Data Issues

Confronting Advanced IRB Implementation

The Risk Management Association’s Advanced IRB Symposium

Scott Dillman

June 19, 2003

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Agenda

IntroductionIntroduction

CP 3 Data Requirements and ImplicationsCP 3 Data Requirements and Implications

Develop a Data Management ApproachDevelop a Data Management Approach

• Acquisition, Maintenance and DistributionAcquisition, Maintenance and Distribution

• Data Quantity versus Data QualityData Quantity versus Data Quality

Establish Data Management RoadmapEstablish Data Management Roadmap

• Strategic ApproachStrategic Approach

• Tactical PlansTactical Plans

Conclusion Conclusion

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Introduction - Where do “we” stand ?

Financial Institutions worldwide are in the midst of implementing the Basel II Accord

30-35 % of the US institutions have addressed the data implications. Banks face challenges particularly around data structures and cleansing

Few institutions have yet developed a disclosure strategy, which will impose additional data challenges

Cost implications for data management and warehouse efforts range between $5 and $20 million whereas estimations for cleansing efforts are between $3 and $10 million (Estimates for Top 150 Banks)

Data collection efforts have started………

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Data Requirements for the IRB Implementation

Banks must collect and store data on rating decisions, histories of borrowers, probabilities of default and rating migration to track the predictive power of the rating system.

A history of PD and realized default rates associated with each grade must be retained.

Retain data used in the process of allocating exposures to pools, including data on borrower and transaction risk characteristics used either directly or through use of a model, as well as data on delinquency.

Banks must disclose their method of calculating their minimum capital requirement and the key assumptions of PD and loss given default (LGD) for each portfolio.

The data and capital calculators must be auditable.

Changes in methods and data (both data sources and periods covered) must be clearly and thoroughly documented.

Internal Audit has to review the accuracy and completeness of position data and the verification of the consistency, timeliness and reliability of data sources used to run internal models, including the independence of such data sources.

CP3 Data Implications

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Data Requirements and their Implications

Auditable – Methodology, processes, data sources must be clear, transparent, consistent and fully documented. Policies, Standards and Guidelines must be in place to define data rules across the organization. Processes and documentation must be accessible.

Completeness – There may be deficiencies in current systems and processes to capture the required information for current transactions; or, there may be incomplete data for previous transactions.

Comprehensiveness – store detailed borrower, credit facility characteristics and rating data. Data must allow retrospective re-allocation of obligors and facilities to rating grades.

Consolidation – across products and of client information on small or remote systems.

Controls – covering retention of documentation, consistency of use and demonstrating completeness and accuracy of procedures. Documentation of changes over time.

CP3 Data Implications

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Data Requirements and their Implications (con’t)

Data source – model and reporting inputs must be mapped back to their original data source.

Disclosure and reporting – are the systems capable of generating the required reports and disclosures?

History – for Probability of Default, Loss Given Default, Exposure at Default and ratings. This history must be consistent across products and business lines.

Robustness – Put in place top-down standards, review procedures, transparent data flows, access controls and security, data metrics and contingency plans. Address risks and controls appropriately. Assign ownership.

Suitability – does the retained information actually reflect the transactions that took place?

CP3 Data Implications

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Implementation of a Data Management Program provides a foundation for addressing these data issues

Data Acquisition Extraction Transformation Load Business Rules Selection Criteria

Data Distribution Timeliness Frequency Format

Reporting Capabilities

Data Maintenance Quality Cleansing Accuracy Monitoring Retention Business Continuity

Distribution

Maintenance

Acquisition

Tech

nol

ogy

Pro

cess

Pol

icie

sGov

ernan

ce

Primary C

haracteristics

Supporting Characteristics

Data Management Framework

Data Approach

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Data Acquisition activities center around the identification and extraction of data from source systemsItems to consider:

• Identify source systems

• Determine technical process for extracting, calculating and converting data

• Assess strength of common identifiers

• Assess data availability

• Determine data selection criteria

• Pre-Cleanse - reconcile identifiers, classifiers, validate fields, etc.

