Neural network detection of management fraud using published financial data

This paper uses a Artificial Neural Network (AutoNet) to develop a model for detecting management fraud. The study offers an in-depth examination of important publicly available predictors of fraudulent financial statements. We find a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements. © 1998 John Wiley & Sons, Ltd.     

Hybrid performance evaluation of sustainable service and manufacturing supply chain management: An integrated approach of fuzzy dematel and fuzzy inference system

The aim of this paper is to propose a comprehensive framework for simultaneously measuring the performance of sustainable service and manufacturing supply chain management. Application of the proposed approach also results in reduced uncertainty of the performance measurement process caused by qualitative criteria evaluation. The proposed approach consists of two main steps. First, the fuzzy decision‐making trial and evaluation laboratory (DEMATEL) method has been used to determine important criteria by avoiding low influences; and then a Mamdani fuzzy inference system model has been adopted and applied for performance evaluation of sustainable supply chain management (SSCM). This model is employed in order to cope with the vagueness that exists in the SSCM performance investigation due to the vagueness intrinsic in the evaluation of criteria. In the proposed model, human reasoning has been modelled with fuzzy inference rules and has been set in the system, which is an advantage compared with those models in which fuzzy set theory and multicriteria decision‐making models are integrated. The proposed approach has been implemented in the pipe and fitting industry in order to highlight its application in real life. Sensitivity analysis has been carried out to determine the influence of service and manufacturing criteria on SSCM performance. The findings reveal that sustainable manufacturing criteria compared with sustainable service criteria have more effect on the performance of SSCM.     

A fuzzy logic-driven multiple knowledge integration framework for improving the performance of expert systems

To maintain a high performance in an ill-structured situation, expert systems should depend on multiple sources of knowledge rather than a single type. For this reason, we propose multiple knowledge integration by using a fuzzy logic-driven framework. Types of knowledge being considered here are threefold: machine, expert and user. Machine knowledge is obtained by a back- propagation neural network model from historical instances of a target problem domain. Expert knowledge is related to interpreting the trends of external factors that seem to affect the target problem domain. User knowledge represents a user’s personal views about information given by both expert knowledge and machine knowledge. The target problem domain of this paper is one-week-ahead stock market stage prediction: Bull, Edged-up, Edged-down, and Bear. Extensive experiments with real data proved that the proposed fuzzy logic-driven framework for multiple knowledge integration can contribute significantly to improving the performance of expert systems. Copyright © 1998 John Wiley & Sons, Ltd.     

风险管控系统建设评价

风险管控评价是目前国内外理论界与实务界关注的重点,而关注焦点集中于风险管控系统运行期间的评价,对风险管控系统建设阶段的评价则鲜有涉及。本文以风险管控系统建设过程中的成本、进度和质量为对象,探讨如何构建和使用风险管控系统建设评价,为完善风险管控系统评价体系做有益的补充和尝试。     

舞弊治理：基于上市公司财务舞弊特征的分析

选取2003～2007年度5645家沪深两地上市公司为样本,首先对其中209家舞弊公司财务舞弊的影响因素做统计分析,明晰上市公司财务舞弊的特征;然后,根据舞弊公司不同特征进一步实证检验,得出大规模事务所的选择、股权集中度、公司规模与财务舞弊负相关,独立董事比例与财务舞弊成"U"型关系,但没有发现代表财务状况指标净资产收益率的显著性影响,究其原因,发现舞弊公司存在虚增利润以外的其他更多隐性盈余操纵手段.     

A practical road map for assessing cyber risk

The increase in interconnectivity and developments in technology have caused cyber security to become a universal concern. This paper highlights the dangers of the evolution of cyber risk, the challenges of quantifying the impact of cyber-attacks and the feasibility of the traditional actuarial methodologies for quantifying cyber losses. In this paper, we present a practical roadmap for assessing cyber risk, a roadmap that emphasizes the importance of developing a company and culture-specific risk and resilience model. We develop a structure for a Bayesian network to model the financial loss as a function of the key drivers of risk and resilience. We use qualitative scorecard assessment to determine the level of cyber risk exposure and evaluate the effectiveness of resilience efforts in the organization. We highlight the importance of capitalizing on the knowledge of experts within the organization and discuss methods for aggregating multiple assessments. From an enterprise risk management perspective, impact on value should be the primary concern of managers. This paper uses a value-centric/reputational approach to risk management rather than a regulatory/capital-centric approach to risk.     

