In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.

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DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.

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Credit Card Fraud Detection Using AdaBoost and Majority Voting

An analysis of DEMATEL approaches for criteria interaction handling within ANP.A classification of the methods related to criteria interaction phenomenon in MADM.Detailed explanations and numerical examples are given.Bibliometric analysis is provided. Majority of the Multiple-Attribute Decision Making (MADM) methods assume that the criteria are independent of each other, which is not a realistic assumption in many real world problems. Several forms of interactions among criteria might occur in real life situations so that more sophisticated/intelligent techniques are required to deal with particular needs of the problem under consideration. Unfortunately, criteria interaction concept is very little issued in the literature. It is still a very important and critical research subject for intelligent decision making within MADM. The present paper aims to put a step forward to fill this gap by depicting the general picture, which provides a classification of methods related to criteria interaction phenomenon, and discuss/review the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) hybridizations first time in the literature. DEMATEL and ANP hybridizations grab remarkable attention of decision analysis community in recent years and seem as one of the most promising approaches to handle criteria interactions in a MADM setting.

An analysis of DEMATEL approaches for criteria interaction handling within ANP

An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey

The development of high-tech industry has been prosperous around the world in past decades, while technology and finance have already become the most significant issues in the information era. While high-tech firms are a major force behind a country's economic development, it requires a lot of money for the development process, as well as the financing difficulties for its potential problems, thus, how to evaluate and establish appropriate technology and financial services platforms innovation strategy has become one of the most critical and difficult issues. Moreover, how the chosen intertwined financial environment can be optimized in order that high-tech firms financing problems can be decided has seldom been addressed. Thus, this research aims to establish a technology and financial services platform innovation strategy improvement model, as based on the hybrid MADM model, which addresses the main causal factors and amended priorities in order to strengthen ongoing planning. A DEMATEL technique, as based on Analytic Network Process, as well as modified VIKOR, will be proposed for selecting and re-configuring the aspired technology and financial services platform. An empirical study, as based on China's technology and financial services platform innovation strategy, will be provided for verifying the effectiveness of this proposed methodology. Based on expert interviews, technology and financial services platforms innovation strategy improvement should be made in the following order: credit guarantee platform (C)_credit rating platform (B)_investment and finance platform (A).

/pdf/evaluating-the-enhancement-and-improvement-of-china-s-z0990wfvh8.pdf

Evaluating the enhancement and improvement of China's technology and financial services platform innovation strategy

https://link.springer.com/content/pdf/10.1007/s10799-021-00342-8.pdf

Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model

This study proposed a novel hybrid mechanism for corporate operating assessment and then establishes a fusion architecture that integrates two-level data envelopment analysis (two-level DEA), incremental filter wrapper feature selection technique (IFWFS), and fuzzy support vector machine (FSVM) for performance forecasting. The two-level DEA is executed to deal with the problem encountered by one-level DEA (i.e., original DEA) and further enhances its discriminant ability. Sequentially, the IFWFS is applied to determine the most essential feature subsets as well as decreases the calculation complexities and storage requirements. The selected important features were fed into FSVM (one of the kernel-based mechanisms) to construct the forecasting model. The proposed model has been examined by real-life cases and is a promising alternative for corporate risk management. The decision makers can take this mechanism as a decision support tool to modify their investment portfolios as well as increase their profit margin under this fluctuating business atmosphere.

Management decision by combination two-level DEA and kernel-based mechanism

This study discusses the key factors of financial distress warning models for companies using corporate governance variables and financial ratios as the research variables, sieving out influential variables based on the attribute simplification process of rough set theory (RST). Then, we construct some classification models for diagnosing enterprise distress based on RST, using a data mining technique of decision trees with the selected indicators and variables. The empirical results obtained from analysis of enterprise distress indicators, show that financial distress is not only affected by the traditional financial ratios, but also by corporate governance variables. In addition, enterprise distress diagnosis models constructed based on RST and decision trees can effectively diagnose firms in times of crisis. In particular, the RST models are more accurate. This study provides a reference for better understanding the symptoms that might lead to a company’s financial crisis in advance and thus provide a valuable reference for investment decision making by stakeholders.

Using Rough Set Theory and Decision Trees to Diagnose Enterprise Distress – Consideration of Corporate Governance Variables

This study examines the impact of corporate social responsibility (CSR) news reports on corporate operating performance forecasting using a large database of publicly-listed electronics firms in Taiwan. By executing latent topic modelling, we can break down CSR news reports into multiple dimensions and then examine which dimension(s) affects operating performance. To offer decision-makers with a comprehensive, overarching view of the corporate’s operations, the data envelopment analysis (DEA), that can handle multiple input and multiple output variables simultaneously, was conducted to generate the performance rank. The analyzed results were then fed into support vector machine (SVM) to construct the model for performance forecasting. The inherent parameters were decided by harmony search (HS) algorithm. The proposed approach, supported by real samples, can assist both internal and external stakeholders in allocating scarce resources to specific CSR dimensions to enhance a corporate’s growth potential as well as to achieve a win-win situation.

Fu-Hsiang Chen

Papers

Evaluating the enhancement and improvement of China's technology and financial services platform innovation strategy

Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model

Management decision by combination two-level DEA and kernel-based mechanism

Using Rough Set Theory and Decision Trees to Diagnose Enterprise Distress – Consideration of Corporate Governance Variables

The Impact of CSR's Multi-dimensions on Performance Assessment: A Joint Utilization of Latent Topic Modelling and Support Vector Machine