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November 2016
AnaCredit Analytical Credit Dataset of the ECB - ImplementationChallenges and Approaches
THE AUTHORS
Sergio Gianni is a partner at Avantage Reply. Sergio has over 20 years of experience in
Prudential Risk, Finance and Regulation. Prior to establishing Avantage Reply Italy in 2008,
he worked for two large consultancies. His expertise includes credit risk management,
financial analysis, compliance and cover both the setting of governance, strategy, model
development and validation as well as their implementation in policy, system development
and regulatory reporting. He has also contributed articles to various publications and
frequently speaks in industry conferences.
Sergio Gianni
About Avantage Reply
Established in 2004, Avantage Reply (a
member firm of Reply) is a pan-European
specialised management consultancy de-
livering change initiatives in the areas of
Compliance, Finance, Risk and Treasury.
Website: www.avantagereply.com
Oscar McCarthy is an associate partner at Avantage Reply. He has sixteen years of
professional experience in commercial and investment banking, covering all major risk
types (credit, market, operational, liquidity and strategic) and the associated prudential
regulations. His areas of expertise include both the setting of the risk governance,
strategy and appetite as well as their implementation in policy, methodology and
reporting. He has a Ph.D. in Mathematics, and is certified as a Professional Risk Manager.
Oscar serves pro-bono on the Board of the Professional Risk Manager’s International
Association, and is the editor of the Professional Risk Manager’s Handbook (market risk).
Oscar McCarthy
Rob Konowalchuk joined Avantage Reply UK in September 2016 as an associate partner,
having spent his career in various advisory roles within financial services risk and regulation.
With 16 years of experience in the financial services sector (primarily banking and capital
markets), Rob has become an expert in a broad range of issues facing financial services
firms, particularly in prudential risk and regulation. With a background in accounting and
auditing, Rob applies expertise in governance, controls, systems and data to design and
deliver change and remediation programmes, primarily driven by prudential regulation
(with a focus on capital and liquidity management, regulatory reporting, capital planning
and stress testing, and recovery and resolution planning). Rob has a degree in Business
Administration and is qualified as a chartered accountant in the UK and Canada.
Rob Konowalchuk
TABLE OF CONTENTS
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1
Abstract
Overview
National Central Credit Registers as a starting point
The new approach to expanded statistical and supervisory reporting within the SSM
How Avantage Reply can help
Target Architecture
Organisational aspects and data governance
Data gap analysis and remediation
Re-engineering processes and procedures
Key take-aways
Appendix - case studies
Abstract Availability of sufficiently granular and reliable
data is a major priority of the Single Supervisory
Mechanism (‘SSM’). To this end, the ECB is
demanding additional and more granular
reporting from banks under its supervisory remit
by introducing its new reporting requirement:
the Analytical Credit Dataset – also known as
AnaCredit. This reporting will form a detailed
eurozone bank dataset on credit risk and is seen
as a critical enabler of effective European banking
supervision. Its implementation represents the first
step towards establishing a harmonised statistical
credit reporting framework within the eurozone.
AnaCreditAbstract
2
“The ECB has every interest to facilitate and promote integration and standardisation also on the ‘input side’, in the internal systems of the banks, for only this will ensure coherent information.”Mario Draghi, Seventh ECB Statistics Conference
on 15 October 2014
AnaCreditOverview
3
OverviewIn the SSM Framework Regulation, AnaCredit
is placed in the middle of a roadmap for further
developing micro and macro prudential supervision in
the euro system based on granular credit data within
the context of a harmonised supervision approach.
Its implementation is a key challenge for banks that
needs to be addressed in a short timeframe.
On 4 December 2015, the ECB released the Draft
Regulation on the collection of granular credit and
credit risk data, setting out the statistical reporting
requirements, the reporting population and the
reporting thresholds. The consultation period
ended on 29 January 2016. The ECB assessed the
consultation responses received by National Central
Banks (NCBs) and, as a result, issued an amended
version of the draft regulation on 18th May 2016. The
regulation has been approved by Governing Council
on 1st June 20161.
The objective of AnaCredit is to establish a
central register of granular data about the credit
exposures of credit institutions within the eurozone.
