A rule-based fuzzy reasoning system for assessing the risk of management fraud

Detecting management fraud and assessing the risk of management fraud are significant issues confronting the auditing profession. Considerable theoretical and empirical research (Loebbecke, Eining, and Willingham, 1989; Bell, Szykowny, and Willingham, 1993; Fanning, Cogger, and Srivastava, 1995; and Hansen, McDonald, Messier, and Bell, 1996) has been accomplished investigating these issues. Building on this research, we demonstrate the construction of a rule-based fuzzy reasoning system to assess the risk of management fraud. The paper illustrates how fuzzy sets can be used intuitively to measure red flags on a categorical or interval scale, how different red flags can be combined using fuzzy rules, and how a single measure of the risk of management fraud can be derived. The knowledge base for this fuzzy reasoning system is developed by using the causal model of management fraud developed by Loebbecke, Eining and Willingham (1989), the empirical investigation of this model by Bell, Szykowny, and Willingham (1993), other researchers’ efforts and the authors’ judgments, using XpertRule software. The fuzzy reasoning system is tested using the fraud data provided by KPMG Peat Marwick. We discuss methods to magnify the knowledge base of this fuzzy reasoning system to make it a viable auditing tool, the costs and benefits of building a fuzzy reasoning system, and further extensions of this research. Copyright © 1998 John Wiley & Sons, Ltd.     

SCAN: A Case-Based Reasoning Model for Generating Information System Control Recommendations

This paper describes SCAN, a case-based reasoning model for generating information system control recommendations. The purpose of the paper is to explain how a case-based reasoner may be used to support inexperienced information system auditors in evaluating controls and proposing audit recommendations. As a case-based reasoner, SCAN functions by reasoning by analogy to similar past cases. SCAN models audit experience as traces of past cases which are stored in a case knowledge base. In addition to a database of past audit cases, SCAN consists of indices for storing and retrieving cases, a similarity metric for measuring case similarity, and rules for using similar cases to generate control recommendations. SCAN uses past cases to remind the user of previous control failures, to set expectations about case features and controls, to use as a pattern against which to compare a client's controls and to help justify or explain its recommendations. SCAN's recommendations were judged to be more like those of an experienced auditor than either a student or a textbook model.     

税收执法说理制度的实证考察与理论阐释——基于88份税务行政裁判文书的分析

国家税务总局在《关于全面推进依法治税的指导意见》中提出建立税收执法说理制度,这一制度是约束税收行政裁量权、切实保护纳税人权利的有力举措。本文以88份税收执法说理争议裁判文书为分析对象,从案由、争议类型、争议内容及裁判结果等方面对我国税收执法说理制度运行状况展开考察。实践表明,目前税收执法说理制度的实施效果欠佳,存在没有说理、说理不充分、说理不当等现象,亟待治理。从规范层面来看,现行税收执法说理制度存在着法律性质不清、说理规则不明、说理责任缺失等不足,应当从法律上明确税收执法主体的说理义务,从说理对象、说理形式、说理内容、说理标准等方面系统构建说理规则,建立说理责任机制,确保税收执法说理制度的良性实施。     

Bayesian Fraud Risk Formula for Financial Statement Audits

In this article we extend the work of Loebbecke et al. (1989 ) and illustrate the use of an evidential reasoning approach for developing fraud risk analysis models under the Bayesian framework. New formulations facilitating fraud risk assessments are needed because decision tree approaches previously used to develop analytical models are not appropriate in complex situations involving several interrelated variables. To demonstrate the evidential reasoning approach, a fraud risk assessment formula is derived and illustrated. The fraud risk formula captures the impact of the presence or absence of and interrelationships between the three ‘fraud triangle’ risk factors: Incentives, Attitude and Opportunities. The formula includes the impact of risks and controls related to these three fraud risk factors as well as the impact of forensic audit procedures and relevant analytical and other procedures that provide evidence for the presence or absence of fraud. This formula may be used in audit practice both to help plan the audit and to assess fraud risk sequentially as audit evidence is obtained.     

