1. Data Issues Confronting Advanced IRB Implementation The Risk Management Association’s Advanced IRB Symposium Scott Dillman June 19, 2003

2. 2 Agenda  Introduction  CP 3 Data Requirements and Implications  Develop a Data Management Approach Acquisition, Maintenance and Distribution Acquisition, Maintenance and Distribution Data Quantity versus Data Quality Data Quantity versus Data Quality  Establish Data Management Roadmap Strategic Approach Strategic Approach Tactical Plans Tactical Plans  Conclusion

3. 3 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………

4. 4 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

5. 5 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

6. 6 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

7. 7 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 Technology Process Policies Governance Primary Characteristics Supporting Characteristics Data Management Framework Data Approach

8. 8 Data Acquisition activities center around the identification and extraction of data from source systems Items 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

9. 9 Data Maintenance tasks are often overlooked and significantly impact data quality Items 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

10. 10 Data Quality Process Overview DEFINEASSESSSUSTAIN 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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12. 12 Address Correction -(Code1, PostalSoft, etc.) Standardized Records Step1 Corrected Records Address Correction Exceptions Address Exception Handling Record Matching & Survivorship - Vality or Trillium Matched/ Cleansed Records Validation Filter - Informatica Validation Filter Exceptions Valid Cleansed Records Validation Exception Handling Output Formatting & Cross Reference Build - Informatica Cross Reference Valid Cleansed Records Source System Feeds Code Exceptions Standardized Records Step1 Metrics Reformat to Common Record Layouts - Informatica Std Input Records Lexical & Syntax Standardization - Vality or Trillium External Data Sources Input File Reject Notice Standardized Records Step1 Domain Standardization - Informatica Cross Reference Code Exception Handling Cleansing Cleansing Rules Domain Values Metadata InputOutput Process/ Tools Web App. Color Key Data Cleansing Factory Process Flow Data Cleansing is only one part of Data Maintenance Data Approach Rules & Values

13. 13 Processes & People to Maintain Credit Data Integrity Senior Management Credit Analysts Credit Control Data Integrity Analysts Operations Product 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

14. 14 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

15. 15 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

16. 16 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

17. 17 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 Accuracy Credit Data Credit DataManagement Management Cleansing Mapping Transformation Processes Quality Model Controls Retention Structure Availability Standards Conclusion

18. 18 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……..

19. 19 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 Roadmap Strategic Approach Strategic Approach Tactical Plans Tactical Plans Conclusion

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