Transcript Presentation - Quality on Statistics 2010

Use of credit register data
for statistical purposes:
advantages and preconditions,
current and potential future uses
Violetta Damia, Vitaliana Rondonotti
European Conference on Quality in Official Statistics
Helsinki, 4-6 May 2010
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Contents
 Background
 Credit register’s data: scope and coverage
 Central Credit Register data: advantages and drawbacks
 Preconditions and current limitations for the statistical use
of Central Credit Register data
 Conclusions and way forward
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Background
Increase in ESCB data needs, in particular for:
• Enhanced data content (coverage, level of details)
• Higher frequency and improved timeliness
• Higher flexibility
while maintaining:
• Comprehensive, harmonised and consistent statistics
• Minimum reporting burden
• High quality data
 Use of granular and flexible datasets maintained in microdatabases and registers, where appropriate
 In particular possible use of credit registers for statistical
purposes
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Credit register’s data: scope and coverage (1/2)
Credit Registers: granular databases on loan information
Central Credit Registers (CCR): generally maintained by
National Central Banks, collect information mainly from
supervised institutions, to support:
1) bank supervisors for credit risk assessment of supervised
financial institutions
2) financial institutions for credit risk evaluation of transactions
3) economic analysis
And, on a case by case, CCR are used for research and
statistics
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Credit register’s data: scope and coverage (2/2)
Credit Registers: granular databases on loan information
Private Credit Bureaus (PCB):
collect information from different data sources (lenders,
firms, households, etc.) to support lenders in the
assessment of credit conditions for small and medium-size
enterprises (also modelling consumer behaviours, or
assessment of default probability by type of loans)
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Central credit register’s data: advantages
Advantages:

granular information – wide coverage

data updated, revised and checked on a regular basis

number of attributes of interest

possible links with other sources (identifiers)

for euro area/EU statistics, availability of CCR data in a
significant number of countries
(BE, DE, ES, FR, IT, AT, PT, SI, SK, BG, CZ, LT, LV, RO)
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Central credit register’s data: drawbacks
Drawbacks:

data collected mainly for supervisory purposes, therefore:

differences in coverage, content, definitions and methodologies
and lack of certain breakdowns

(often high) thresholds

for statistical purposes, no direct data influence/responsibility

for euro area/EU statistics, additional lack of harmonisation
cross-country and lack of CCR data in some countries

data confidentiality
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Preconditions and current limitations for the statistical
use of Central Credit Register’s data (1/2)
 banking supervision
Purposes
 financial stability
 credit risk and (in some cases) economic analysis
statistical use is generally not part of the main purposes,
although some statistics are compiled, mainly for internal
purposes and quality checking; very limited use for ECB
statistical requirements
Data access for
statistical use
 CCR often not maintained by the Statistics Department
Legal framework
for statistical use
 yes to ensure data collection and confidentiality
 limited access (mainly with no interface)
 supervised financial institutions
Reporting
population
 census but often with rather high threshold
(the threshold varies from EUR 50 to 1.5m)
 home country principle (banks’ foreign branches included,
but can be separately identified)
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Preconditions and current limitations for the statistical
use of Central Credit Register’s data (2/2)
Basis of
reporting
 mainly borrower-by-borrower (use of borrower identifiers mainly for
domestic borrowers - internal assigned codes - links with internal
databases, enterprise numbers);
 in some cases loan-by-loan (use of loan identifiers - internal
assigned codes)
Credit data type
 outstanding amounts (end-of month)
 (largely) consistent definition of loans
 both positive (loans granted) and negative (defaulting) info
 no information on interest rates
 no consistent sectoral classification of counterparties
 no classification by purpose
 in some cases classification by size of firms
Credit data  breakdown by countries mostly available
classifications  breakdown by currencies or maturity not always available
 little information on securitised, syndicated, back mortgage loans
 some information on collateral, derivatives, guarantees, credit lines,
commitment credit
 owing to high thresholds, many CCRs hardly cover loans to
Data coverage households for purposes other than financing house purchase
 loans to non financial corporations are generally well covered
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Conclusions and way forward (1/2)
Some examples of statistical use of Central Credit
Registers’ data:
 compile/check Monetary and Financial Statistics and
support compilation of certain statistical breakdowns
 build up and maintain list of attributes to support national
and euro area sampling
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Conclusions and way forward (2/2)
For a wider statistical use:
 Need to overcome the shortcomings identified in scope,
coverage, definitions, reporting framework, interoperability,
links with other sources
 Assessment of merits and costs for statistical use
 Development of coherent and integrated system(s) to
create statistical databases to meet various needs ensuring
coverage, punctuality and timeliness, consistency and
harmonisation, reliability.
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