeded by management who display low-risk attitudes towards fraud or by client’s who are considered to be highly important to the profitability of the local office. Finally, I examine how professional skepticism is defined from a regulator’s perspective. When a public company is accused of fraudulent financial reporting, regulators may determine iii that the audit performed on the fraudulent financial statements was deficient. Prior research has suggested that in such cases, insufficient skepticism is often a leading cause of alleged audit failure. Within a fairness theory framework, this study examines enforcement actions against auditors between 1999 and 2009, and identifies certain factors that are associated with a citation for a lack of professional skepticism. Overall, results suggest that regulators approach the issue by determining whether auditors should have been more skeptical. Factors found to affect this determination include whether the auditor was perceived as having been aware of an elevated risk of fraud or whether the client was accused of having provided the auditor with false or misleading information during the course of their investigation.

The ability of auditors to detect fraud, including intentional material misstatements in earnings, remains key to the credibility of audit firms and confidence in capital markets. The PCAOB concludes from its most recent inspections of public company audits that auditors often fail to assess and respond to risks of material misreporting by management. In a behavioral experiment, this study concludes that auditors can increase sensitivity to management motivation to misreport by actively seeking to transform identified risk factors focused on the organization, into factors focused on top managers, and to evaluate whether these manager-focused risk factors represent incentives for personal gain or pressures to avoid a personal loss on the managers. Currently, auditing standards use incentive and pressure as interchangeable constructs, but auditors in this study assess pressure on managers to avoid a loss as a greater risk than an incentive to managers to attain a gain. Results also demonstrate that auditors will be made more sensitive to fraudulent financial reporting risk when focusing on pressure on top managers, than they will be by engaging in a traditional process of assessing total fraud risk based on the three fraud triangle elements. This study is the first to propose a theoretical explanation for why prior studies reflect auditor insensitivity to organizational level fraud risk factors. This study is also the first to enhance knowledge about auditor risk assessment and decision-making through the application of prospect theory and through disaggregation of one of the three elements of the fraud triangle model, by differentiating between incentive and pressure for misreporting earnings.

This study investigates whether a relationship exists between fraudulent financial reporting and a variety of corporate governance attributes. Numerous high profile accounting scandals perpetuated over recent years have brought prominence to the corporate governance structure employed by US public companies. Many of these scandals involved manipulation of the financial reporting process by high level managers. It is therefore thought that a lack of effective oversight provided by the governing bodies engaged to monitor the actions of management may be at the heart of the problem. A review of prior research is used to identify the attributes of corporate governance relevant for inclusion in this study and to provide support for the posing of twenty directional hypotheses. The selected corporate governance attributes are classified into four broad categories depicting Audit Committee Functionality, Board of Director Composition, Ownership Structure, and External Auditor Factors. A matched pair research design is utilised to determine whether significant differences exist between the corporate governance attributes employed by fraud and non-fraud companies. A sample of 76 fraud companies, identified through an examination of Accounting and Auditing Enforcement Releases issued by the Securities Exchange Commission and drawn from a total of 223 companies examined, are tested along with an industry-size matched sample of non-fraud companies. The results of univariate paired t-tests and a conditional logistic regression equation find that statistically significant relationships do exist between a number of corporate governance attributes and fraudulent financial reporting. Specifically, the study finds that the percentage of independent directors on a company's board, the existence of a nominating committee, and the engaging of a Big6 auditor are negatively related to the incidence of fraud. Whereas, the average number of directorships held by audit committee members, the duality of the CEO and Chairman of the Board positions, and the percentage of company ownership held by outside blockholders are positively related to the incidence of fraudulent financial reporting.

Dissertation tries to put near auditor's reaction to realized frauds during audit of final accounts. Beginning is focused on sence of audit, history and present. Frauds of accounting entity from point of view of international standards on auditing (ISA) are specified in next part of thesis. Main part is focus on concrete reaction of auditor, not only on accounting frauds, but also on possible legalization of earnings from criminal activity. Conclusion deal with actual progres of ISA and forensic investigation.

由於財務報表舞弊往往足以震撼投資大眾，造成資本市場重大損失，各國監管單位無不盡力降低此事件之爆發，以維護資本市場秩序、保障投資人，是以本研究欲瞭解影響中國大陸上市公司舞弊之因素為何，以及如何建立舞弊預測模型提供財務報表使用者作為參考之用。本文利用2007年至2014年受懲罰之上市公司為研究對象，採Logistic迴歸進行實證分析，結果顯示裁決性收入與Z"-Score對於財務報表舞弊無顯著相關，相反的獨立董事比例、是否具ST壓力、存貨週轉率、應收帳款週轉率、主營業務利潤率與財務報表舞弊具顯著關係，另外利用迴歸結果中顯著變數建立財務報表舞弊模型，發現整體正確率為53.31％。 / Due to the severe impacts caused by fraudulent financial reporting, securities regulatory commissions in most countries put much emphasis on maintaining the order of the capital markets and protecting the investors’ interests. In order to realize the factors of financial statement fraud, especially for China listed companies, and build the detecting model for the financial statements users, I select some listed companies punished by the government during the period 2007-2014 as the samples in this dissertation. Then, I use logistic regression model to test which variables are significant to fraudulent financial reporting, and the results show that the discretionary revenue and Z"-Score do not have impact on it. On the contrary, the percentage of independent directors, pressure from avoiding being “ST”, inventory turnover, accounts receivable turnover, and percentage of income from main operation are significantly relevant to fraudulent financial reporting. Moreover, when including these significant variables in the detecting model, the accuracy of the model can up to 53.31 percent.

No description available.

增長層級式自我組織映射網路(GHSOM)屬於一種非監督式類神經網路，為自我組織映射網路(SOM)的延伸，擅長於對樣本分群，以輔助分析樣本族群裡的共同特徵，並且可以透過族群間存在的空間關係假設來建立分類器，進而辨別出異常的資料。 因此本研究提出一個創新的對偶方法(即為一個建立決策支援系統架構的方法)分別對舞弊與非舞弊樣本分群，首先兩類別之群組會被配對，即辨識某一特定無弊群體的非舞弊群體對照組，針對這些配對族群，套用基於不同空間假設所設立的分類規則以檢測舞弊與非舞弊群體中是否有存在某種程度的空間關係，此外並對於舞弊樣本的分群結果加入特徵萃取機制。分類績效最好的分類規則會被用來偵測受測樣本是否有舞弊的嫌疑，萃取機制的結果則會用來標示有舞弊嫌疑之受測樣本的舞弊行為特徵以及相關的輸入變數，以做為後續的決策輔助。 更明確地說，本研究分別透過非舞弊樣本與舞弊樣本建立一個非舞弊GHSOM樹以及舞弊GHSOM樹，且針對每一對GHSOM群組建立分類規則，其相應的非舞弊/舞弊為中心規則會適應性地依循決策者的風險偏好最佳化調整規則界線，整體而言較優的規則會被決定為分類規則。非舞弊為中心的規則象徵絕大多數的舞弊樣本傾向分布於非舞弊樣本的周圍，而舞弊為中心的規則象徵絕大多數的非舞弊樣本傾向分布於舞弊樣本的周圍。 此外本研究加入了一個特徵萃取機制來發掘舞弊樣本分群結果中各群組之樣本資料的共同特質，其包含輸入變數的特徵以及舞弊行為模式，這些資訊將能輔助決策者(如資本提供者)評估受測樣本的誠實性，輔助決策者從分析結果裡做出更進一步的分析來達到審慎的信用決策。 本研究將所提出的方法套用至財報舞弊領域(屬於財務舞弊偵測的子領域)進行實證，實驗結果證實樣本之間存在特定的空間關係，且相較於其他方法如SVM、SOM+LDA和GHSOM+LDA皆具有更佳的分類績效。因此顯示本研究所提出的機制可輔助驗證財務相關數據的可靠性。此外，根據SOM的特質，即任何受測樣本歸類到某特定族群時，該族群訓練樣本的舞弊行為特徵將可以代表此受測樣本的特徵推論。這樣的原則可以用來協助判斷受測樣本的可靠性，並可供持續累積成一個舞弊知識庫，做為進一步分析以及制定相關信用決策的參考。本研究所提出之基於對偶方法的決策支援系統架構可以被套用到其他使用財務數據為資料來源的財務舞弊偵測情境中，作為輔助決策的基礎。 / The Growing Hierarchical Self-Organizing Map (GHSOM) is extended from the Self-Organizing Map (SOM). The GHSOM’s unsupervised learning nature such as the adaptive group size as well as the hierarchy structure renders its availability to discover the statistical salient features from the clustered groups, and could be used to set up a classifier for distinguishing abnormal data from regular ones based on spatial relationships between them. Therefore, this study utilizes the advantage of the GHSOM and pioneers a novel dual approach (i.e., a proposal of a DSS architecture) with two GHSOMs, which starts from identifying the counterparts within the clustered groups. Then, the classification rules are formed based on a certain spatial hypothesis, and a feature extraction mechanism is applied to extract features from the fraud clustered groups. The dominant classification rule is adapted to identify suspected samples, and the results of feature extraction mechanism are used to pinpoint their relevant input variables and potential fraud activities for further decision aid. Specifically, for the financial fraud detection (FFD) domain, a non-fraud (fraud) GHSOM tree is constructed via clustering the non-fraud (fraud) samples, and a non-fraud-central (fraud-central) rule is then tuned via inputting all the training samples to determine the optimal discrimination boundary within each leaf node of the non-fraud (fraud) GHSOM tree. The optimization renders an adjustable and effective rule for classifying fraud and non-fraud samples. Following the implementation of the DSS architecture based on the proposed dual approach, the decision makers can objectively set their weightings of type I and type II errors. The classification rule that dominates another is adopted for analyzing samples. The dominance of the non-fraud-central rule leads to an implication that most of fraud samples cluster around the non-fraud counterpart, meanwhile the dominance of fraud-central rule leads to an implication that most of non-fraud samples cluster around the fraud counterpart. Besides, a feature extraction mechanism is developed to uncover the regularity of input variables and fraud categories based on the training samples of each leaf node of a fraud GHSOM tree. The feature extraction mechanism involves extracting the variable features and fraud patterns to explore the characteristics of fraud samples within the same leaf node. Thus can help decision makers such as the capital providers evaluate the integrity of the investigated samples, and facilitate further analysis to reach prudent credit decisions. The experimental results of detecting fraudulent financial reporting (FFR), a sub-field of FFD, confirm the spatial relationship among fraud and non-fraud samples. The outcomes given by the implemented DSS architecture based on the proposed dual approach have better classification performance than the SVM, SOM+LDA, GHSOM+LDA, SOM, BPNN and DT methods, and therefore show its applicability to evaluate the reliability of the financial numbers based decisions. Besides, following the SOM theories, the extracted relevant input variables and the fraud categories from the GHSOM are applicable to all samples classified into the same leaf nodes. This principle makes that the extracted pre-warning signal can be applied to assess the reliability of the investigated samples and to form a knowledge base for further analysis to reach a prudent decision. The DSS architecture based on the proposed dual approach could be applied to other FFD scenarios that rely on financial numbers as a basis for decision making.