Corporate Finance and Corporate Governance

A combined treatment of corporate finance and corporate governance is herein proposed. Debt and equity are treated not mainly as alternative financial instruments, but rather as alternative governance structures. Debt governance works mainly out of rules, while equity governance allows much greater discretion. A project-financing approach is adopted. I argue that whether a project should be financed by debt or by equity depends principally on the characteristics of the assets. Transaction-cost reasoning supports the use of debt (rules) to finance redeployable assets, while non-redeployable assets are financed by equity (discretion). Experiences with leasing and leveraged buyouts are used to illustrate the argument. The article also compares and contrasts the transaction-cost approach with the agency approach to the study of economic organization.     

论"电子钱包"的规范与监管

民间发行的卡基支付工具日渐增多,引发出一些法律问题亟待解决。但由于其地位处于灰色区域,使得现行的法律无法有针对性地加以适用。要改变这种状况首先就要为其正名,废除已有的不合理规定。然后进行金融创新,对其进行有效的规范与监管,以遏制腐败、偷税漏税、损害消费者利益的现象的出现。     

Optimal life insurance purchase and consumption/investment under uncertain lifetime

In this paper, we consider optimal insurance and consumption rules for a wage earner whose lifetime is random. The wage earner is endowed with an initial wealth, and he also receives an income continuously, but this may be terminated by the wage earner’s premature death. We use dynamic programming to analyze this problem and derive the optimal insurance and consumption rules. Explicit solutions are found for the family of CRRA utilities, and the demand for life insurance is studied by examining our solutions and doing numerical experiments.     

THE ECONOMICS OF MALARIA CONTROL

Ideally, in devising and assessing policies to control disease,the rules and reasoning of economics should be combined withcomprehensive epidemiological information to arrive at the bestdecisions. Simple economic concepts can be of great practicalassistance to policymakers in disease control. This articledescribes the economic principles to be applied and the kindof information needed to make informed choices about the optionsfor controlling malaria. In this context, the article surveysthe research on the costs that malaria imposes on people andeconomies, discusses how to assess the costs and effects ofinterventions used to combat the problem, and identifies theconceptual difficulties and gaps in information that must bebridged before the marriage of the two disciplines can be effectivelyconsummated.      

An analysis of the association of traditional demographic variables with the moral reasoning of auditing students and auditors

Accountants may be inadequate moral reasoners (Armstrong, 1987; Ponemon, 1992). Accounting ethics education research has suggested several approaches to improving the moral reasoning of accounting students (Langenderfer & Rockness, 1989; Ponemon & Glazer, 1990). This study uses independent samples of 91 auditing students and 207 auditors to evaluate whether demographic variables traditionally associated with higher levels of moral reasoning in other populations are associated with auditing students' and auditors' moral reasoning. Age and education, demographics traditionally associated with moral reasoning, were nonsignificant for both samples. Moral reasoning scores increased through the third-year staff level, and decreased from the senior through the partner levels. Women, subjects with higher grade point averages, and those who had taken ethics courses demonstrated higher levels of moral reasoning in both samples. The results indicate that accounting educators can influence the moral reasoning of the profession by recruiting and retaining bright students, particularly women, and by designing ethics education interventions that will help accounting students incorporate more than simply rules in making ethical decisions.     

The DIME System: A Preliminary Report

Investment management is a complex, yet promising, domain for the application of intelligent automation. In this paper we discuss the Diversified Investment Management Expert (DIME) system which is under development. DIME is designed to operate in a dynamic environment and hence one of its salient features is its knowledge acquisition via learning. The system starts out with some rules built into it, and machine-learning techniques are used to acquire further knowledge with time. In this paper we focus on how similarity-based learning (SBL) is used to acquire new rules and explanation-based learning (EBL) to refine the built-in rules and the newly acquired ones.     

