The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation PDF
Preview The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation
The Credit Scoring Toolkit This page intentionally left blank The Credit Scoring Toolkit Theory and Practice for Retail Credit Risk Management and Decision Automation Raymond Anderson 1 3 Great Clarendon Street, Oxford OX2 6DP Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York © Raymond Anderson 2007 The moral rights of the author have been asserted Database right Oxford University Press (maker) First published 2007 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this book in any other binding or cover and you must impose the same condition on any acquirer British Library Cataloguing in Publication Data Data available Library of Congress Cataloging in Publication Data Data available Typeset by Newgen Imaging Systems (P) Ltd., Chennai, India Printed in Great Britain on acid-free paper by Bath Press Ltd., UK. ISBN 978–0–19–922640–5 10 9 8 7 6 5 4 3 2 1 To Nofunisela Mirriam Nyikiza Housekeeper and Friend, for your many years of service This page intentionally left blank Contents List of figures xiii List of tables xv List of equations xix Preface xxiii Acknowledgments xxvii Outline xxix Module A Setting the scene 1 1 Credit scoring and the business 3 1.1 What is credit scoring? 3 1.2 Where is credit scoring used? 7 1.3 Why is credit scoring used? 14 1.4 How has credit scoring affected credit provision? 21 1.5 Summary 25 2 Credit micro-histories 27 2.1 History of credit 28 2.2 History of credit scoring 38 2.3 History of credit bureaux 44 2.4 History of credit rating agencies 51 2.5 Summary 53 3 The mechanics of credit scoring 55 3.1 What are scorecards? 55 3.2 What measures are used? 63 3.3 What is the scorecard development process? 73 3.4 What can affect the scorecards? 83 3.5 Summary 90 Module B Risky business 93 4 The theory of risk 95 4.1 The risk lexicon 95 4.2 Data and models 101 4.3 Conclusion 107 5 Decision science 109 5.1 Adaptive control 110 5.2 Be the master, not the slave 112 5.3 Summary 118 6 Assessing enterprise risk 121 6.1 Credit risk assessment 101 122 6.2 SME lending 128 6.3 Financial ratio scoring 132 6.4 Credit rating agencies 141 6.5 Modelling with forward-looking data 149 6.6 Conclusion 155 Module C Stats and maths 159 7 Predictive statistics 101 161 7.1 An overview of predictive modelling techniques 163 7.2 Parametric techniques 165 7.3 Non-parametric techniques 172 7.4 Critical assumptions 178 7.5 Results comparison 185 8 Measures of separation/divergence 187 8.1 Misclassification matrix 190 8.2 Kullback divergence measure 191 8.3 Kolmogorov–Smirnov (KS) 195 8.4 Correlation coefficients and equivalents 198 8.5 Chi-square (�2) tests 208 8.6 Accuracy tests 212 8.7 Summary 219 9 Odds and ends 223 9.1 Descriptive modelling techniques 223 9.2 Forecasting tools 225 9.3 Other concepts 231 9.4 Basic scorecard development reports 235 9.5 Conclusion 240 10 Minds and machines 243 10.1 People and projects 243 10.2 Software 250 10.3 Summary 252 Module D Data! 255 11 Data considerations and design 257 11.1 Transparency 257 11.2 Data quantity 259 viii Contents 11.3 Data quality 262 11.4 Data design 269 11.5 Summary 273 12 Data sources 275 12.1 Customer supplied 276 12.2 Internal systems 280 12.3 Credit bureaux data 284 12.4 Summary 296 13 Scoring structure 299 13.1 Customisation 299 13.2 Hosting—internal versus external 304 13.3 Integrating data 306 13.4 Credit risk scoring 310 13.5 Matching! 311 13.6 Summary 313 14 Information sharing 315 14.1 Credit registries 315 14.2 Do I or don’t I? 321 14.3 Summary 326 15 Data preparation 329 15.1 Data acquisition 329 15.2 Good/bad definition 335 15.3 Observation and outcome windows 344 15.4 Sample design 347 15.5 Summary 353 Module E Scorecard development 355 16 Transformation 357 16.1 Transformation methodologies 357 16.2 Classing 361 16.3 Use of statistical measures 367 16.4 Pooling algorithms 370 16.5 Practical cases 372 16.6 Summary 376 17 Characteristic selection 379 17.1 Considerations for inclusion 379 17.2 Statistical measures 382 17.3 Data reduction methods 384 17.4 Variable feed 389 17.5 Summary 393 18 Segmentation 395 18.1 Segmentation drivers 395 Contents ix 18.2 Identifying interactions 397 18.3 Addressing interactions 398 18.4 Summary 399 19 Reject inference 401 19.1 Why reject inference? 402 19.2 Population flows 403 19.3 Performance manipulation tools 406 19.4 Special categories 408 19.5 Reject inference methodologies 409 19.6 Summary 417 20 Scorecard calibration 419 20.1 Score banding 420 20.2 Linear shift and scaling 424 20.3 Reconstitution using linear programming 429 20.4 Summary 431 21 Validation 433 21.1 Components 435 21.2 Disparate impact 439 21.3 Summary 440 22 Development management issues 441 22.1 Scheduling 441 22.2 Streamlining 442 22.3 Summary 444 Module F Implementation and use 445 23 Implementation 447 23.1 Decision automation 447 23.2 Implementation and testing 452 23.3 Summary 456 24 Overrides, referrals, and controls 457 24.1 Policy rules 457 24.2 Overrides 458 24.3 Referrals 460 24.4 Controls 463 24.5 Summary 466 25 Monitoring 467 25.1 Portfolio analysis 469 25.2 Performance tracking 473 25.3 Drift reporting 480 25.4 Selection process 484 25.5 Summary 493 x Contents 26 Finance 495 26.1 Loss provisioning 495 26.2 Direct loss estimation 497 26.3 Loss component estimation 501 26.4 Scoring for profit 512 26.5 Risk-based pricing 519 26.6 Summary 525 Module G Credit Risk management cycle 527 27 Marketing 529 27.1 Advertising media 529 27.2 Two tribes go to war—quantity versus quality 530 27.3 Pre-screening 532 27.4 Data 534 27.5 Summary 536 28 Application processing 537 28.1 Gather—interested customer details 538 28.2 Sort—into strategy buckets 543 28.3 Action—accept or reject 546 28.4 Summary 551 29 Account management 553 29.1 Types of limits 554 29.2 Over-limit management 556 29.3 More limit and other functions 560 29.4 Summary 564 30 Collections and recoveries 567 30.1 Overview 567 30.2 Triggers and strategies 570 30.3 Scoring 572 30.4 Summary 575 31 Fraud 577 31.1 Types of fraud 579 31.2 Fraud detection tools 584 31.3 Fraud prevention strategies 586 31.4 Fraud scoring 588 31.5 Summary 591 Module H Regulatory environment 593 32 Regulatory concepts 595 32.1 Best practice 595 Contents xi 32.2 Good governance 596 32.3 Business ethics and social responsibility 598 32.4 Compliance hierarchy 600 32.5 Summary 601 33 Data privacy and protection 603 33.1 Background 603 33.2 Data privacy principles 610 33.3 Summary 619 34 Anti-discrimination 621 34.1 Discrimination—what does it mean? 621 34.2 Problematic characteristics 624 34.3 Summary 625 35 Fair lending 627 35.1 Predatory lending 628 35.2 Irresponsible lending 629 35.3 Responsible lending 631 35.4 Summary 632 36 Capital adequacy 635 36.1 Basel capital accord 1988 (Basel I) 637 36.2 New Basel capital accord 2004 (Basel II) 638 36.3 RWA calculation 644 36.4 Summary 647 37 Know Your customer (KYC) 649 37.1 Due diligence requirements 650 37.2 Customer identification requirements 651 38 National differences 653 38.1 United States of America 653 38.2 Canada 655 38.3 United Kingdom 656 38.4 Australia 658 38.5 Republic of South Africa (RSA) 659 Glossary 663 Bibliography 709 Appendices 721 Index 723 xii Contents List of figures 1. Module flow xxix E.1 Scorecard development process xliv G.1 Credit risk management cycle l 1.1 Scoring aspects 9 1.2 Historical data use 23 3.1 Bias and flat maximum 59 3.2 Bad rate by score 66 3.3 Cut-off strategies 66 3.4 Score distributions 68 3.5 Power and accuracy loss 68 3.6 Development process 74 3.7 Environmental drift 84 4.1 Risk linkages 96 4.2 Risky business 97 4.3 Ease of measurement 102 4.4 Risk assessment revolutions 103 4.5 Volumes and profits 104 5.1 RIsk strategies 110 5.2 Adaptive-control process 111 5.3 Optimisation 117 6.1 Default rates and mean reversion 143 6.2 Rating migration matrix 150 7.1 Linear regression 166 7.2 Logistic function 171 7.3 Decision tree 173 8.1 Misclassification graph 191 8.2 Weight of evidence 193 8.3 Population stability 195 8.4 Kolmogorov–Smirnov 196 8.5 Lorenz curve 203 8.6 ROC curve 207 8.7 Chi-square distribution 210 8.8 Degrees of freedom 210 9.1 Markov chain illustration 226 9.2 Monotonicity/classing 233 9.3 Strategy curve 239 10.1 Software strategies 251 11.1 Application/Behavioural Trade-off 267 13.1 Independent scores 306 13.2 Discrete scores 307 13.3 Integrated scores 308 13.4 Customer scoring 311 15.1 Outcome points 333 15.2 Data assembly—flow diagram 335 15.3 Good/bad definition 336 15.4 Application scoring—sample window 345 17.1 Stepping 390 18.1 Segmentation—classification tree 397 18.2 Segmentation—strategy curve comparison 399 19.1 Population flows 404 19.2 Bivariate visualisation 415 19.3 Bivariate inference process 416 20.1 Scorecard features 427 24.1 Credit policy/score matrix 458 25.1 Scorecard performance drift 473 25.2 Life cycle effect 477 25.3 New account effect 478 25.4 Score misalignment 479 25.5 Final decision and score overrides by score 491 25.6 Override reason codes by score 493 26.1 Loss-timing curves 503 26.2 LGD distribution 508 26.3 Example LGD distribution 512 26.4 Time to write-off 512 26.5 Risk versus profit per account 515 27.1 Response versus acceptance 531 27.2 Risk versus return 532 27.3 Risk versus response 533 27.4 Risk, response, value scoring 534 27.5 Data mart 535 28.1 Gather 539 28.2 Sort 543 28.3 Action 547 29.1 Limit strategies 555 29.2 Pay/no pay 557 29.3 Card authorisations 559 29.4 Risk versus usage 562 30.1 C&R—flowchart 569 30.2 Entry versus sequential definitions 574 31.1 UK plastic fraud losses 578 36.1 Basel I versus Basel II 639 xiv List of figures
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