Credit Risk Database (CRD)
for SME finance in Japan
Satoshi Kuwahara
CRD Association, Japan
May, 2015
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1. SME’s Presence in Japan
2. Some problems in SME finance
3. Previous resolution of the problems
4. Collapse of bubble economy
5. Changes in SME finance
6. Credit bureaus and credit databases in Japan
7. CRD Association in Japan
8. Data content
9. Mechanism of CRD Database
10. CRD services for members
11. Practical usage of CRD database & scoringmodels12. To maintain quality of CRD scoring models
13. Importance of validation
14. CRD keys to success
15. Possible contribution of CRD to other Asian
countries
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1. SME’s Presence in Japan
Added Value(manufacturing industry)Large
enterprises¥33trillion
41.8%

SME
¥46trillion
58.2%

Added Value is from METI,2009 Economic Census for Business Activity•••

SMEs account for about 99.7% of all enterprises
SMEs account for about 69.7% of all workers
SMEs account for about 58.2% of added value (in manufacturingindustry)

SMEs play an important role in the economy, but tend to
face difficulties in financing.
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2. Some Problems in SME finance• Asymmetric information----information gap between borrowers(SMEs) andlenders(Financial Institution)• Fragility of SMEs to the change of businessenvironment
Continuous monitoring borrowers cost lenders agreat deal
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3. Previous resolutionof the problems• Previous resolution of the problems is to utilize theland as collateral.
• In Japan, there was the land myth at the bubble
economy period.
----The land price never fall after all.
Such a wrong recognition cause
the financial system to rely
heavily on the land as collateral.
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4. Collapse of bubble economy
Year-on-year rate (%)
30.0

•

25.0
20.0
15.0
10.0
5.0

Residential area

People perceived that land price did notcontinue to rise any more.
----The price of land was keeping decline
and the population in Japan washeading to diminish.

0.0
-5.0

Before

-10.0
-15.0

Commercial area
80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

Source: Ministry of Land, Infrastructure and Transport•

The excessive amount of collateralor heavy burden of guarantee was
required for financing.

After Bubble
Burst
If the land price will go down more, ・・・

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5. Changes in SME finance
The exit from excessive reliance on land collateral andthe introduction of rigorous evaluation tool for credit risk
• Both Financial Institutions and SMEs recognized loans withexcessive reliance on collateral and guarantee had not been
any more maintainable• Financial Institutions needed to improve the quality of risk
management and SMEs hoped to get better access tofinance• Both of them gave an incentive for the introduction of more
rigorous evaluation of credit risks

To create credit risk information
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6. Credit bureaus and credit
databases in Japan (1/2)
There are two types of organization that deal with credit risk information: Credit
bureau (Credit research company & Designated credit bureaus) & Credit
databases
Credit bureau (Credit research company &Designated credit bureaus)

Credit databases

Information

Personally identifiable information---Information of individual borrowers

Anonymous information---An average borrower in the group with same
attributes (statistical information)

Discipline to the borrowers

DirectTo acquire good financial record(“reputation
collateral”)
To avoid being blacklisted

Indirect
To belong to higher credit rating group
To improve financial condition

Promoting competition in
financial market

DirectTo reduce information monopoly

Indirect
To reduce overestimated risk-premium by
improving predictability

Constraint come from privacy
problems

Tight constraints— Contents(depth) of information— Tight constraints affect the depth andlength of database— Preservation period— Accuracy of data
―Needs for correction of self-information

No constraints— It is comparatively easy to develop deep andrich database enabling more value-add
services.

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6. Credit bureaus and credit
databases in Japan (2/2)
Credit bureaus
Credit researchcompany

Credit databases

Designated credit bureaus

Ⓐ

Ⓑ

Ⓒ

Ⓓ

Ⓔ

Ⓕ

CRD

Ⓖ

Ⓗ

Established

1892

1900

1986

1988

1984

2000

2001

2004

2004

MemberInstitutions

n.a.

n.a.

1,436

1,216

967

65

180

64

279

Creditinformation

2.4
million

3.1
million

n.a.

11.68
million*725,000

2.1
million

679,000

684,000

Default
data*

n.a.

n.a.

n.a.

n.a.

251,000

333,000

n.a.

n.a.

19.5
million*n.a.

* Personal credit information.
* * The data for Ⓐ, Ⓑ and Ⓕ were as of the end of August 2012 and the data for Ⓒ, Ⓓ and Ⓔ were as of the end of March 2015. The data for CRD was
as of the end of September 2014. The data for Ⓖ and Ⓗ were taken from The Research Report, Spring 2008, the Center for Financial Industry Information
Systems.
* * * The methods to collect data by Credit research company are very unique. They collect data from borrowers by interviewing directly.