Expert judgements of sea-level rise at the local scale

Whilst local projections of sea-level rise (SLR) are necessary to facilitate targeted climate change adaptation and communication strategies, downscaling from global climate models can be problematic. Here, we use expert probability judgement to elicit a suite of local projections, and associated uncertainties, for future SLR on the Severn Estuary in the south-west of the UK. Eleven experts from a range of policy and academic backgrounds took part in a structured probability elicitation exercise for the years 2050, 2100 and 2200. In addition to the quantitative elicitation, the experts’ reasoning during the task was qualitatively analysed. Quantitative analyses show that although there is consensus that sea levels will rise on the Estuary in future, there is wide variation between judgements and much uncertainty regarding the magnitude of future rise. For example, median estimates of SLR (compared to the 2011 level) range from 9.6 to 40 cm in the year 2050; 20 to 100 cm in 2100; and 35 to 300 cm in 2200. Fifty per cent confidence intervals and ninety per cent confidence intervals vary even more. Qualitative analyses indicate that experts’ judgements may have been influenced by their choice of methods and information sources, the ways in which they thought about the future, and heuristics. The study shows the merits of integrating qualitative and quantitative methods to explore the reasoning behind uncertainty judgements. We conclude that where expert probability judgements are to be used to characterise uncertainty such reasoning should be made explicit.     

On the efficacy of stop-loss rules in the presence of overnight gaps

A stop-loss rule is a risk management tool whereby the investor predefines some condition that, upon being triggered by market dynamics, implies the liquidation of her outstanding position. Such a tool is widely used by practitioners in financial markets with the hope of improving their investment performance by cutting losses and consolidating gains. We analyze in this work the performance of four popular implementations of stop-loss rules applied to asset prices whose returns are modeled with consideration of overnight gaps, that is, jumps from the closing price of one day to the opening price of the next trading day. In addition, our models include acute momentary price drops (flash crashes), which are often believed to erode the performance gains that might be derived from stop-loss rules. For this analysis we consider different models of asset returns: random walk, autoregressive and regime-switching models. In addition, we test the performance of the considered stop-loss rules in a non-parametric, data-driven framework based on the stationary bootstrap. As a general conclusion we find that, even when including overnight gaps and flash crashes in our price models, in rising markets stop-loss rules improve the expected risk-adjusted return according to most metrics, while improving absolute expected return in falling markets. Furthermore, we find that in general the simple fixed percentage stop-loss rule may be, in risk-adjusted terms, the most powerful among the popular rules that this work considers.     

Approximation methods for piecewise deterministic Markov processes and their costs

In this paper, we analyse piecewise deterministic Markov processes (PDMPs), as introduced in Davis (1984). Many models in insurance mathematics can be formulated in terms of the general concept of PDMPs. There one is interested in computing certain quantities of interest such as the probability of ruin or the value of an insurance company. Instead of explicitly solving the related integro-(partial) differential equation (an approach which can only be used in few special cases), we adapt the problem in a manner that allows us to apply deterministic numerical integration algorithms such as quasi-Monte Carlo rules; this is in contrast to applying random integration algorithms such as Monte Carlo. To this end, we reformulate a general cost functional as a fixed point of a particular integral operator, which allows for iterative approximation of the functional. Furthermore, we introduce a smoothing technique which is applied to the integrands involved, in order to use error bounds for deterministic cubature rules. We prove a convergence result for our PDMPs approximation, which is of independent interest as it justifies phase-type approximations on the process level. We illustrate the smoothing technique for a risk-theoretic example, and compare deterministic and Monte Carlo integration.     

Automated Induction of Rule-based Neural Networks from Databases

This paper describes our approach to the problem of automated knowledge acquisition from large databases of examples using an information-theoretic approach. Our previous research has resulted in practical algorithms (ITRULE) for the automatic induction of rules from large example databases. Utilizing these algorithms, the raw data can be transformed into a set of human readable IF THEN rules, thus giving insight into the knowledge hidden within the data. These rules can then be automatically loaded into an expert system shell. Alternatively, they can be used to build a new type of parallel inference system—a rule-based neural network. This process enables a prototype expert system to be automatically generated and up and running in a matter of minutes, compared with months using a manual knowledge-acquisition approach. The resulting expert system can then be used as a sophisticated search and analysis tool to query the original database capable of reasoning with uncertain and incomplete data.     

