Michalis Doumpos

A Multicriteria Approach to Assess Banking Performance: The Case of Greece

The Greek banking system in the last years has become a system of thoroughly changes. In this new environment the Greek commercial banks should be adapted and develop new strategy and inclinations, in order to satisfy depositor demands and increase their security and profitability. A multicriteria approach is proposed for evaluating banking performance over multiple criteria. Firstly, a multivariate statistical analysis is applied to select the most important financial ratios, which derive from the study of financial statements of a sample of Greek commercial banks. Then, the multicriteria method PROMETHEE is applied to rank the banks according to their financial performance during the period 1995–1999.

Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics

This study examines the development of corporate failure prediction models for European firms in the energy sector, using a large dataset from 18 countries. The construction of the models is based on a multiple criteria decision aid (MCDA) approach taking into account both ordinal criteria and nominal country-sector effects. The analysis is based on different modeling specifications. First, traditional financial variables are examined, which are then extended with additional country-level data related to the economic and business environment, as well as data about the energy efficiency policies of the countries and the characteristics of their energy markets and networks. The results indicate that energy-related attributes have high discriminating power and add valuable information compared to the other attributes.

Applications to Corporate Default Prediction and Consumer Credit

This chapter illustrates the application of analytical predictive and descriptive techniques for credit risk assessment. To this end, two case applications are presented using data sets involving corporate defaults and credit card loans. The first part is devoted to the prediction of corporate defaults. A data set of 13,414 European small and medium-sized manufacturing enterprises (SMEs) from six countries is considered during the period 2009–2011. The information available for the firms involves their financial characteristics. Corporate default prediction models are constructed with statistical, machine learning, and multicriteria decision making techniques. The analysis of the results covers both the predictive performance of the models, as well as the insights that they provide regarding the factors that affect the default risk for European SMEs. In the second part, a descriptive multivariate clustering approach is employed to obtain analyze credit card loan applications. A publicly available data set of 30,000 cases is analyzed with the k-medoids algorithm to identify clusters of borrowers having similar characteristics. The results are discussed in terms of the common features of the clusters and their level of credit risk.

Data Analytics for Developing and Validating Credit Models

The development of credit risk assessment models in the context of credit scoring and rating, is a data-intensive task that involves a considerable level of sophistication in terms of data preparation, analysis, and modeling. From a data analytics perspective, the construction of credit scoring and rating models can be considered as a classification task, that requires the development of models differentiating the borrowers by their level of credit risk. The model fitting process can be implemented with various methodological approaches, based on different types of models, model fitting criteria, and estimation procedures. This chapter presents an overview of different analytical modeling techniques from various fields, such as statistical models (naive Bayes classifier, discriminant analysis, logistic regression), machine learning (classification trees, neural networks, ensembles), and multicriteria decision aid (value function models and outranking models). Moreover, performance measurement issues are discussed, focusing on the presentation of various popular metrics for evaluating the predictive power and information value of credit scoring and rating models.

Credit Scoring and Rating

Credit scoring usually refers to models and systems that provide a numerical credit score for each borrower, mostly for internal use by financial institutions and corporate clients. Credit ratings provide risk classifications for corporate loans, bond issues, and countries (e.g., sovereign credit ratings). This chapter describes the basic characteristics of both schemes. The presentation begins with a discussion of the different contexts of scoring and rating (through the cycle and point in time assessments, issuer ratings and issue-specific ratings, behavioral—profit scoring and social lending). Then, the main modeling requirements are outlined, and the model development process is explained. The chapter closes with a brief discussion of the credit rating industry, focusing on the major credit rating agencies (CRAs), who play a crucial role due to the globalization of the financial markets and the wide range of debt issues, which pose challenges to their monitoring and risk assessment for investors, financial institutions, supervisors, and other stakeholders.

Conclusions and Future Research

Credit risk measurement and management is an active area of research that combines elements from various disciplines. As new forms of credit gain ground (e.g., from traditional corporate and consumer loans to crowdfunding, social lending, etc.) and tighter regulatory requirements are imposed (Basel accords and new reporting and accounting standards such as IFRS 9), new opportunities and challenges arise for practitioners and researchers.

Introduction to Credit Risk Modeling and Assessment

Credit is a fundamental tool for financial transactions in the private and public sector, providing the liquidity needed for all forms of economic activity for consumers and corporate activities. This chapter sets the basis for understanding the concepts and aspects of credit risk management and the current practices in this field. The discussion starts with a presentation of the recent trends in credit provision, and an outline of the regulatory framework. Then, some fundamental factors that create uncertainties are outlined, and the main elements of credit risk modeling are identified, namely the estimation of the probability of default, loss given default, and exposure at default. The requirements set by the Basel Capital Accords regarding these elements are discussed and different modeling schemes are outlined, including judgmental approaches, data-driven empirical models, and financial models. The chapter closes with some financial measures for assessing the loan profitability, such as risk-adjusted return on capital.

Multiattribute Assessment of the Financial Performance of Non-life Insurance Companies: Empirical Evidence from Europe

The European insurance market has undergone major changes over the past couple of decades, which have created new opportunities but also a lot of challenges and threats for insurers in Europe. In this study, we focus on non-life insurance companies in Europe, over the period 2000–2012, and employ a data-driven multidimensional approach to assess their financial performance, taking into account profitability, solvency, and operating performance indicators. The assessment isolates country-specific effects and, through a second-stage explanatory analysis, we examine the impact of country differences with respect to their economic status and the features of their insurance markets.

Teaching OR Courses to Satisfy Educational and Professional Needs in an Engineering School

Operations research (OR) analysts are highly skilled professionals responsible for one or more aspects of problem-solving. According the U.S. Bureau of Labor Statistics, a significant increase is expected regarding the job opportunities for OR professionals over the next few years. The necessary key skills for such professionals include: analytical skills, verbal communication, mathematical, problem-solving, interpersonal, critical thinking and written communication skills, and knowledge of modeling software. In this chapter, we first discuss our experience in a Greek engineering school for more than 20 years as tutors of OR courses such as, linear programming, decision support systems, and decision science, in comparison to international practice. Subsequently, we present the results during a three-year (2009–2011) pilot effort to enhance the students’ level of scientific knowledge along with their communication (writing and speaking) and team-working skills. The results of the pilot effort have been very encouraging.

Disaggregation Approaches for Multicriteria Classification: An Overview

Multicriteria classification problems have been an very active area of research in MCDA for more than two decades. Such problems refer to the assignment of a given set of alternatives into predefined categories/classes. Preference disaggregation approaches provide a valuable basis for facilitating the construction of multicriteria classification models using a data-driven process. In this chapter, we provide an overview of the preference disaggregation techniques in multicriteria classification, covering the existing types of decision models, the approaches used for model inference, as well as robustness issues.