Credit risk analytics with R / (Record no. 240912)
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| 000 -LEADER | |
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| fixed length control field | 06377nam a22002537a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260305143110.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260216b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789357461559 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | AIMIT LIBRARY |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | 1 |
| Classification number | 658.4038011 |
| Item number | AROR |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Arora, R K. |
| 9 (RLIN) | 255074 |
| 245 ## - TITLE STATEMENT | |
| Title | Credit risk analytics with R / |
| Statement of responsibility, etc. | By R K Arora and Prerna Lal. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi : |
| Name of publisher, distributor, etc. | Wiley , |
| Date of publication, distribution, etc. | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxi,440p. ; |
| Other physical details | PB |
| Dimensions | 24 cm |
| 500 ## - GENERAL NOTE | |
| General note | Credit risk analytics is a set of tools and techniques that enable lenders to take credit decisions and estimate the credit risk by predicting the credit behaviour of potential borrowers. Beginning with the fundamental concepts of credit risk analytics, this book offers in-depth insight into credit scoring models, probability of default (discrete time models and continuous time models) and modelling (exposures, recoveries, default correlations, and counterparty risk). Adopting a balanced strategy combining theoretical explanation and practical applications, the book demonstrates how you can build credit risk models using R and apply them into practice. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Preface<br/><br/>About the Authors<br/><br/>Chapter 1 Credit Risk Analytics<br/><br/>1.1 Introduction<br/><br/>1.2 Credit Risk<br/><br/>1.3 Credit Risk Analytics<br/><br/>1.4 Factors Driving Use of Credit Risk Analytics<br/><br/>1.5 Factors Affecting Credit Risk<br/><br/>1.6 Benefits of Credit Risk Analytics<br/><br/>1.7 Credit Risk Analytics Software<br/><br/>1.8 Application of Credit Risk Analytics to Credit Scoring<br/><br/>1.9 Current Challenges in Credit Risk Analytics<br/><br/>1.10 Career Options in Credit Risk Analytics<br/><br/>Chapter 2 Credit Scoring Models<br/><br/>2.1 Introduction<br/><br/>2.2 Techniques to Build Scorecards<br/><br/>2.3 Decision Trees<br/><br/>2.4 Logistic Regression versus Decision Trees<br/><br/>2.5 Other Classification Techniques<br/><br/>2.6 Credit Scoring for Retail Exposures<br/><br/>2.7 Credit Scoring for Non-Retail Exposures<br/><br/>2.8 Role of Big Data<br/><br/>2.9 Overruling of Scorecard<br/><br/>2.10 Applications of Credit Scoring<br/><br/>2.11 Limitations of Credit Scoring<br/><br/>2.12 Evaluation of a Scoring Model<br/><br/>Chapter 3 Probability of Default: Discrete Time Models<br/><br/>3.1 Introduction<br/><br/>3.2 Default Events<br/><br/>3.3 Conditional and Unconditional Default<br/><br/>3.4 Hazard Rate<br/><br/>3.5 Cumulative Default Probability<br/><br/>3.6 Risk-Neutral and Real-World Probabilities<br/><br/>3.7 Calculation of Default Probability Using Historical Data<br/><br/>3.8 Option Theoretic Approach<br/><br/>3.9 Default Probability (DP) Models<br/><br/>3.10 Migration Probabilities<br/><br/>3.11 Term Structure of Default Probability<br/><br/>3.12 Basel Requirements<br/><br/>Chapter 4 Probability of Default: Continuous Time Hazard Models<br/><br/>4.1 Introduction<br/><br/>4.2 Survival Analysis<br/><br/>4.3 Life Table Models<br/><br/>4.4 Kaplan–Meier (KM) Analysis<br/><br/>4.5 Cox Proportional Hazard (CPH) Model<br/><br/>4.6 Accelerated Failure Time (AFT) Models<br/><br/>4.7 Discrete Time Hazard Models versus Continuous Time Hazard Models<br/><br/>Chapter 5 Modelling Exposures at Default<br/><br/>5.1 Introduction<br/><br/>5.2 Exposure at Default (EAD)<br/><br/>5.3 Counterparty Credit Exposure<br/><br/>5.4 Basel Guidelines<br/><br/>5.5 EAD Modelling<br/><br/>Chapter 6 Modelling Recoveries and Loss Given Default<br/><br/>6.1 Introduction<br/><br/>6.2 Measures of Recovery<br/><br/>6.3 Determination of Recovery Rates<br/><br/>6.4 Factors Affecting Recovery