The credit scoring toolkit: theory and practice for retail credit risk management and decision automation
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2007
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | LVI, 731 S. Ill., graph. Darst. |
| ISBN: | 9780199226405 0199226407 |
Internformat
MARC
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| 100 | 1 | |a Anderson, Raymond |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a The credit scoring toolkit |b theory and practice for retail credit risk management and decision automation |c Raymond Anderson |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2007 | |
| 300 | |a LVI, 731 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
| _version_ | 1826212350897684480 |
|---|---|
| adam_text |
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
3
3
7
14
21
25
27
28
38
44
51
53
55
55
63
73
83
90
Module
В
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
Credit scoring and the business
1.1
What is credit scoring?
1.2
Where is credit scoring used?
1.3
Why is credit scoring used?
1.4
How has credit scoring affected credit provision?
1.5
Summary
Credit micro-histories
2.1
History of credit
2.2
History of credit scoring
2.3
History of credit bureaux
2.4
History of credit rating agencies
2.5
Summary
The
mechanics of credit scoring
3.1
What are scorecards?
3.2
What measures are used?
3.3
What is the scorecard development process?
3.4
What can-affect the scorecards?
3.5
Summary
Contents
5.2
Be the master, not the slave
112
5.3
Summary
118
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
С
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
(χ2)
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
Contents
t».
262
269
273
12 Data
sources
275
276
280
284
296
13
Scoring structure
299
299
304
306
310
311
313
14
Information sharing
315
315
321
326
15
Data preparation
329
329
335
344
347
353
Module
E Scorecard
development
355
16
Transformation
357
357
361
367
370
372
376
17
Characteristic selection
379
379
382
384
389
393
18
Segmentation
395
18.1
Segmentation drivers
395
11.3
Data quality
11.4
Data design
11.5
Summary
Data
sources
12.1
Customer supplied
12.2
Internal systems
12.3
Credit bureaux data
12.4
Summary
Scoring structure
13.1
Customisation
13.2
Hosting
—
internal versus external
13.3
Integrating data
13.4
Credit risk scoring
13.5
Matching!
13.6
Summary
Information sharing
14.1
Credit registries
14.2
Do
1
or don't
1?
14.3
Summary
Data
preparation
15.1
Data acquisition
15.2
Good/bad definition
15.3
Observation and outcome windows
15.4
Sample design
15.5
Summary
Transformation
16.1
Transformation methodologies
16.2
Classing
16.3
Use of statistical measures
16.4
Pooling algorithms
16.5
Practical cases
16.6
Summary
Characteristic selection
17.1
Considerations for inclusion
17.2
Statistical measures
17.3
Data reduction methods
17.4
Variable feed
17.5
Summary
χ
Contents
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
447
452
456
24
Overrides, referrals, and controls
457
457
458
460
463
466
25
Monitoring
467
469
473
480
484
493
Implementation
23.1
Decision automation
23.2
Implementation and testing
23.3
Summary
Overrides, referrals, and controls
24.1
Policy rules
24.2
Overrides
24.3
Referrals
24.4
Controls
24.5
Summary
Monitoring
25.1
Portfolio analysis
25.2
Performance tracking
25.3
Drift reporting
25.4
Selection process
25.5
Summary
Finance
26.1
Loss provisioning
26.2
Direct loss estimation
26.3
Loss component estimation
26.4
Scoring for profit
26.5
Risk-based pricing
26.6
Summary
Contents
xi
26
Finance
495
495
497
501
512
519
525
Module
G
Credit Risk management cycle
527
27
Marketing
529
529
530
532
534
536
28
Application processing
537
538
543
546
551
29
Account management
553
554
556
560
564
30
Collections and recoveries
567
567
570
572
575
31
Fraud
577
579
584
586
588
591
Module
H
Regulatory environment
593
32
Regulatory concepts
595
32.1
Best practice
595
Marketing
27.1
Advertising media
27.2
Two tribes go to war
—
quantity versus quality
27.3
Pre-screening
27.4
Data
27.5
Summary
Application processing
28.1
Gather
—
interested customer details
28.2
Sort
—
into strategy buckets
28.3
Action
—
accept or reject
28.4
Summary
Account management
29.1
Types of limits
29.2
Over-limit management
29.3
More limit and other functions
29.4
Summary
Collections and recoveries
30.1
Overview
30.2
Triggers and strategies
30.3
Scoring
30.4
Summary
Fraud
і
31.1
Types of fraud
31.2
Fraud detection tools
31.3
Fraud prevention strategies
31.4
Fraud scoring
31.5
Summary
32.2
Good governance
32.3
Business ethics and social responsibility
32.4
Compliance hierarchy
32.5
Summary
33
Data |
privacy and protection
33.1
Background
33.2
Data privacy principles
33.3
Summary
34
Anti-discrimination
34.1
Discrimination
—
what does it mean?