Learning to profit with discrete investment rules

The learning of optimal discrete investment rules is analysed and related to the problem of forecasting financial returns. The aim is twofold: to characterize some 'good' learning methods for agents using investment rules of this form and to explain why many observed investment rules such as technical trading rules are discrete. A consistent estimator for discrete investment rules is used and it is shown, using simulations, that direct estimation of investment rules is preferable to the estimation of forecasting models to be used in such rules. This model and the associated results indicate there are a number of reasons why it may be easier to learn a good discrete investment rule than to learn a continuous rule; this provides a partial explanation of why discrete investment rules are used so widely.     

Do organizations adopt sophisticated capital budgeting practices to deal with uncertainty in the investment decision?: A research note

This study examines the impact of uncertainty on the sophistication of capital budgeting practices. While the theoretical applications of sophisticated capital budgeting practices (defined as the use of real option reasoning and/or game theory decision rules) have been well documented, empirical evidence on the factors that affect the importance and use of these sophisticated capital budgeting practices is scarce. I investigate the relation between specific uncertainties and sophisticated capital budgeting practices in 189 Dutch organizations. The empirical results show that sophisticated capital budgeting involves the use of multiple tools and procedures (such as Monte Carlo simulations, certainty equivalents, Game Theory decision rules and Real Option Reasoning). An increase in financial uncertainty is associated with the use and importance of sophisticated capital budgeting practices. Finally, size and industry are also related to the use and importance of sophisticated capital budgeting practices.     

Considerations on the subject of lease accounting

The question of the correct method for recording lease transactions has already been the subject of debate both domestically and internationally for some time now (2009). The discussion on a set of rules for recording such operations has recently been fuelled by the inclusion in the FASB and IASB's agendas of a joint project regarding accounting rules for both the lessor and the lessee. To this very moment the preliminary output of this joint project has been the drafting of a discussion paper published on 19 March 2009. The present work offers a critical commentary on the main innovations introduced by the boards on the subject of lease accounting and illustrates an alternative accounting model which, starting with the identification of the essential economic elements of any lease contract, would be best suited to representing, in accounting terms, the reasoning and the actual purpose of a lease transaction.     

Causal reasoning in financial reporting and voluntary disclosure

Causal reasoning involves understanding the cause of events that have already happened (i.e., diagnosis) as well as predicting which future events will occur (i.e., prediction). Although this type of reasoning is an important part of financial reporting and voluntary disclosure, very little research has relied on it as a basis for developing and interpreting testable research ideas. The purpose of this paper is twofold: First, we review key theories from psychology that pertain to causal reasoning. Second, we identify how these theories can be successfully used by behavioral researchers interested in financial reporting and voluntary disclosure.     

Discussion of “The robustness of the Sabarnes Oxley effect on the U.S. capital market”

In this paper, I use anecdotal evidence and logical reasoning to suggest that, despite the use of an extensive database, it is not possible to conclude that passage of the Sarbanes Oxley Act did not have an impact on companies’ delisting decisions. Moreover, the instrumental variables used to proxy for SOX effects are too weak and suffer from a significant endogeneity problem given that passage of SOX was driven by many of the economic and control problems that are used to control for market and company factors. I also discuss some broader issues about the trade-off between large sample statistical inference and anecdotal analysis for addressing practical questions.     

A European view on the legitimacy of accounting procedures: Towards a deliberative-accountability framework for analysis

Accounting rules affect fundamental areas of social interaction encompassing groups that have diverse and conflicting interests regarding financial reporting. In the absence of a coherent social choice theory, concepts of legitimacy can be used to assess the acceptance of accounting standard-setting processes and their resulting norms. In this paper, we analyze the standard-setting process in Europe. Accounting rules in Europe are developed in a two-stage process involving both private standard-setting and public rule-making. From a structural perspective, the European Union (EU) is well positioned to develop legitimate accounting procedures. However, the original purpose and the ensuing legitimacy of its control mechanism are jeopardized when EU structures are used and sometimes abused for policy formation and the creation of EU-IFRS.