Rates<br/><br/>6.5 Stochastic Recovery Rates<br/><br/>6.6 Estimation of LGD<br/><br/>6.7 Issues in Estimating LGD<br/><br/>6.8 Basel Guidelines<br/><br/>6.9 Sources of Recovery Data<br/><br/>Chapter 7 Modelling Credit Risk Correlations<br/><br/>7.1 Introduction<br/><br/>7.2 Sources of Dependence<br/><br/>7.3 Correlation Definition<br/><br/>7.4 Other Measures of Dependence<br/><br/>7.5 Factor Models of Correlation<br/><br/>7.6 Correlation Estimation by Credit Risk Models<br/><br/>7.7 Indirect and Direct Co-Dependence in Credit Risk Models<br/><br/>Chapter 8 Modelling Counterparty Credit Risk<br/><br/>8.1 Introduction<br/><br/>8.2 Credit Value Adjustment (CVA)<br/><br/>8.3 Expected Future Exposure in Interest Rate Swaps and Currency Swaps<br/><br/>8.4 Pricing CVA in Practice<br/><br/>8.5 Management of Counterparty Credit Risk<br/><br/>8.6 Counterparty Credit Risk Regulation<br/><br/>Chapter 9 Credit Value at Risk<br/><br/>9.1 Introduction<br/><br/>9.2 Exposure at Default<br/><br/>9.3 Loss Given Default<br/><br/>9.4 Credit Risk Correlations<br/><br/>9.5 Expected and Unexpected Loss<br/><br/>9.6 Credit Risk Models<br/><br/>9.7 Alternative Approaches<br/><br/>9.8 Basel Guidelines<br/><br/>9.9 Regulation under the Indian Law<br/><br/>Appendix A Exploratory Data Analysis<br/><br/>A.1 Introduction<br/><br/>A.2 Univariate Non-Graphical EDA<br/><br/>A.3 Univariate Graphical EDA<br/><br/>A.4 Multivariate Graphical EDA<br/><br/>A.5 Multivariate Non-Graphical EDA<br/><br/>A.6 Inferential Statistics<br/><br/>Appendix B Data Pre-Processing for Credit Risk Modelling<br/><br/>B.1 Sources of Data<br/><br/>B.2 Aggregation of Data<br/><br/>B.3 Sampling<br/><br/>B.4 Types of Data<br/><br/>B.5 Visual Exploration of Data<br/><br/>B.6 Descriptive Statistics<br/><br/>B.7 Dealing with Missing Values<br/><br/>B.8 Detection and Treatment of Outliers<br/><br/>B.9 Standardisation of Data<br/><br/>B.10 Categorisation<br/><br/>B.11 Coding Using Weights of Evidence<br/><br/>B.12 Variable Selection<br/><br/>B.13 Segmentation<br/><br/>B.14 Definition of Default<br/><br/>Appendix C Introduction to R<br/><br/>C.1 The R Environment<br/><br/>C.2 Download and Install R on Windows<br/><br/>C.3 Installing RStudio<br/><br/>C.4 Introduction to R Programming<br/><br/>C.5 Installing R Packages<br/><br/>Appendix D Guidance Note on Credit Risk Management<br/><br/>Chapter 1 Policy Framework<br/><br/>Chapter 2 Credit Rating Framework<br/><br/>Chapter 3 Credit Risk Models<br/><br/>Chapter 4 Portfolio Management and Risk Limits<br/><br/>Chapter 5 Managing Credit Risk in Inter-bank Exposure<br/><br/>Chapter 6 Credit Risk in Off-balance Sheet Exposures<br/><br/>Chapter 7 Country Risk<br/><br/>Chapter 8 Loan Review Mechanism/Credit Audit<br/><br/>Chapter 9 RAROC Pricing/Economic Profit<br/><br/>Chapter 10 New Capital Accord: Implications for Credit Risk Management |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Statement of responsibility | About the Author<br/>Ravinder Kumar Arora is a Professor of Finance and Accounting at International<br/><br/>Management Institute, New Delhi. He is also a fellow member of the Institute of Cost Accountants of India and the Institute of Company Secretaries of India. Dr Arora has around 23 years of experience in teaching Managerial Accounting, Corporate Finance, Project Finance, Risk Management, Cost Management |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Credit scoring models |
| 9 (RLIN) | 255075 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Modelling exposures at default |
| 9 (RLIN) | 255076 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Modelling recoveries and loss given default |
| 9 (RLIN) | 255077 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Lal, Prerna. |
| 9 (RLIN) | 255078 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Edition | 1st |
| Call number prefix | 658.4038011 AROR |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
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| Dewey Decimal Classification | MBA | St Aloysius Institute of Management & Information Technology | St Aloysius Institute of Management & Information Technology | Analytics | 02/12/2026 | SK Publishers & Distributors | 1059.00 | Bill.no:SKP4043;Bill.dt:2026/2/2 | 658.4038011 AROR | MBA15238 | 05/23/2026 | 794.25 | 02/16/2026 | Book |