34.2
Problematic characteristics
34.3
Summary
35
Fair lending
35.1
Predatory lending
35.2
Irresponsible lending
35.3
Responsible lending
35.4
Summary
36
Capital adequacy
36.1
Basel capital accord
1988
(Basel
1)
36.2
New Basel capital accord
2004
(Basel II)
36.3
RWA
calculation
36.4
Summary
37
Know Your customer (KYC)
37.1
Due diligence requirements
37.2
Customer identification requirements
38
National differences
38.1
United States of America
38.2
Canada
38.3
United Kingdom
38.4
Australia
38.5
Republic of South Africa (RSA)
Glossary
Bibliography
Appendices
Index
596
598
600
601
603
603
610
619
621
621
624
625
627
628
629
631
632
635
637
638
644
647
649
650
651
653
653
655
656
658
659
663
709
721
723
The Credit Scoring Toolkit provides an all-encom¬
passing view of the use of statistical models to
assess retail credit risk and provide automated
decisions, and in eight modules it provides frame¬
works for both theory and practice. The book first
explores the economic justification and history of
Credit Scoring, risk linkages and decision science,
statistical and mathematical tools, the assessment
of business enterprises, and regulatory issues
ranging from data privacy to Basel II. It then
provides a practical how-to-guide for scorecard
development, including data collection, scorecard
implementation, and use within the credit risk
management cycle.
Including numerous real-life examples and an
extensive glossary and bibliography, the text assumes
little prior knowledge making it an indispensable
desktop reference for graduate students in statistics,
business, economics and finance, MBA students,
credit risk and financial practitioners.
■
Highly-accessible guide to Credit Scoring
■
Comprehensive, up-to-date, and wide ranging
■
Assumes little prior knowledge
■
Numerous examples and illustrations
■
Extensive glossary and bibliography |
| adam_txt |
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
3
3
7
14
21
25
27
28
38
44
51
53
55
55
63
73
83
90
Module
В
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
Credit scoring and the business
1.1
What is credit scoring?
1.2
Where is credit scoring used?
1.3
Why is credit scoring used?
1.4
How has credit scoring affected credit provision?
1.5
Summary
Credit micro-histories
2.1
History of credit
2.2
History of credit scoring
2.3
History of credit bureaux
2.4
History of credit rating agencies
2.5
Summary
The
mechanics of credit scoring
3.1
What are scorecards?
3.2
What measures are used?
3.3
What is the scorecard development process?
3.4
What can-affect the scorecards?
3.5
Summary
Contents
5.2
Be the master, not the slave
112
5.3
Summary
118
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
С
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
(χ2)
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
Contents
t».
262
269
273
12 Data
sources
275
276
280
284
296
13
Scoring structure
299
299
304
306
310
311
313
14
Information sharing
315
315
321
326
15
Data preparation
329
329
335
344
347
353
Module
E Scorecard
development
355
16
Transformation
357
357
361
367
370
372
376
17
Characteristic selection
379
379
382
384
389
393
18
Segmentation
395
18.1
Segmentation drivers
395
11.3
Data quality
11.4
Data design
11.5
Summary
Data
sources
12.1
Customer supplied
12.2
Internal systems
12.3
Credit bureaux data
12.4
Summary
Scoring structure
13.1
Customisation
13.2
Hosting
—
internal versus external
13.3
Integrating data
13.4
Credit risk scoring
13.5
Matching!