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7. CRD Association in Japan(1/2)••

To deal with the requirements of excessive collateral in SME financing (To cope with thecollapse of the financial system relied heavily on the collaterals)
To manage the requirements of sophisticated risk management corresponding to BaselⅡ

The Leading user conference was organized by SME Agency.

CRD management Council was founded in March,2001.
*CRD Management Council renamed itself CRD Association in April, 2005.

••

58members attended.
Scoring Model (CRD Model 1 ver.1) was released.

【Missions of CRD】
a) Facilitating the fund provision of SMEs in Japanb) Improving the quality of risk management in financec) Managing the database in a fair manner
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7. CRD Association in Japan(2/2)
Members institutions and data increased dramatically.
End March 2001

End March 2015

CGCs

52

51

Public financial institutions

2

3

Private financial institutions

4

116

Rating companies, etc.

--

4

Total

58

174

* All of the CGCs join CRD Association legally.

Corporations
Number ofcorporations

Sole proprietors

Number ofstatements

Number of sole
proprietors

Number ofstatements

End March 2002

853

2,480

253

497

End March 2015

2,210

16,644

1,099

4,519

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8. Data content (1/3)• Financial data (B/S, P/L)
Balance Sheet
AssetsLiabilities• Current assets―Cash and cash
equivalents―Inventories• Current liabilities―Short-term debt

• Fixed assets―Tangible fixed assets―Intangible assets―Investments• Deferred assets

Profit & Loss Statement
Sales
Cost of goods sold
Gross profit• Fixed liabilities―Long-term debt

OperatingOperatingexpenses
expenses• Salaries expense• Depreciation expense
・・・・・・・・

Shareholders’EquityOperating income• Capital stock

Non-operating income/expense• Interest expense
・・・・・・・・

Financial Indexes• Capital-to-asset ratio• Degree of borrowing on lending

Income before provision for income taxes• Ratio of interest-bearing liabilities

Provision for income taxes• Ratio of current profits to assets

Net income

・・・・・・・・

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8. Data content (2/3)• Non-financial data (Qualitative items)–(a) Owning or not owning real estate; (b) Successor or no successor; and(c) Birth year of CEO.

• Default data
– (a) 3 months or more arrears; (b) de facto bankruptcy; (c) bankruptcy; and(d) subrogation (applicable for credit guarantee corporations).
– (e) substandard and (f) potentially bankruptcy were added as correspondence
to BaselⅡ since April 2003.
* Attributes (for consolidation purpose)(a) First Japanese character of company’s name; (b) Date of establishment; and (c) Postal code.

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8. Data content (3/3)
The submitted data are cleaned and consolidated.
Data cleansing is conducted twice.
•

1st cleansing <BASIC CLEANSING >An example: 1st cleansing– When the CRD receives the data from member
institutions– After checking elementarily, we create every eachmember’s database.
•

Check the condition as follows••

2nd cleansing <MORE RIGOROUS CLEANSING >An example: 2nd Cleansing– Before the CRD consolidate member’s data
– To unify each member’s database into one database,this process is more rigorous than former one.

Current assets ≤ Assets
Fixed assets ≤ Assets

Check the condition as follows•

Assets≒Currents assets+Fixed assets + Deferred assets
summing up details (making little allowances)

Consolidation•

The database needs to consolidate the data of thesame borrowers.

These process enable to create high quality database and we monitor thequality of data continuously.
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9. Mechanism of CRD Database
Data on SMEs under the Small and Medium
Enterprise Basic Law(1) Financial data
(2) Non-financial data
(3) Default information

Members(credit guarantee
corporations, public
and private financial
institutions)(1) (Provision of data)

CRD Database

Member data
Stored in anonymous
form

Cleansing
CRD services(1) Credit risk scoring(2) Sample data provision(3) Statistical information provision(4) Other services

Data consolidation

Database for Use(Accumulated CRD data)(2) (Provision of services)

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10. CRD services for members (1/3)• Basic servicesi.

Scoring service•

Members can use CRD scoring models and evaluate credit risk ofborrowers and potential borrowers in following ways.
— By installing the model program directly to members’ internal rating
systems or by installing the model application to PCs in members’ office.
— By accessing CRD members’ website via the internet or by requesting toreturn submitted data with scoring results.
Financialinformation

Probabilityof default
(PD)

Model

CRD database
Financial data on SMEs
throughout Japan

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Inputs (financial indexes):••••

Capital-to-asset ratio
Degree of borrowing on lending
Ratio of interest-bearing liabilities……..