In addition AnaCredit will reinforce the current
European supervisory and regulatory framework,
in part by supporting the Supervisory Review and
1 Regulation (EU) 2016/867 of the European Central Bank of 18 May 2016 on the collection of
granular credit and credit risk data (ECB/2016/13)
AnaCreditOverview
4
Evaluation Processes (‘SREP’) methodologies. In addition,
AnaCredit will inform statistical analysis and monetary policy
decision-making.
AnaCredit is a first step towards improved statistical data
collection; moving from a silo-based approach to high
granularity risk and finance data from different sources
consolidated into a central repository. Considering the high
level of heterogeneity of the current credit data collection
within the eurozone and the different levels of data
granularity, AnaCredit will be implemented in stages taking
into account the time needed by banks (the ‘reporting
agents’) to be compliant with the data requirements. The
table below illustrates the information required and critical
data elements.
The first stage begins on 1 September 2018 with preliminary
identification of counterparties. Depending on national
discretion, NCBs may require reporting agents to provide
partial or complete counterparty reference data and credit
data in advance of this date. The data will be collected from
participating NCBs, who themselves collect it from credit
institutions. Each credit institution will be required to report
95 data attributes (including seven identifiers for each:
Reporting agent, Observed agent, Counterparty, Contract,
Instrument, Protection identifier and Protection provider)
about their credit exposures, which are grouped into two
prescribed templates containing ten tables.
Some of this data is not currently produced and collected
by banks in a structured way, so the creation of these
templates and tables represents a new level of complexity
depending on the data availability and sources as well
as different national regulations (including rules on data
privacy). The table below illustrates the data attributes,
level of complexity and critical data elements required. To
an extent, this aligns with current harmonised regulatory
reporting in the EU (Common Reporting (‘COREP’) and
Financial Reporting (‘FINREP’)) but is at a much more
granular level. This presents challenges, recalling the
enormous complications that arose during the creation of
the Loan Tape in the Asset Quality Review (‘AQR’) exercise.
1. Counterparty reference data
Template Table # Attributes
2. Instrument data
Level of Complexity
Critical data elements
22 High • Counterparty identification through
Code LEI (Legal Entity Identifier)
• Enterprise size, data of enterprise size
and number of employees, according
to Recommendation 2003/361/EC
23 Medium • Details on interest rate (cap, floor, type)
3. Financial data 11 Medium • Details on interest rate (next interest
rate reset date)
4. Counterparty instrument data 1 Low
5. Joint liabilities data 1 Low
• Status and date of forbearance and
renegotiation
TEM
PLA
TE 1
6. Accounting data 16 Medium
TEM
PLA
TE 2
7. Protection received data 9 Medium • Detail on type of protection and real
estate collateral location
8. Instrument-protection received data 2 Medium • Third party priority claims against the
protection
9. Counterparty risk data 1 Low
10. Counterparty default data 2 Low
Figure 1: AnaCredit structure structure: templates, tables and critical data elements
AnaCreditOverview
The timeline and the main reporting requirements for the first and subsequent phases are showed in the figure below:
• IT solutions involved in the management of credit risk
will need to be adapted to handle the new information and
frequency of reporting;
• Processes that support underwriting, monitoring and
reporting will need to be revisited to ensure complete and
accurate data capture and robust, controlled production of
the new data submissions;
• Credit risk modelling processes may need to changed
based on new data structures and content to the extent
that the data used is currently aligned to information
provided to national CCRs.
More detail on our views on the business and process
impact is included later in this paper (in the section ‘How Avantage Reply can help’ and the accompanying case studies).
5
Amount equal to or larger than EUR 25K on any reporting reference date within the reference period
Amount equal to or larger than EUR 25K on any reporting reference date within the reference period
Instrument by Instrument Instrument by Instrument
Resident credit institutions and resident foreign branches of credit institutions
NCBs’ right to grant derogations to small reporting agents, to be adopted at least two years prior to its introduction to allow sufficient time for implementation
Loans and depositsCredit granted by credit institutions to legal entities on individual basis No personal data
Extension to derivatives, other accounts receivable, off-balance-sheet items Credit extended to persons other than legal persons, including to sole proprietors On consolidated basisPersonal data (ensuring the privacy rights)
To ensure the appropriate identification of counterparties, NCBs shall transmit to the ECB a first set of the counterparty reference data, six months prior to the first transmission
All templates and related tables need to be provided at the same time
Reporting Threshold
Approach
Population
Scope
Submission
FIRST STAGE - 1ST SEPTEMBER 2018 SUBSEQUENT STAGES
Figure 2: AnaCredit reporting requirements
AnaCredit replaces the current local Central Credit
Registers (‘CCRs’), ensuring a standard level of granularity
on common data among different EU countries. The main
advantages will be realised over a longer term because
AnaCredit will contribute to a convergence of existing
European CCRs and the creation of CCRs in the countries
where they are absent. In this context, the CCRs appear to
be the starting point to evaluating the incremental efforts
required in each country.
