Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2019 | Book

Introduction to Credit Risk Modeling and Assessment Read chapter Read first chapter

Analytical Techniques in the Assessment of Credit Risk

An Overview of Methodologies and Applications

Authors: Michalis Doumpos, Christos Lemonakis, Dimitrios Niklis, Constantin Zopounidis

Publisher: Springer International Publishing

Book Series : EURO Advanced Tutorials on Operational Research

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

This book provides a unique, focused introduction to the analytical skills, methods and techniques in the assessment of credit risk that are necessary to tackle and analyze complex credit problems. It employs models and techniques from operations research and management science to investigate more closely risk models for applications within the banking industry and in financial markets. Furthermore, the book presents the advances and trends in model development and validation for credit scoring/rating, the recent regulatory requirements and the current best practices. Using examples and fully worked case applications, the book is a valuable resource for advanced courses in financial risk management, but also helpful to researchers and professionals working in financial and business analytics, financial modeling, credit risk analysis, and decision science.

Table of Contents

Frontmatter
Chapter 1. Introduction to Credit Risk Modeling and Assessment
Abstract
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.
Michalis Doumpos, Christos Lemonakis, Dimitrios Niklis, Constantin Zopounidis
Chapter 2. Credit Scoring and Rating
Abstract
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.
Michalis Doumpos, Christos Lemonakis, Dimitrios Niklis, Constantin Zopounidis
Chapter 3. Data Analytics for Developing and Validating Credit Models
Abstract
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 (naïve 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.
Michalis Doumpos, Christos Lemonakis, Dimitrios Niklis, Constantin Zopounidis
Chapter 4. Applications to Corporate Default Prediction and Consumer Credit
Abstract
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.
Michalis Doumpos, Christos Lemonakis, Dimitrios Niklis, Constantin Zopounidis
Chapter 5. Conclusions and Future Research
Abstract
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.
Michalis Doumpos, Christos Lemonakis, Dimitrios Niklis, Constantin Zopounidis
Backmatter
Metadata
Title
Analytical Techniques in the Assessment of Credit Risk
Authors
Michalis Doumpos
Christos Lemonakis
Dimitrios Niklis
Constantin Zopounidis
Copyright Year
2019
Publisher
Springer International Publishing
Electronic ISBN
978-3-319-99411-6
Print ISBN
978-3-319-99410-9
DOI
https://doi.org/10.1007/978-3-319-99411-6