13.6
Summary
Information sharing
14.1
Credit registries
14.2
Do
1
or don't
1?
14.3
Summary
Data
preparation
15.1
Data acquisition
15.2
Good/bad definition
15.3
Observation and outcome windows
15.4
Sample design
15.5
Summary
Transformation
16.1
Transformation methodologies
16.2
Classing
16.3
Use of statistical measures
16.4
Pooling algorithms
16.5
Practical cases
16.6
Summary
Characteristic selection
17.1
Considerations for inclusion
17.2
Statistical measures
17.3
Data reduction methods
17.4
Variable feed
17.5
Summary
χ
Contents
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
447
452
456
24
Overrides, referrals, and controls
457
457
458
460
463
466
25
Monitoring
467
469
473
480
484
493
Implementation
23.1
Decision automation
23.2
Implementation and testing
23.3
Summary
Overrides, referrals, and controls
24.1
Policy rules
24.2
Overrides
24.3
Referrals
24.4
Controls
24.5
Summary
Monitoring
25.1
Portfolio analysis
25.2
Performance tracking
25.3
Drift reporting
25.4
Selection process
25.5
Summary
Finance
26.1
Loss provisioning
26.2
Direct loss estimation
26.3
Loss component estimation
26.4
Scoring for profit
26.5
Risk-based pricing
26.6
Summary
Contents
xi
26
Finance
495
495
497
501
512
519
525
Module
G
Credit Risk management cycle
527
27
Marketing
529
529
530
532
534
536
28
Application processing
537
538
543
546
551
29
Account management
553
554
556
560
564
30
Collections and recoveries
567
567
570
572
575
31
Fraud
577
579
584
586
588
591
Module
H
Regulatory environment
593
32
Regulatory concepts
595
32.1
Best practice
595
Marketing
27.1
Advertising media
27.2
Two tribes go to war
—
quantity versus quality
27.3
Pre-screening
27.4
Data
27.5
Summary
Application processing
28.1
Gather
—
interested customer details
28.2
Sort
—
into strategy buckets
28.3
Action
—
accept or reject
28.4
Summary
Account management
29.1
Types of limits
29.2
Over-limit management
29.3
More limit and other functions
29.4
Summary
Collections and recoveries
30.1
Overview
30.2
Triggers and strategies
30.3
Scoring
30.4
Summary
Fraud
і
31.1
Types of fraud
31.2
Fraud detection tools
31.3
Fraud prevention strategies
31.4
Fraud scoring
31.5
Summary
32.2
Good governance
32.3
Business ethics and social responsibility
32.4
Compliance hierarchy
32.5
Summary
33
Data |
privacy and protection
33.1
Background
33.2
Data privacy principles
33.3
Summary
34
Anti-discrimination
34.1
Discrimination
—
what does it mean?
34.2
Problematic characteristics
34.3
Summary
35
Fair lending
35.1
Predatory lending
35.2
Irresponsible lending
35.3
Responsible lending
35.4
Summary
36
Capital adequacy
36.1
Basel capital accord
1988
(Basel
1)
36.2
New Basel capital accord
2004
(Basel II)
36.3
RWA
calculation
36.4
Summary
37
Know Your customer (KYC)
37.1
Due diligence requirements
37.2
Customer identification requirements
38
National differences
38.1
United States of America
38.2
Canada
38.3
United Kingdom
38.4
Australia
38.5
Republic of South Africa (RSA)
Glossary
Bibliography
Appendices
Index
596
598
600
601
603
603
610
619
621
621
624
625
627
628
629
631
632
635
637
638
644
647
649
650
651
653
653
655
656
658
659
663
709
721
723
The Credit Scoring Toolkit provides an all-encom¬
passing view of the use of statistical models to
assess retail credit risk and provide automated
decisions, and in eight modules it provides frame¬
works for both theory and practice. The book first
explores the economic justification and history of
Credit Scoring, risk linkages and decision science,
statistical and mathematical tools, the assessment
of business enterprises, and regulatory issues
ranging from data privacy to Basel II. It then
provides a practical how-to-guide for scorecard
development, including data collection, scorecard
implementation, and use within the credit risk
management cycle.
Including numerous real-life examples and an
extensive glossary and bibliography, the text assumes
little prior knowledge making it an indispensable
desktop reference for graduate students in statistics,
business, economics and finance, MBA students,
credit risk and financial practitioners.
■
Highly-accessible guide to Credit Scoring
■
Comprehensive, up-to-date, and wide ranging
■
Assumes little prior knowledge
■
Numerous examples and illustrations
■
Extensive glossary and bibliography |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Anderson, Raymond |
| author_facet | Anderson, Raymond |
| author_role | aut |
| author_sort | Anderson, Raymond |
| author_variant | r a ra |
| building | Verbundindex |
| bvnumber | BV023094737 |
| callnumber-first | H - Social Science |
| callnumber-label | HG3751 |
| callnumber-raw | HG3751.5 |
| callnumber-search | HG3751.5 |
| callnumber-sort | HG 43751.5 |
| callnumber-subject | HG - Finance |
| classification_rvk | QK 320 SK 980 |
| ctrlnum | (OCoLC)123797221 (DE-599)BVBBV023094737 |
| dewey-full | 658.88 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.88 |
| dewey-search | 658.88 |
| dewey-sort | 3658.88 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV023094737 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:42:29Z |
| indexdate | 2025-03-10T13:03:34Z |
| institution | BVB |
| isbn | 9780199226405 0199226407 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016297557 |
| oclc_num | 123797221 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-11 DE-29T DE-858 |
| owner_facet | DE-355 DE-BY-UBR DE-11 DE-29T DE-858 |
| physical | LVI, 731 S. Ill., graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| spelling | Anderson, Raymond Verfasser aut The credit scoring toolkit theory and practice for retail credit risk management and decision automation Raymond Anderson 1. publ. Oxford [u.a.] Oxford Univ. Press 2007 LVI, 731 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Credit scoring systems Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Kreditwesen (DE-588)4032950-1 gnd rswk-swf Credit Scoring (DE-588)4403996-7 gnd rswk-swf Risikomanagement (DE-588)4121590-4 gnd rswk-swf Kreditwesen (DE-588)4032950-1 s Credit Scoring (DE-588)4403996-7 s Risikomanagement (DE-588)4121590-4 s Entscheidungsfindung (DE-588)4113446-1 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016297557&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016297557&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Anderson, Raymond The credit scoring toolkit theory and practice for retail credit risk management and decision automation Credit scoring systems Entscheidungsfindung (DE-588)4113446-1 gnd Kreditwesen (DE-588)4032950-1 gnd Credit Scoring (DE-588)4403996-7 gnd Risikomanagement (DE-588)4121590-4 gnd |
| subject_GND | (DE-588)4113446-1 (DE-588)4032950-1 (DE-588)4403996-7 (DE-588)4121590-4 |
| title | The credit scoring toolkit theory and practice for retail credit risk management and decision automation |
| title_auth | The credit scoring toolkit theory and practice for retail credit risk management and decision automation |
| title_exact_search | The credit scoring toolkit theory and practice for retail credit risk management and decision automation |
| title_exact_search_txtP | The credit scoring toolkit theory and practice for retail credit risk management and decision automation |
| title_full | The credit scoring toolkit theory and practice for retail credit risk management and decision automation Raymond Anderson |
| title_fullStr | The credit scoring toolkit theory and practice for retail credit risk management and decision automation Raymond Anderson |
| title_full_unstemmed | The credit scoring toolkit theory and practice for retail credit risk management and decision automation Raymond Anderson |
| title_short | The credit scoring toolkit |
| title_sort | the credit scoring toolkit theory and practice for retail credit risk management and decision automation |
| title_sub | theory and practice for retail credit risk management and decision automation |
| topic | Credit scoring systems Entscheidungsfindung (DE-588)4113446-1 gnd Kreditwesen (DE-588)4032950-1 gnd Credit Scoring (DE-588)4403996-7 gnd Risikomanagement (DE-588)4121590-4 gnd |
| topic_facet | Credit scoring systems Entscheidungsfindung Kreditwesen Credit Scoring Risikomanagement |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016297557&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016297557&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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