Impact on banks
The transition has several implications both for regulators
and banks in respect of business processes and underlying
infrastructure. The main areas that will be affected are as
follows:
AnaCreditNational Central Credit Registers as a starting point
6
From lenders’ perspective, CCRs represent granular
databases with three main purposes:
• Accurately assessing the credit quality of potential
borrowers in supervised financial institutions;
• Supporting financial transactions through the
evaluation of risk by credit institutions; and
• Enabling economic analysis.
These databases exist and operate in 14 EU countries2
(Austria, Belgium, Bulgaria, Czech Republic, France,
Germany, Italy, Latvia, Lithuania, Portugal, Romania,
Slovenia, Slovakia and Spain), under the supervision of
NCBs, to monitor and manage credit risk information and
provide an overview of the credit exposure concentrations
and indebtedness levels of resident and non-resident
borrowers.
Unfortunately, harmonisation across CCRs in some EU
countries is limited. Data is collected at different levels
of granularity, which can be seen in more detail in the
following table in terms of:
• Reporting threshold and frequency;
• Population and perimeter; and
• Lack of detailed information on asset, collateral and
valuation.
National Central Credit Registers (CCRs) as a starting point
Figure 3: Main differences between European CCRs
Reporting variables Italy3 Austria Belgium Czech Replublic
Germany4 Spain France Portugal Romania
Threshold
Frequency
National Central Banks
Credit Institutions
Insurance companies
Investment companies
Foreign bank branches
Branches of the “Caisse des depots”
Resident private entities
Resident institutions
Resident public administrations
Resident non-financial companies
Collateral
Loan purpose
Maturity
REP
OR
TIN
G P
OP
ULA
TIO
NR
EPO
RTI
NG
AG
ENTS
REP
OR
TIN
G
REQ
UIR
EMEN
TS
30,000 350,000 25,000 n/a 1,000,000 6,000 76,00045,0005
50 5,000
monthly monthly monthly monthly quarterly monthly monthly monthly monthly
AnaCreditNational Central Credit Registers as a starting point
2 Memorandum of understanding on the exchange of information among national central credit
registers for the purpose of passing it on to reporting institutions (Source: https://www.bancaditalia.
it/statistiche/raccolta-dati/centrale-rischi/Memorandum_of_Understanding_10.pdf ).
NCBs in every EU country in the scope of SSM will be
responsible for evaluating the timing and convergence
methods between AnaCredit and local CCRs; moving
towards the instrument-by-instrument basis required by
AnaCredit.
3 Bank of Italy recently provided the following updates:
• Anticipation of the collection on counterparty data attributes by 3 months;
• Postponement of the integration between CCR (Italian Central Credit Register) and AnaCredit until
not before 2020, mandating an extended period of parallel running. As thus, institutions must consider
the need to introduce means of ensuring consistency between the two activities.
4 The German authorities (Ministry of Finance, Bundesbank and BaFin) agreed to delay the
intended reform of the local credit register until January 1, 2019 to decide about the convergence
of the LCR and AnaCredit or the abolition of the LCR. In addition, the authorities are considering the
option that AnaCredit could replace other existing statistical reports.
5 France uses two different thresholds based on the geographical limits: EUR 76,000 for reporting
institutions located in metropolitan France and the French overseas territories. EUR 45,000 for
reporting institutions located in the French overseas departments (Guadeloupe, Martinique, Guyana
and Reunion) and in the territorial units of St. Pierre and Miquelon and the departmental unit of Mayotte.
AnaCreditThe new approach to expanded statistical and supervisory
reporting within the SSM
8
The new approach to expanded statistical and supervisory reporting within the SSMAnaCredit gives banks an opportunity to improve their
data management, but poses some important challenges.
Current monetary policy, supervisory and other reporting
standards require data, which are collected in a large
number of reports with different submission frequencies
and different levels of aggregation. When a reporting
framework is updated or a new one is created, banks
must interpret it, extract the data from their IT systems
and transform it appropriately to produce the final data
submission in accordance with the relevant regulation.
