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Author: Michalis Doumpos
Publisher: Springer
ISBN: 3319994115
Category : Business & Economics
Languages : en
Pages : 115
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Book Description
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.
Author: Michalis Doumpos
Publisher: Springer
ISBN: 3319994115
Category : Business & Economics
Languages : en
Pages : 115
Get Book Here
Book Description
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.
Author: Bart Baesens
Publisher: John Wiley & Sons
ISBN: 1119143985
Category : Business & Economics
Languages : en
Pages : 517
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Book Description
The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
Author: Krzysztof Jajuga
Publisher: Springer Nature
ISBN: 3030430782
Category : Business & Economics
Languages : en
Pages : 246
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Book Description
This volume features a selection of contributions presented at the 2019 Wroclaw Conference in Finance, covering a wide range of topics in finance and financial economics, e.g. financial markets; monetary policy; corporate, personal and public finance; and risk management and insurance. Reflecting the diversity and richness of research in the field, the papers discuss both fundamental and applied finance, and offer a detailed analysis of current financial-market problems, including specifics of the Polish and Central European markets. They also examine the results of advanced financial modeling. Accordingly, the proceedings offer a valuable resource for researchers at universities and policy institutions, as well as graduate students and practitioners in economics and finance at both private and government organizations.
Author: Vanessa Le Leslé
Publisher: International Monetary Fund
ISBN: 1475502656
Category : Business & Economics
Languages : en
Pages : 50
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Book Description
In this paper, we provide an overview of the concerns surrounding the variations in the calculation of risk-weighted assets (RWAs) across banks and jurisdictions and how this might undermine the Basel III capital adequacy framework. We discuss the key drivers behind the differences in these calculations, drawing upon a sample of systemically important banks from Europe, North America, and Asia Pacific. We then discuss a range of policy options that could be explored to fix the actual and perceived problems with RWAs, and improve the use of risk-sensitive capital ratios.
Author: Clark R. Abrahams
Publisher: John Wiley & Sons
ISBN: 0470461683
Category : Business & Economics
Languages : en
Pages : 320
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Book Description
Credit Risk Assessment The New Lending System for Borrowers, Lenders, and Investors Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors equips you with an effective comprehensive credit assessment framework (CCAF) that can provide early warning of risk, thanks to its forward-looking analyses that do not rely on the premise that the past determines the future. Revealing how an existing credit underwriting system can be extended to embrace all relevant factors and business contexts in order to accurately classify credit risk and drive all transactions in a transparent manner, Credit Risk Assessment clearly lays out the facts. This well-timed book explores how your company can improve its current credit assessment system to balance risk and return and prevent future financial disruptions. Describing how a new and comprehensive lending framework can achieve more complete and accurate credit risk assessment while improving loan transparency, affordability, and performance, Credit Risk Assessment addresses: How a CCAF connects borrowers, lenders, and investors with greater transparency The current financial crisis and its implications The root cause to weaknesses in loan underwriting practices and lending systems The main drivers that undermine borrowers, lenders, and investors Why a new generation of lending systems is needed Market requirements and how a comprehensive risk assessment framework can meet them The notion of an underwriting gap and how it affects the lenders' underwriting practices Typical issues associated with credit scoring models How improper use of credit scoring in underwriting underestimates the borrower's credit risk The ways in which the current lending system fails to address loan affordability How mortgage and capital market financial innovation relates to the crisis
Author: Majid Bazarbash
Publisher: International Monetary Fund
ISBN: 1498316034
Category : Business & Economics
Languages : en
Pages : 34
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Book Description
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Author: Tony Van Gestel
Publisher: Oxford University Press
ISBN: 0199545111
Category : Business & Economics
Languages : en
Pages : 552
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Book Description
This first of three volumes on credit risk management, providing a thorough introduction to financial risk management and modelling.
Author:
Publisher: Lulu.com
ISBN: 9291316695
Category : Bank capital
Languages : en
Pages : 294
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Book Description
Author: Bart Baesens
Publisher: John Wiley & Sons
ISBN: 1119278341
Category : Business & Economics
Languages : en
Pages : 516
Get Book Here
Book Description
The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
Author: Naoyuki Yoshino
Publisher: Routledge
ISBN: 0429684568
Category : Business & Economics
Languages : en
Pages : 354
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Book Description
There is limited access for small and medium-sized enterprises (SMEs) to bank credit. This book proposes new and sustainable models to help ease the access of SMEs to finance and boost economic growth and job creation in Asia. This book looks at the difficulties of SMEs in accessing finance and suggests ways on how to mitigate these challenges. It suggests how we can develop credit information infrastructures for SMEs to remedy the asymmetric information problem and to utilize credit rating techniques for the development of a sustainable credit guarantee scheme. The book provides illustrations of various Asian economies that implemented credit guarantee schemes and credit risk databases and is a useful reference for lessons and policy recommendations.