• Reformat, decompose, and standardise

• Store with referential integrity and data validation triggers

• Establish consistent standards and business rules for data aggregation and transformation

Basel Impact:

• Locate loan, collateral, financial decision data. Allow for external data sources.

• Build multi-year data sets for both dynamic and static data.

• Map loan type, collateral and other codes from various systems.

• Structure data mart to calculate PD, LGD, EAD.

• Use of external data (e.g., FICO scores).

• Aggregate across counterparties and exposures.

• Design data flows and processes associated with measurement systems in a transparent and accessible manner.

Data Approach

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Data Maintenance tasks are often overlooked and significantly impact data qualityItems to consider:

• Supporting business processes

• Redundancy of data

• Enterprise-wide data quality standards

• Data cleansing programs

• Ongoing monitoring and review

• Retention

Basel Impact:

• Accuracy of risk assessment is directly impacted by quality and completeness of data.

• Data storage requirements e.g. ratings data since inception of relationship, LGD and EAD information.

• Maintain data on overrides of risk ratings.

• Data definitions must be consistent across the pool of historical data. New definitions, e.g. definition of default, and existing data must be mapped. Mapping must be transparent and documented.

• Retain information on all ratings decisions, who took them, which model was used, date. The information must stand up to external verification.

• Increased standards and frequency of data collection. The use of approximations and infrequent data collection as conducted today by many credit areas will not suffice.

Data Approach

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Data Quality Process Overview

DEFINE ASSESS SUSTAIN

Meet or Exceed Quality Requirements

Below Quality Requirements

Changes to Requirements

Key Risks To Be Addressed

Quality requirements are unknown or are not being addressed

Business perceives data quality levels are higher than actuality

Source data does not meet the increased level of data quality required

•Overall data quality degrades over time•No adequate control environment in place

Data Quality Process

IMPROVE

Data Approach

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Asc

end

antS

AP

Data Quality Process

Pre

para

tion

Bus

ine

ss B

lue

prin

tR

ealis

atio

nF

ina

l Pre

pG

o Li

ve

Mapping of Data Quality to

AscendantSAP

2.1 Identify Data Quality Scope

2.2 Identify Data Gaps

2.3 Identify Critical Data Elements

2.4 Define Data Quality Requirement Criteria

2.5 Define Data Quality Requirement Metrics

Define2

1.1 Understand Objectives and Scope

1.2 Define Data Quality Strategy

1.3 Create Data Quality Workplan

1.4 Estimate and Obtain Resources Required

1.5 Assess Risks

Initiate1

3.1 Develop Assessment Plan

3.2 Execute Assessment Plan

3.3 Analyze Results3.4 Perform Root

Cause Analysis

Assess3

4.1 Develop Improvement Plan

4.2 Execute Improvement Plan

4.3 Re-Perform Assessment Plan

4.4 Analyze Results

Improve4

7.1 Finalize Documentation

7.2 Update Knowledge Management Resources

Wrap-Up7

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Sustain

5.1 Develop DataQualityEnvironment

• Policies and Procedures

• Roles and Responsibilities

• Security Profiles• Application

Controls• Data Quality

Monitoring and Certification Process

• Develop Key Performance Indicators

• Training• Communication

Plan

6.1 Implement Data Quality Environment

6.2 Perform Post-Implementation Review of the Data Quality Environment

Implement6Design5

V1.0.5

Sus

tain

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AddressCorrection -(Code1,

PostalSoft, etc.)