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10. CRD services for members (2/3)
ii. Sample data provision•

Members can use random sampling data from CRD database.

•Some examples of utilization are as follows.

— To complete insufficient data for creating members’ internal scoring model— To validate members’ internal scoring model— To develop financial products in new areaiii. Statistical information provision•

Members can use statistical information such as the financial
indexes.

•

Some examples of utilization are as follows.

— To compare the financial statistics based on each member’s customers withthose of CRD database for improving the credit risk management— To develop customer oriented marketing strategy and new financing
services

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10. CRD services for members (3/3)• Additional services
Management consulting support system (“McSS”)
•
•

•
•

“McSS” is a consulting tool constructed of CRD scoringmodel and CRD data analysis.
It can indicate the relative position of each borrower
compared with the average borrower of group having same
attributes.
It can evaluate the current position and the future
simulation of borrowers.
Members can advice their borrowers for the improvementof management and financial situation using McSS analysis.

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11. Practical usage of CRD
database & scoring models (1/4)
Some examples using CRD database and scoring models are
as follows.
i. Using same scoring models•

The Credit Guarantee Corporations of all over Japan and the member
financial Institutions can evaluate SME borrowers using same scoringmodels.

【Credit Guarantee Fee Rate Classification】【Credit Insurance System】(Unit: annual rate %)Classification
Credit Gurantee fee rate

1

2
2.20

3
2.00

4
1.80

5
1.60

6
1.35

7
1.10

8
0.90

9
0.70

0.50

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11. Practical usage of CRD database &scoring models (2/4)
ii. Validating own scoring models•

In comparison to CRD model scoring, member can validate theirinternal scoring models.

High Own scoring (PD)

Low

Low

CRD scoring (PD)High

 coincidence ofevaluation
Low risk zone Different evaluation
CRD scoring – HighInternal scoring – Low

 Different evaluation
CRD scoring – Low
Internal scoring – High coincidence ofevaluation
High risk zone
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• Focus on the
difference• Specify thecause of thedifference

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11. Practical usage of CRD database &scoring models (3/4)
iii.

Using the financial indexes of CRD in regional comprehensive strategy

The Abe administration established the Regional Revitalization Headquarter Office (RRHO) andasked local governments to establish the Regional Comprehensive Strategy (RCS).
The RCS aims preventing depopulation and revitalizing local areas. Regional governments are
asked to set the targets after 5years in its strategy. They have to choose Key Performance Indicator(KPI) as those targets that are possible to be verified. Regional governments have to promote the
RCS by utilizing the PDCA cycle.
CRD provides statistical indexes concerning SME management as KPI candidates.

Schematic views
ROA
Increased
Incomerate

××prefecture

ROE

The
whole
country

××prefecture
The
whole
country

Expenseratio

The transition of ROA of
construction industry

Capital-to-asset
ratio
2010

Degree of dependency on loans

21

2011

2012

2013

2014

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11. Practical usage of CRD database &scoring models (4/4)
iv. Others• SME Agency— To use CRD data and scoring result of CRD models for Policy
making and monitoring policy effect (e.g. Credit Guarantee
system)• Bank of Japan— To analyze and to examine the situation of SMEs
(check the distribution of credit risk and financing situation of SMEs, etc.)• FSA— To check the level of each financial institution’s credit risk rating(We are recognized by FSA as a third-party evaluation)
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12. To maintain quality of CRD scoringmodels (1/2)
We created the system that evaluated CRD models
objectively in order to maintain the quality of themodels and to acquire the reliance on our services.
(1) Model quality management guidelines•

We established the guideline in 2007 on the model development, model
operation & validation.

•

In accordance with the guideline, we organized the Third-Party
Evaluation Committee for CRD scoring models.

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12. To maintain quality of CRD scoringmodels (2/2)(2) Regular Validation & its Assessment• We validate CRD models annually in line with the guideline and theregulation under the Small and Medium-sized Enterprise Credit
Insurance Act and FSA notification.
— To check the transition of actual data as compared with the data which thecurrent models are based on— To check Accuracy ratio (AR) of the models— To compare PD with actual default rate
— To check the stability of the model— To check the explanatory ability of the variables to detect default•

The Third-Party Evaluation Committee for CRD scoring models assesses
the result of validation.

(3) Disclosure of assessment by the Third-Party Evaluation Committee
Annual Report by Third-Party Evaluation Committee is delivered to themembers and the summary of the assessments are disclosed to public on
CRD website.
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13. Importance of validation (1/3)
The accuracy of scoring models tend to deteriorate with the lapse of time. We
need to validate scoring models in order to maintain the quality of scoring models.
We show 3cases of our validation and reaction to those as follows.

• CASE1 after Lehman shock
We modified the scoring model to correspond with the situation after Lehman shock.
 Many SMEs’ bankruptcies were
triggered by Lehman shock.

default rate in CRD database
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1

0.8

Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct

0.9

in 2003in 2004

in 2005

in 2006

in 2007

in 2008

in 2009

in 2010in 2011in 2012accounting period

The
distributionof SMEs Then Default rate (≒bankruptcy ratio
among SMEs) in CRD database rised
rapidly. Actual default rate is higher than
PD of scoring model.

in 2013 The probability of SME bankrupt
heightened after Lehman shock.
 The shape of distribution curve was not
drastically changed. We could keep highAR just by changing parameter values.
PD 

Low ← ←

PD

→ → High25

1
1  exp(  Z )Z     X 

Adjusting 𝛼, mainly

𝛼, 𝛽, 𝛾: parameters
Χ: general assessmentof financial indexes
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13. Importance of validation (2/3)• CASE2 The Great East Japan Earthquake
Accuracy Ratio (AR) index
1.2

AR in 2002=1.0
1.1

1.0

0.9

0.8

2002

2003

2004

2005

2006

2007

2008

accounting period

2009

2010
half ayear

The accuracy ratio of scoring model
which was very stable until 2009
suddenly declined in the first half yearof 2010(accounting period).
 We thought that the sudden decline of
AR was affected by the Great East Japan
Earthquake.

 We explored the background of the sudden AR decline on the validation of scoringmodels’ in following way.
———

We calculated AR excluding the data of heavily devastated area (Fukushima prefecture, Miyagi prefecture and
Iwate prefecture).
We specified industries damaged badly.
We calculated AR by the data set shifting one month by one month in order to identify when it declined.

We figured out that the decline of AR occurred at the same time as the earthquake,
deeply in devastated area and considerably in manufacturing industry caused by the
supply chain troubles and the electric power supply shortage.
We decide to maintain the current scoring models and notified specified areas andindustries where the accuracy of scoring model predictability was undermined,
accompanied with the degree of damage in each area and industry.
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13. Importance of validation (3/3)• CASE3 From the recent validation

We explore continuously factors deteriorating performance of scoring models on thevalidation. The acquired knowledge on the validation enhances the operation knowhow of the scoring models and is helpful in designing new scoring models in a practicalway.
We identified categories the level of the AR of which are continuously low.
Accuracy Ratio (AR) index

Accuracy Ratio (AR) index

above ¥1billion in 2010=1.0

1.2¥30million or less
¥300million or less¥1billion or less

1.1

1.2

¥100million or less
¥500million or lessabove ¥1billion

1.1

1.0

1.0

0.9

0.9

0.8

0.8

above 30years in2010=1.0
2years or less20years or lessunknown

5years or less
30years or less10years or lessabove 30years

2012

2013 half a year

0.7

0.7
2010

2011

2012

2010

2013 half a year

accounting period

2011

accounting period

 We calculated the level of AR at each category of sales
volume.

 We calculated the level of AR at each category of theperiod of doing business.

 The categories of lesser sales volume and younger SMEs get lower AR continuously.
 We supposed the sales volume and the period of doing business is dependent each other to some extent.
 We conclude the short period of business practice is a more fundamental factor through the investigation offarther segmented group data.
 We notified the detail of the investigation to members and utilize the knowledge for designing new scoringmodels.

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14. CRD keys to success❷ Very reliable scoring models•

Creating CRD
models withhigh quality &incomparable
big database❸ maintenance of the quality••

Scoringmodels•Validation regularly
Coping with the deterioration of thequality of models
Creating new models

Validation

Services

Credit Risk
Database (CRD)❶ Data-collecting system••

Free from the constraints caused from privacy problem •Easy to encourage members to send data with few
free-rider problems

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CRD
Members

Data & Fees
❹ New services

As well as improving accuracy of scoringmodels, providing members with othernew valued-add services like the risk
management for housing and apartment
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15. Possible contribution of CRD
to
other
Asian
countries
Based on the experience of CRD service provision in Japan, we can support to establish
and improve the similar service provision systems in Asian countries. Particularly we canprovide consulting services in constructing and improving scoring models. The procedure of
the consultation is as follows.
❶ To analyze a present-situation• Validation of present scoring models• Extraction of problems ・・・• Analysis of present data
• Judging possibility to create scoring models ・・・❷ What kind of models do you need?
• PD or EL (PD&LGD) model❸ To create database(DB) for creating new models❹ To create variables and Prototype version ofscoring models• Prototype Models• Out sample data Validation

Follow-up services❺ To validate new models regularly

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