These processes are often highly complex, involving
multiple systems, manual interventions and judgements
and can therefore produce unintentionally inconsistent
results between submissions. A common reporting
framework is helpful in order to avoid overlap, duplication
and inconsistency. The proliferation of reporting
requirements that demand more granularity and complexity
has motivated the ECB to begin building a joint European
Information System based on three main components:
1. A single, comprehensive and harmonised primary
European Reporting Framework (ERF), or “output layer”
for regular data transmission. This model aims to ensure
precise, simple and unambiguous definitions of information
prescribed by regulatory or statistical reports.
2. The Banks’ Integrated Reporting Data Dictionary(BIRD), an “input layer” that describes data elements
on their granular level and defines them in an accurate,
standardised and unique way. It will be a common
language for data within the European banking sector,
aiming to provide a standardised model for organising
banks’ internal data warehouses in an integrated way,
and the transformations to the data that the banks need
perform. The BIRD consists of documentation. It is not an IT
tool and does not add reporting requirements. The BIRD’s
main advantages are:
• Greater consistency and better quality data at source,
eliminating the need to manage each mandatory data
collection in a separate way;
• Increased efficiency, consistency and harmonisation
of data, lower costs of report production; and
• Consistent interpretation and clarity of requirements.
The first version of the BIRD addresses the new
requirements related to the collection of AnaCredit data.
The initial creation of a stable glossary covering (at least)
AnaCredit has been the subject of ECB workshops since
autumn 2015 and some volunteer NCBs have started
working on a the pilot, with implementation expected by
the first quarter of 20176.
The future BIRD extensions, are still under evaluation
and should be able to cover also the following statistical
and supervisory reporting requirements: ECB’s Monetary
Financial Institutions’ Balance Sheet Items Statistics
(BSI), ECB’s Monetary Financial Institutions’ Interest Rate
Statistics (MIR), ECB’s Securities Holdings Statistics (SHS),
and EBA’s Implementing Technical Standards (ITS), which
encompass COREP and FINREP.
This will require a long implementation period and it
is crucial to understand the role of BIRD in the SSM
Regulation Framework.
3. A single data dictionary (SDD), a methodological
and semantic integration of existing European reporting
frameworks through the creation of clear and non-
overlapping definitions of the data (reconciled across
several regulatory frameworks) for all reports that are
generated from the data submitted by credit institutions (as
opposed to the common language established under BIRD
for the data reported by banks).
6 Banks’ Integrated Reporting Dictionary, available at: http://www.banks-integrated-reporting-
dictionary.eu/documents:birdproject
9
AnaCreditThe new approach to expanded statistical and supervisory
reporting within the SSM
ECB initiatives in the area of integration
Institutions NCBs ECB
sourcing
internal mapping
Pri
mar
y da
ta (s
ourc
e)
Rep
ortin
g da
ta w
areh
ouse
Reporting granular and micro data
Feedback loop
Manipulating data from data warehouse to the regulatory data model
Pri
mar
y R
epor
ts (E
RF)
Sec
onda
ry R
epor
ts
TRANSFORMATION
Transformation rules specified through NCBs
and ECB
BIRD (Bank’s Integrated Reporting Dictionary)
SDD (Single Data Dictionary)
ERF (European Reporting Framework)
Figure 4: The Role of BIRD, ERF and SDD (from ECB initiatives in the area of integration)
The AnaCredit regulation will need to be assessed and
implemented in conjunction with the developments in the
ERF and BIRD in order to achieve a harmonised framework.
To implement AnaCredit with BIRD, a complex reconciliation
with the current structured reporting framework will be
necessary for local CCRs.
There are various processes and instruments adopted
by national competent authorities (NCAs) in the different
countries that govern the production of harmonised and non-
harmonised reporting. Both within banks and regulators, a
range of well-structured and less prescriptive approaches
are in place governing the end-to-end regulatory reporting
production process.
For example, in Italy the whole production process of
regulatory reporting is governed by the protocol PUMA2 (Unified Business Matrix Procedure), an feature of Italian
banking system since 1974. The PUMA2 is an integrated
process that develops reference documentation for the
production of data on financial intermediaries (e.g. the matrix
report forms for bank’s balance sheet information, statistical
and supervisory reports of financial companies that are on
the special register, reports of the CCRs, payment system
investigation, etc.). In detail PUMA2 provides:
• Extensive coverage of regulatory reporting indicators,
extended to Basel III, derived from a combination
of financial / regulatory reporting systems as well
as data from other functions (e.g. risk management
departments);
• A well-defined reporting framework including the
input data dictionary, output data dictionary and data
transformation rules (data lineage) through the decision
table; and
• Wider cooperation through collaboration between
representatives of Bank of Italy, Italian banks, and
market players in the evolution of PUMA2 to address
any new regulatory reporting. This aspect ensures
sharing and reducing investment especially for small
and medium-sized banks to promptly address the new
regulatory requirements.
In Italy, the PUMA2 protocol will ensure a transition
consistent with the BIRD ECB initiative. The main goal
is the achievement of standardisation, uniformity and
regulatory compliance in view of the adoption of a pan-
European data model. The German Bundesbank envisages
a standardisation of the IT systems in the context of the
ERF and awaits the results of the discussion at the level of
the statistics committee of the European System of Central
Banks.
Source: European Central Bank
10
AnaCreditHow Avantage Reply can help
How Avantage Reply can helpWith extensive experience and expertise in risk
management, risk data and risk and regulatory reporting,
Avantage Reply is well placed to assist our clients in the
implementation of AnaCredit. Having already assisted
clients with end-to-end solutions, we have experience
in developing infrastructure (e.g. systems architecture,
process and control design, data management) that
meets ECB and EBA requirements. Avantage Reply also
provides services across other core elements of credit
risk, SSM regulatory reporting and compliance.
Avantage Reply offers a proven approach that
addresses the key challenges required by the new
reporting requirements, helping clients accelerate
the transformation needed in terms of architecture,
organisational aspects and data collection. These
aspects are essential to the successful implementation
of AnaCredit, as illustrated below.
Target architectureMoving system architecture from silo-based,
fragmented regulatory reporting capability towards
an integrated platform, enabling adherence to
multiple reporting requirements, with more
granular drill-down capability.
Re-engineering processes and proceduresRevision, remediation and optimisation of
internal processes for collecting and processing data
to align to the new reporting requirements.
Organisational aspects and data governanceMultidisciplinary approach to reporting production,
review, challenge and validation, involving integrated
management of the dataset by risk, finance,
data and IT through either a centralised or
decentralised operating model.
Due diligence for requested data setStructured gap analysis (of ‘stock’ data as well as
new lending data produced) will be necessary to
identify data issues (gaps in availability, quality
and processes), their criticality, and appropriate
remediation of these issues.
ANACREDITAnalytical
Credit Data Set
Figure 5: Challenges and solutions for AnaCredit
Target Architecture
The SSM approach is designed to evaluate the banking
group as a whole and requires a drill-down approach to
data collection and analysis with loan / instrument level
granularity. Therefore, it is necessary to design and
implement a systems architecture that features an analytical
group database, ensuring consistency between different
reporting submissions (harmonised and not harmonised)
and consistency of customer classifications. This must
also adhere to the BCBS principles for effective risk data
aggregation and reporting (‘BCBS 239’).
To achieve this, in order to enforce a better architecture to
store and process data, banks are implementing different
solutions that leverage innovative technological enablers.
In some cases, banks are focusing on system architectures
based on effective interaction between risk and finance data.
These solutions leverage the accounting and regulatory
reporting systems at group level. This approach facilitates
better compliance with BCBS 239, but requires investment
in local data warehouse and other solutions to address the
requirements of local subsidiaries and business lines.
AnaCreditHow Avantage Reply can help
Alternatively, “Data Lake” solutions involve a more
innovative rethinking of data management. Here, the
architecture provides:
• A data hub that includes all the data (structured, semi-
structured, and unstructured) of the bank, with simple
interfaces with existing systems and calculation engines,
allowing easier data interrogation; and
• A common financial and risk metric layer that makes
available the most relevant metrics and simplifies
data reconciliation. This solution can provide a better
understanding of risk data from input to final measures.
Organisational aspects and data governance
In parallel with architectural upgrades, more
stringent regulatory constraints and onerous
reporting requirements require a thorough review of
organisational aspects, culture and data governance;
focusing on these main aspects:
• Data management organisational structure: A
centralised structure can be employed in which the
Chief Data Officer (CDO) is the main responsible
function for data quality; while in a distributed
structure, “data stewards” have this responsibility
and a committee is established to monitor and report
on data quality to top management;
• Processes and relationships between thevarious roles and functions: Clear definition of
roles, responsibilities, dependencies and hand-
overs in either a centralised or distributed operating
model;
• Governance policy on data: Harmonisation and
extension of internal policies on data governance to
accommodate the more stringent requirements; and
• Reporting Model: Adequate processes established
to enable review, challenge, issue escalation,
remediation and final review of data prior to
submission.
Data gap analysis and remediation
Many banks’ current processes and data stores do not
facilitate data collection of the mandated granularity or
the required data may be unreliable (either because
of data quality issues or the absence of a controlled
extraction mechanism). Therefore, as a starting point
to implementing AnaCredit, a gap analysis will be
necessary to identify gaps in availability, quality
and processes and appropriate remediation of
these issues. As there will likely be numerous
gaps identified, an approach to assessing criticality
of data elements and prioritising remediation
must be adopted.
Re-engineering processes and procedures
In order to ensure reliable and timely data collection on
loans, it may be necessary to enhance credit workflow
tools starting from the origination phase, through to
monitoring, collections, provisioning and recovery.
This will ensure that the data is captured, flagged
and stored in a way that enables compliance with the
AnaCredit reporting specifications.
11
AnaCreditKey take-aways
Key take-awaysWith AnaCredit now in an operative phase, the main task of the
NCAs is to define the steps of the implementation process. All
EU countries within the SSM have adopted the regulations at the
NCA level and initiated implementation activities based on their
national discretions. As such, institutions have launched specific
projects but many of these institutions are expressing concerns
about the level of costs and complexity in achieving compliance
with the new regulations. These concerns are not unfounded as
the procurement of the required attributes, the reconsideration
of processes, organisational aspects and technical implementation
all pose difficult challenges.
For example, in Italy only significant banks have started dedicated
projects and are now assessing the main impacts. From the German
point of view, it is important that the new regime puts less burden on
the reporting by smaller banks. The results of a recent Luxembourg
banking industry study expected that the AnaCredit project
could cost smaller banks millions of euros for full compliance.
The same study noted that larger banks on the other hand might
need to completely rethink reporting systems, credit systems,
finance systems and processes to efficiently and effectively comply.
In all cases, it is clear that potential synergies and shared
dependencies with ongoing projects in regulatory reporting
must be leveraged to limit the impact on institutions’ bottom
line. A good starting point to these considerations would be
the implementation of BCBS 239 risk data aggregation techniques,
the evolution of COREP/FINREP and the implementation of IFRS 9.
The overall AnaCredit framework requires a close dialogue and
exchange of information between ECB and NCB/NCAs for efficient
implementation.
Despite these challenges, the efficient and effective implementation
of AnaCredit and the accompanying regulatory framework should
bring significant benefits as well. The overall regulatory environment
will make it easier for banks to streamline regulatory reporting
processes and focus more on value creation. As such, the direct
benefits may be estimated not only with respect to greater efficiency
of the internal systems of reporting and monitoring but also through
increasing the efficiency of lending processes making good
credit decisions guided by reliable data such as those provided
by AnaCredit. Indeed, clear and detailed information is needed
to maintain a healthy and transparent financial system, with great
advantages for both supervisory authorities and institutions.
AnaCreditAppendix
13
Appendix
Case Study: Enhancing the quality of risk reporting through the introduction of BCBS 239
The following case describes a project we undertook for
one of the largest Globally Important Systemic Banks (GSIBs)
in Europe to implement a new single reporting architecture.
The project, which had the main objective of aligning risk and
finance data streams, was key to earning a passing grade from
the regulator on initial implementation of BCBS 239.
The project required a new approach to dealing with the
organisation of data, both in the local IT systems and at the
group level. The solution revolved around the effective
interaction between risk and financial reporting streams and
the creation of a unified, single reporting stream. This had
three main areas of focus:
1) Source data: Review and enhancement of localoperational systems. Data ownership is defined at the
level of the relevant primary operational systems (e.g.
source or transactional systems) and includes responsibility
for the quality of the information that is produced. Based on
this concept, the operational system flows were enhanced
to provide a new group architecture with both risk and
financial data from the source.
2) Group architecture: A single architecture to handle bothrisk and finance needs. A new architecture at the group level
was put in place to ensure the consistent treatment of both
accounting/finance and risk data. This in turn enhanced the
quality of information to be used in reporting streams.
3) Single point of reference for regulatory reporting: Groupand local needs. The constant evolution of regulatory reporting
and regulatory information/data requests required numerous
resources at both the group level and NCA level to adapt
and adjust, proving costly and difficult to maintain quality.
With the introduction of a single point of reference for regulatory
reporting in Europe, the client has begun a natural alignment to
the SSM with a single stream of reporting.
Focus Area
Gro
up a
nd
Loca
l Rep
ortin
gN
ew G
roup
Arc
hite
ctur
eLe
gal E
ntity
Systems architecture
Local Operational/Source Systems
Figure 6: Group Level Architecture
Group Exchange Layer
1
2
Group Accounting
Platform3
Group data warehouse
6
Single Flow
Accounting Engines
4 5Risk
Engines
BI Tools / User Interface
7RegulatoryReporting
Regulator Requests, SSM Enhancements
Internal Reporting and
Monitoring
KEY POINTSDescription BCBS Principle
1. High priority is placed on specifications for the sourcing of Accounting and Extra-Accounting data from the Legacy
2. An Exchange Layer has the dual role of formatting data and performing controls (consistency and coherence)
3. Data is integrated into the Group Accounting Platform where it is made available to Data Consumers
4. Accounting Engines determine key figures, such as Debit and Credit classes
5. Risk Engines (feed only after Accounting figures have been established) calculate Key Risk Indicators, such as RWA and Liquidity
6. A single DWH collects data after all calculations have been performed and is made accessible to the User through BI tools
7. Reporting Systems, both Internal (ex. surveillance) and External (ex. Corep and Finrep) are fed from a single entity
4. Completeness5. Timeliness
3. Accuracy and Integrity
3. Accuracy and Integrity4. Completeness8. Comprehensiveness
6. Adaptability7. Accuracy of Reporting9. Clarity and Usefulness
6. Adaptability7. Accuracy of Reporting9. Clarity and Usefulness
3. Accuracy and Integrity4. Completeness8. Comprehensiveness
10. Frequency11. Distribution
AnaCreditAppendix
14
Case Study: Recovery data on NPL positions
The following case study illustrates the approach followed during
the due diligence activities on non-performing loans (’NPL’)
positions (managed by the work-out department) in respect
of recovery information on collateral, assets, valuation, with a
particular focus on credit risk.
The scope of the work focused on positions with a lack of
information or unclear articulation of the links between credit
line, collateral and assets. Additionally, the work ensured
that all NPL positions secured by a real estate property were
appraised by qualified valuation expert in order to realise the
benefits of such valuations and the transparency required in
the reporting to regulators.
Our team was comprised of legal experts, consultants and
loan work-out managers, and performed the following
activities:
1) Detailed investigation of available data/informationA careful examination of the documentation available on
paper files or data housed in IT systems was performed in
order to ascertain the consistency and validity of the credit
lines secured by collateral (real estate and financial).
2) Enhancement of the data set within IT systemsFollowing the analysis, a data room was established to
facilitate data cleansing within IT systems to make them
consistent with those present in the dossier. The purpose of
this step was to align the IT system and ensure data accuracy,
quality and completeness with the statistical and supervisory
reporting framework.
Moreover, to support the activities of the data room, tracking
and monitoring tools were prepared, with the aim of creating
periodic reports on the progress of work.
The arrangement of the data thus prepared, however, is
relevant not only on the information pertaining to the stock
of existing loans; but also to ensure the correct acquisition of
information on new loans. This was also necessary to improve
the on-going processes and procedures.
Figure 7: Action plan on data collection
Recoveries data set• Identification of the perimeter• Deploy approach (due diligence or automatic
interventions)• Integrity and data quality checks
Main areas affected• Collateral (Performing and Non Performing)• Assets• Valuations
1 2 3 4Set up working team Due Diligenceactivities
IT systems update Quality check
Recovery data on stock
Data collection on-going
Structural rethinking of end-to-end credit process for the correct acquisition of the data to the new reporting system
Main areas affected• Preliminary phase and Loan deliberation• Collateral Acqusition• Workout actions
1 2 3 4Credit Risk Policies
Loan Granting Monitoring Workout
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