Standardized Records

Step1

CorrectedRecords

AddressCorrectionExceptions

AddressException Handling

Record Matching& Survivorship -

Vality or Trillium

Matched/CleansedRecords

Validation Filter -Informatica

ValidationFilter

Exceptions

ValidCleansedRecords

ValidationException Handling

Output Formatting& Cross Reference

Build - Informatica

Cross Reference

ValidCleansedRecords

SourceSystemFeeds

CodeExceptions

Standardized Records

Step1

Metrics

Reformat to Common Record

Layouts - Informatica

Std Input Records

Lexical & Syntax

Standardization -Vality or Trillium

External Data

Sources

InputFile Reject

Notice

Standardized Records

Step1

DomainStandardization -

Informatica

Cross Reference

Code Exception Handling

CleansingCleansing

RulesDomain Values

Metadata

Input OutputProcess/

ToolsWebApp.

Color Key

Data Cleansing Factory Process Flow

Data Cleansing is only one part of Data Maintenance

Data Approach

Rules &Values

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Processes & People to Maintain Credit Data Integrity

Senior Management

Credit Analysts

Credit ControlData Integrity

Analysts

OperationsProduct Control

• Monitor credit approval process

• Monitor key counterparty policy compliance

• Manage credit analysts

• Credit management reporting

• Credit approval

• Counterparty maintenance

• Limit maintenance

• Static data updates

• Static data monitoring and control

• Limit monitoring

• Feed control

• Price review

• Account linking

Prices

Adjustments

Books & records

Account links

Account linking & counterparty set-up

Feed status communication

Set review flags

Request static data updates

Set review flags

Counterparty monitoring

Approval monitoring

Sign-offs

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Successful Data Distribution is dependent on clearly defined user requirements and a sound technology platform

Items to consider:

• End user requirements

• End user sophistication

• Format

• Reporting architecture

• Timeliness

Basel Impact:

• Need business staff with the technical skills to manipulate risk data.

• Data repository must have an added time dimension.

• Data must be available in raw form and must feed into reporting tools.

• Distribution model must be flexible to account for (inevitable) changes.

• Reporting systems to generate, track and report operational risk loss data by business line.

• Quality of documentation and audit trails to trace data back to source systems.

Data Approach

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There are distinct benefits of a Comprehensive Data Management Program aside from Regulatory Compliance

Conclusion

Trustworthiness of data sources provides for reliable capital calculations

A consistent understanding and use of data across the organization

Precise information will support the goal to reduce the regulatory capital

Once in place, a sound data structure is easy to access, amend and maintain

Assigned data ownership helps to maintain clean data

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The Data Management Roadmap

Conclusion

Strategic Approach

Plan A – Leverage off of enterprise wide data management program, or

Plan B – Work to establish enterprise wide data management program

Work with Sarbanes Oxley, Privacy and AML programs. Find other areas that have budget and comparable data management goals

Bottom Line – establish enterprise wide approach to Data Acquisition, Data Maintenance and Data Distribution

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Basel Data Issues – Strategic & Tactical

Basel Data Issues to address: Structure - Ownership

Data Availability

Data Model

Standards

Accuracy

Data Mapping

Data Transformation & Translation

Data Processes

Retention

Controls

Cleansing

Data Quality

AccuracyAccuracy

Credit DataCredit DataManagementManagement Credit DataCredit DataManagementManagement

Cleansing Cleansing

MappingMapping

TransformationTransformation

ProcessesProcesses

QualityQuality

ModelModel

ControlsControlsRetentionRetention

StructureStructure AvailabilityAvailability

StandardsStandards

Conclusion

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The Data Management Roadmap

Conclusion

Tactical Plans

Address data scarcity issues…. Devise approach to collect default data around corporate, bank, sovereign, and specialized lending exposure classes

Work with other institutions to collect PD and LGD data

30 % of “you” do not have the correct definition of default ….make the change…….. but how do you integrate the two data sets?

There is plenty of low hanging fruit……..

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Conclusion…….Establish the Data Management Roadmap

Understand the Data Requirements

Develop a Data Management Approach

• Acquisition – address quantity

• Maintenance – address quality

• Distribution – address access & consistency

Establish Data Management RoadmapEstablish Data Management Roadmap

• Strategic ApproachStrategic Approach

• Tactical PlansTactical Plans

Conclusion

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scott.dillman@us.pwc.com