A multi-factor model for the risk management of portfolios

We propose a methodology for modelling the value at risk of a complex portfolio, based on an extension of the Ho, Stapleton and Subrahmanyam technique. We model the variance-covariance structure of up to seven variables. These could represent four country indices and three exchange rates, for example. In addition, the effect of an arbitrary number of orthogonal factors can be analysed. The system is illustrated by estimating the value at risk for a portfolio of international stocks where the factors are stock market indices and exchange rates, a portfolio of international bonds where the factors are interest rates as well as exchange rates, and a portfolio of interest rate derivatives in different currencies. In this last case, we model a two-factor term structure of interest rates in each of the currencies, valuing the derivatives at a future date using these term structures and the Black model. The model is applied for different fineness of the binomial density and computational accuracy and efficiency are estimated.
G13, G15, G21     

Work-from-home and the risk of securities misconduct

In the wake of the global pandemic, a challenge for CEOs and boards is to set a stakeholder-acceptable organizational balance between remote and traditional office working. However, the risks of work-from-home are not yet fully understood. We describe competing theories that predict the effect on misconduct of a corporate shift to work-from-home. Using internal bank data on securities traders we exploit lockdown variation induced by emergency regulation of the Covid-19 pandemic. Our difference-in-differences analysis reveals that working from home lowers the likelihood of securities misconduct; ultimately those working from home exhibit fewer misconduct alerts. The economic significance of these changes is large. Our study makes an important step toward understanding the link between the balance of work locations and the risk that comes with this tradeoff.     

Can alert models for fraud protect the elderly clients of a financial institution?

ABSTRACT

Using account-level transaction data at a major financial institution, we predict the incidence of suspicious activity that can be related to the external financial fraud of its elderly clients. The data consists of over 5 million accounts of clients aged 70 years and older, and over 250 million transactions extending from January 2015 to August 2016. Our main focus is to improve the detection of alerts within a proprietorial transaction monitoring system. Using logistic regression, random forest and support vector machine learning techniques, together with corrections for imbalanced alert samples, we provide a new alert model for the protection of elderly clients at a financial institution, with out-of-sample predictive accuracy. Our findings show the relative influence of client traits and account activity in our select external fraud alert models.     

A proposed enterprise risk management model for health organizations

Health care organizations are environments with high management complexity and subject to a constant exposure to risks. Enterprise risk management (ERM) has been studied and applied in different economic environments with the aim of improving organizational performance. However, the health sector still suffers from a lack of attention in this context, in particular with regard to the need for a high degree of financial transparency and for the establishment of process-orientated management, and this provides the motivation for the study described in this paper. An ERM model for health organizations is proposed, based on a systematic literature review and on seven case studies in Brazilian hospitals. An approach to economic risk assessment using indicators such as the cash flow at risk and the variability of costs and receipts from the proposed model is suggested. The health organizations involved in the case studies all interpret ERM as a source of information contributing to corporate governance, and the indicators listed provide constructive data for improvement-driven decision-making. Given the interest expressed by the organizations involved, further application and validation of the proposed model in subsequent studies is suggested.     

A validated expert system for decision making in corporate recovery

This paper describes INSOLVE—an expert system for corporate recovery decisions. INSOLVE was built to understand the decision-making processes of corporate recovery experts who deal with companies in financial difficulties. INSOLVE has been developed using a multi-phase process similar to that widely adopted in software engineering. The expert system is described in terms of the assessment task and interpretation models of CommonKADS. The detailed results of the validation of INSOLVE with 17 experts show that it is an accurate model of human expertise in this domain. Copyright © 1999 John Wiley & Sons, Ltd.     

Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures

The understanding of resilience is an emerging topic within the study of risks affecting distributed infrastructure systems. Although recent studies have explored the quantification of system resilience, there has been limited research aimed at understanding the role of multiple performance measures, spatiotemporal heterogeneities, and modeling uncertainties within the assessment of resilience and associated decision-making. Under real-world conditions, there is an increased burden on analysts for translating observed system data (including human and electronic sensor observations) into system performance estimates that may not be directly observable. This paper addresses these issues using a scenario-based risk modeling approach to understand: (1) resilience of complex systems, often in cases of hidden (not readily observable) measures of performance, (2) resilience sensitivity to modeling uncertainties in event and system characteristics, and (3) resilience sensitivity to the measurement of performance across multiple operational perspectives. The methods in this paper integrate uncertainty-driven risk and probabilistic modeling within a multi-state Markov-based approach. This study contributes to the state-of-the-art by developing methodologies for assessing community perceptions of infrastructure system resilience using observable factors and inferring possibly hidden performance measures for facilitating adaptive decision-support. The methods are demonstrated with hypothetical spatiotemporal data across multiple system performance dimensions. The analysis results are useful for infrastructure security analysts, system decision-makers, and the general public.     

