Download or read book The Use of Logistic Regression in the Development of Credit Scoring Models written by Dwayne Addison and published by . This book was released on 1994 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Logistic Regression and Its Application in Credit Scoring written by Christine Bolton and published by . This book was released on 2009 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Credit Scoring and Its Applications Second Edition written by Lyn Thomas and published by SIAM. This book was released on 2017-08-16 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit Scoring and Its Applications is recognized as the bible of credit scoring. It contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific issues in bankruptcy, equal opportunities, and privacy legislation. It contains a discussion of economic theories of consumers' use of credit, and readers will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives. New to the second edition are lessons that can be learned for operations research model building from the global financial crisis, current applications of scoring, discussions on the Basel Accords and their requirements for scoring, new methods for scorecard building and new expanded sections on ways of measuring scorecard performance. And survival analysis for credit scoring. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.

Download or read book Developing Credit Scoring Models When Small Sample Sizes Are Available written by Vesarach Aumeboonsuke and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Making lending decision is an important process for financial institutions because it has a direct impact on the profits and losses of financial institutions. Therefore, financial Institutions try to develop good credit scoring models to make lending decisions. The purpose of this research is to compare the performance of the credit scoring models between multiple linear regression and logistic regression. The comparison of the credit scoring models is done through using three sets of population data generated through simulation. The odds ratio is adopted in this research as an evaluation tool. The findings of this research are useful for financial institutions especially commercial banks because they present the evidence of how well each credit scoring model can predict the credit score of the loan applicants.

Download or read book Specification and Informational Issues in Credit Scoring written by Nicholas M. Kiefer and published by . This book was released on 2004 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of "reject inference" -- methods that use a rejected applicant's characteristics, rather than loan performance data, in scoring model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model"--Abstract.

Download or read book Handbook of Credit Scoring written by Elizabeth Mays and published by Global Professional Publishi. This book was released on 1995-03 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: · Credit scoring is a vital and sometimes misunderstood tool in financial services · Evaluates the different systems available Bankers and lenders depend on credit scoring to determine the best credit risks--and ensure maximum profit and security from their loan portfolios. Handbook of Credit Scoring offers the insights of a select group of experts on credit scoring systems. Topics include: Scoring Applications, Generic and Customized Scoring Models, Using consumer credit information, Scorecard modelling with continuous vs. Classed variables, Basic scorecard Development and Validation, Going beyond Credit Score, Data mining, Scorecard collection strategies, project management for Credit Scoring

Download or read book Recent Methods from Statistics and Machine Learning for Credit Scoring written by Anne Kraus and published by Cuvillier Verlag. This book was released on 2014-07-08 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.

Download or read book Consumer Credit Scoring Models with Limited Data written by Maja Sustersic and published by . This book was released on 2007 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we design the neural network consumer credit scoring models for financial institutions where data usually used in previous research are not available. We use extensive primarily accounting data set on transactions and account balances of clients available in each financial institution. As many of these numerous variables are correlated and have very questionable information content, we considered the issue of variable selection and the selection of training and testing sub-sets crucial in developing efficient scoring models. We used a genetic algorithm for variable selection. In dividing performing and nonperforming loans into training and testing sub-sets we replicated the distribution on Kohonen artificial neural network, however, when evaluating the efficiency of models, we used k-fold cross-validation. We developed consumer credit scoring models with error back propagation artificial neural networks and checked their efficiency against models developed with logistic regression. Considering the dataset of questionable information content, the results were surprisingly good and one of the error back propagation artificial neural network models has shown the best results. We showed that our variable selection method is well suited for the addressed problem.

Download or read book Intelligent Credit Scoring written by Naeem Siddiqi and published by John Wiley & Sons. This book was released on 2017-01-10 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: A better development and implementation framework for credit risk scorecards Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data. Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include: Following a clear step by step framework for development, implementation, and beyond Lots of real life tips and hints on how to detect and fix data issues How to realise bigger ROI from credit scoring using internal resources Explore new trends and advances to get more out of the scorecard Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results.

Download or read book Credit Risk Scorecards written by Naeem Siddiqi and published by John Wiley & Sons. This book was released on 2012-06-29 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Credit Risk Scorecards "Scorecard development is important to retail financial services in terms of credit risk management, Basel II compliance, and marketing of credit products. Credit Risk Scorecards provides insight into professional practices in different stages of credit scorecard development, such as model building, validation, and implementation. The book should be compulsory reading for modern credit risk managers." —Michael C. S. Wong Associate Professor of Finance, City University of Hong Kong Hong Kong Regional Director, Global Association of Risk Professionals "Siddiqi offers a practical, step-by-step guide for developing and implementing successful credit scorecards. He relays the key steps in an ordered and simple-to-follow fashion. A 'must read' for anyone managing the development of a scorecard." —Jonathan G. Baum Chief Risk Officer, GE Consumer Finance, Europe "A comprehensive guide, not only for scorecard specialists but for all consumer credit professionals. The book provides the A-to-Z of scorecard development, implementation, and monitoring processes. This is an important read for all consumer-lending practitioners." —Satinder Ahluwalia Vice President and Head-Retail Credit, Mashreqbank, UAE "This practical text provides a strong foundation in the technical issues involved in building credit scoring models. This book will become required reading for all those working in this area." —J. Michael Hardin, PhD Professor of StatisticsDepartment of Information Systems, Statistics, and Management ScienceDirector, Institute of Business Intelligence "Mr. Siddiqi has captured the true essence of the credit risk practitioner's primary tool, the predictive scorecard. He has combined both art and science in demonstrating the critical advantages that scorecards achieve when employed in marketing, acquisition, account management, and recoveries. This text should be part of every risk manager's library." —Stephen D. Morris Director, Credit Risk, ING Bank of Canada

Download or read book Data Science for Economics and Finance written by Sergio Consoli and published by Springer Nature. This book was released on 2021 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Download or read book Developing Credit Scorecards Using Logistic Regression and Classification and Regression Trees written by and published by . This book was released on 2020 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit scoring -- Logistic regression -- Classification and regression and trees -- Model

Download or read book Readings in Credit Scoring written by L. C. Thomas and published by . This book was released on 2004 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit scoring is one of the most successful applications of statistical and management science techniques in finance in the last forty years. This unique collection of recent papers, with comments by experts in the field, provides excellent coverage of recent developments, advances and sims in credit scoring. Aimed at statisticians, economists, operational researchers and mathematicians working in both industry and academia, and to all working on credit scoring and data mining, it is an invaluable source of reference.

Download or read book Credit Risk Scorecards written by Mamdouh Refaat and published by . This book was released on 2011-03-15 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a systematic presentation of credit risk scorecard development and implementation. The text covers the theoretical foundations, the practical implementation and programming using SAS. The book topics include: - Data acquisition - data preparation - EDA, predictive measures and variable selection - Optimal segmentation and binning - Coarse classing and WOE transformations - Development of logistic regression models - Methods of model assessment and evaluation - Scorecard creation and scaling - Automatic generation of scoring code (SAS, SQL, C) - Scorecard monitoring and reporting - Reject inference The SAS implementation contains over 50 ready-to-use SAS macros that can be implemented in the automation of the scorecard creation process.

Download or read book Analytical Techniques in the Assessment of Credit Risk written by Michalis Doumpos and published by Springer. This book was released on 2018-09-29 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Fuzzy Partition Grade of Membership Scores as Covariates in Logistic Regression Credit Scoring Models written by David C. Draper and published by . This book was released on 1999 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: