000			06377nam a22002537a 4500
005			20260305143110.0
008			260216b |||||||| |||| 00| 0 eng d
020			_a9789357461559
040			_cAIMIT LIBRARY
082			_21 _a658.4038011 _bAROR
100			_aArora, R K. _9255074
245			_aCredit risk analytics with R / _cBy R K Arora and Prerna Lal.
250			_a1st ed.
260			_aNew Delhi : _bWiley , _c2023.
300			_axxi,440p. ; _bPB _c24 cm
500			_aCredit 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			_aPreface About the Authors Chapter 1 Credit Risk Analytics 1.1 Introduction 1.2 Credit Risk 1.3 Credit Risk Analytics 1.4 Factors Driving Use of Credit Risk Analytics 1.5 Factors Affecting Credit Risk 1.6 Benefits of Credit Risk Analytics 1.7 Credit Risk Analytics Software 1.8 Application of Credit Risk Analytics to Credit Scoring 1.9 Current Challenges in Credit Risk Analytics 1.10 Career Options in Credit Risk Analytics Chapter 2 Credit Scoring Models 2.1 Introduction 2.2 Techniques to Build Scorecards 2.3 Decision Trees 2.4 Logistic Regression versus Decision Trees 2.5 Other Classification Techniques 2.6 Credit Scoring for Retail Exposures 2.7 Credit Scoring for Non-Retail Exposures 2.8 Role of Big Data 2.9 Overruling of Scorecard 2.10 Applications of Credit Scoring 2.11 Limitations of Credit Scoring 2.12 Evaluation of a Scoring Model Chapter 3 Probability of Default: Discrete Time Models 3.1 Introduction 3.2 Default Events 3.3 Conditional and Unconditional Default 3.4 Hazard Rate 3.5 Cumulative Default Probability 3.6 Risk-Neutral and Real-World Probabilities 3.7 Calculation of Default Probability Using Historical Data 3.8 Option Theoretic Approach 3.9 Default Probability (DP) Models 3.10 Migration Probabilities 3.11 Term Structure of Default Probability 3.12 Basel Requirements Chapter 4 Probability of Default: Continuous Time Hazard Models 4.1 Introduction 4.2 Survival Analysis 4.3 Life Table Models 4.4 Kaplan–Meier (KM) Analysis 4.5 Cox Proportional Hazard (CPH) Model 4.6 Accelerated Failure Time (AFT) Models 4.7 Discrete Time Hazard Models versus Continuous Time Hazard Models Chapter 5 Modelling Exposures at Default 5.1 Introduction 5.2 Exposure at Default (EAD) 5.3 Counterparty Credit Exposure 5.4 Basel Guidelines 5.5 EAD Modelling Chapter 6 Modelling Recoveries and Loss Given Default 6.1 Introduction 6.2 Measures of Recovery 6.3 Determination of Recovery Rates 6.4 Factors Affecting Recovery Rates 6.5 Stochastic Recovery Rates 6.6 Estimation of LGD 6.7 Issues in Estimating LGD 6.8 Basel Guidelines 6.9 Sources of Recovery Data Chapter 7 Modelling Credit Risk Correlations 7.1 Introduction 7.2 Sources of Dependence 7.3 Correlation Definition 7.4 Other Measures of Dependence 7.5 Factor Models of Correlation 7.6 Correlation Estimation by Credit Risk Models 7.7 Indirect and Direct Co-Dependence in Credit Risk Models Chapter 8 Modelling Counterparty Credit Risk 8.1 Introduction 8.2 Credit Value Adjustment (CVA) 8.3 Expected Future Exposure in Interest Rate Swaps and Currency Swaps 8.4 Pricing CVA in Practice 8.5 Management of Counterparty Credit Risk 8.6 Counterparty Credit Risk Regulation Chapter 9 Credit Value at Risk 9.1 Introduction 9.2 Exposure at Default 9.3 Loss Given Default 9.4 Credit Risk Correlations 9.5 Expected and Unexpected Loss 9.6 Credit Risk Models 9.7 Alternative Approaches 9.8 Basel Guidelines 9.9 Regulation under the Indian Law Appendix A Exploratory Data Analysis A.1 Introduction A.2 Univariate Non-Graphical EDA A.3 Univariate Graphical EDA A.4 Multivariate Graphical EDA A.5 Multivariate Non-Graphical EDA A.6 Inferential Statistics Appendix B Data Pre-Processing for Credit Risk Modelling B.1 Sources of Data B.2 Aggregation of Data B.3 Sampling B.4 Types of Data B.5 Visual Exploration of Data B.6 Descriptive Statistics B.7 Dealing with Missing Values B.8 Detection and Treatment of Outliers B.9 Standardisation of Data B.10 Categorisation B.11 Coding Using Weights of Evidence B.12 Variable Selection B.13 Segmentation B.14 Definition of Default Appendix C Introduction to R C.1 The R Environment C.2 Download and Install R on Windows C.3 Installing RStudio C.4 Introduction to R Programming C.5 Installing R Packages Appendix D Guidance Note on Credit Risk Management Chapter 1 Policy Framework Chapter 2 Credit Rating Framework Chapter 3 Credit Risk Models Chapter 4 Portfolio Management and Risk Limits Chapter 5 Managing Credit Risk in Inter-bank Exposure Chapter 6 Credit Risk in Off-balance Sheet Exposures Chapter 7 Country Risk Chapter 8 Loan Review Mechanism/Credit Audit Chapter 9 RAROC Pricing/Economic Profit Chapter 10 New Capital Accord: Implications for Credit Risk Management
505			_rAbout the Author Ravinder Kumar Arora is a Professor of Finance and Accounting at International 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			_aCredit scoring models _9255075
650			_aModelling exposures at default _9255076
650			_aModelling recoveries and loss given default _9255077
700			_aLal, Prerna. _9255078
942			_2ddc _cBK _e1st _k658.4038011 AROR
999			_c240912 _d240912