商业银行内部风险管理体系及其职责

风险管理是商业银行的一项重要管理工作,从内部体系看,风险防控的三道防线是由经营部门、风险管理部门、内控合规部门和内审部门分别承担的,它们在风险防控上存在着交集,需要各司其职、明确分工,以提高风险管理效率。此外,各部门间的相互制约应当适度,风险管理部门的设置要计成本、讲效率。     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

A framework for assessing the systemic risk of major financial institutions

In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro–macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.     

基于战略风险管理模式下的养老保险公司风险管理分析

战略风险管理模式是金融企业比较流行的一种全面风险管理模型。本文在分析养老保险公司业务经营及风险管理特殊性的基础上,应用战略风险管理基本思想,提出了当前形势下养老保险公司风险管理的核心问题,并对公司目前面临的风险类别、风险因素以及风险管控措施进行了分析研究。     

A new approach to assessing model risk in high dimensions

A central problem for regulators and risk managers concerns the risk assessment of an aggregate portfolio defined as the sum of d individual dependent risks $X_i$. This problem is mainly a numerical issue once the joint distribution of $\left(X_1\text{,}{X}_2\text{,}\dots \text{,}{X}_d\right)$ is fully specified. Unfortunately, while the marginal distributions of the risks $X_i$ are often known, their interaction (dependence) is usually either unknown or only partially known, implying that any risk assessment of the portfolio is subject to model uncertainty.Previous academic research has focused on the maximum and minimum possible values of a given risk measure of the portfolio when only the marginal distributions are known. This approach leads to wide bounds, as all information on the dependence is ignored. In this paper, we integrate, in a natural way, available information on the multivariate dependence. We make use of the Rearrangement Algorithm (RA) of Embrechts et al. (2013) to provide bounds for the risk measure at hand. We observe that incorporating the information of a well-fitted multivariate model may, or may not, lead to much tighter bounds, a feature that also depends on the risk measure used. In particular, the risk of underestimating the Value-at-Risk at a very high confidence level (as used in Basel II) is typically significant, even if one knows the multivariate distribution almost completely.Our results make it possible to determine which risk measures can benefit from adding dependence information (i.e., leading to narrower bounds when used to assess portfolio risk) and, hence, to identify those situations for which it would be meaningful to develop accurate multivariate models.     

An inductive reasoning approach for building system safety risk models of aviation accidents

An inductive reasoning approach is employed to develop a prototype hybrid decision support tool whose main objective is to build probabilistic causal models representing the safety risk involved in aviation accidents. In this context, 15 aircraft accidents representative of five major accident types are selected to build an initial seed for the case‐base of the prototype tool. Consequently, within each individual accident model, main clusters of causal factors are identified for inclusion in the initial seed, thereby improving, both quantitatively and qualitatively, the case‐base of the prototype tool. A new methodology developed specifically for indexing aviation accidents into databases is used for indexing the initial seed into the case‐base of the tool. The resulting product is a highly customized conversational decision support tool that provides solution possibilities in the form of probabilistic causal models of accident scenarios retrieved and ranked according to their similarity to the current accident that the intended user investigates.     

Tax risk management and the multinational enterprise

The financial scandals in the United States and other countries ushered in financial reporting and corporate governance reforms that extend beyond the U.S. Sarbanes-Oxley Act of 2002 (SOX). These initiatives have increased the international financial community's awareness of the importance of risk management and internal controls. Tax risk management and related internal controls have been accorded less focus than risk management generally. The purpose of this research is to describe the current state of tax risk management of multinational enterprises (MNEs) by reporting survey responses from chief financial officers (CFOs) of U.S. and non-U.S. MNEs. The research shows that significant progress has been made by large MNEs in developing and implementing both general and tax risk management policies. The results provide guidance in identifying the loci and impact of organizational tax risk and indicate that respondents do not perceive alarming degrees of tax risk in their organizations. The study reveals a remarkable degree of similarity in U.S. and foreign firm responses and demonstrates, unexpectedly, that existing reporting structures enable CFOs to shift a significant degree of tax risk management to heads of tax.