BCBS 239 Compliance - Risk Data Aggregation and Risk Reporting Framework
Location: Prague, NH Hotel Prague
Lecturer: Dr. Krassimir Kostadinov
Key points / questions answered:
What is BCBS 239 and how does it relate to other regulations (Basel III / IFRS 9)?
How to implement the risk reporting completeness principle?
How to assess data accuracy and improve data quality in practice?
What tools and business processes are appropriate to meet adaptability requirements?
Which innovative technologies might help the bank with BCBS 239 and which not?
What could be the overall structure of a BCBS 239 project at the bank?
The purpose of this seminar is to introduce you to a range of methodologies for practical application of the 14 Principles for Effective Risk Data Aggregation and Risk Reporting by the Basel Committee on Banking Supervision (BCBS 239).
We start with a general overview and discussion of the principles, including their relationship to other important regulatory developments, such as ECB's AnaCredit, Basel III and IFRS 9. In the context of the need to accommodate to the ever changing internal and supervisory policies, we present several benchmarks and best practices in areas such as BCBS project set-up, risk data governance, and data architecture.
We then take a closer look at the principle of Completeness, which requests the bank to aggregate and report all material risk types across any relevant dimension such as business line, legal entity, asset type, industry, region and other groupings. We introduce a set of pragmatic KPIs designed to provide a bridge between the risk types and the economic performance of the bank. We explain how, on the one hand side, these KPIs can be calculated only given the complete and granular risk data as prescribed by BCBS 239, and, on the other hand side, how the resulting framework can substantially improve the bank's capability to take decisions in an unbiased, data-driven way.
After this, we turn to the principle of Accuracy and Integrity, which refers to the ability of the bank to produce data and reports on a largely automated basis in order to minimise the probability of errors. We start with a generic framework for assessment of data accuracy and give specific examples and mini-case-studies on how this framework can be applied in practice. We proceed with a description of two pragmatic methods for data quality improvement and apply them to solve common problems that exists in many banks' data-warehouses.
Extrapolating upon the topic of automation, we introduce the principle of Adaptability, which requests the bank to be ready to generate a broad range of on-demand, ad-hoc risk management reports, including requests during stress/crisis situations. On that topic, we introduce several examples for tools and practices that facilitate the reporting adaptability while enhancing Timeliness, Clarity and Usefulness (which are further principles in BCBS 239). In particular, we give a high-level introduction of several innovative technologies which, when applied sensibly, might prove extremely useful for the bank with respect to risk reporting and risk data aggregation.
Finally, we look at the principles in combination. We describe a framework for Risk Data Governance and give practical insights on the development and implementation of IT systems and business processes under consideration of business continuity planning, impact analysis capability, risk taxonomies, ownership and data quality controls.
09.00 - 09.15 Welcome and Introduction
09.15 - 12.00 Overview of BCBS 239
Brief history and scope of the regulation
Lessons learned from the Global Financial Crisis
Overview of the principles
Relationship with other regulatory developments
Basel III, IFRS 9
Benchmarks and Best Practices
BCBS 239 Project set-up
Risk Data Governance
12.00 - 13.00 Lunch
13.00 - 16.30 The Completeness Principle
What is a complete risk report?
Risk, the bank's balance sheet and risk-adjusted performance measurement
Framework for risk measurement: baseline and scenario generation
Operational Risk & Operational Costs
Funding and Liquidity Risk
Other risk types
Calculation examples and details on risk-adjusted performance measures
Gross & Net Margins
Example reports and Q&A
09.00 - 09.15 Recap
09.15 - 12.00 The Accuracy and Integrity Principle
How to assess data accuracy?
Data-driven accuracy KPIs
Sample analysis approach
Stress test approach
How to improve data quality?
Handling dimensional hierarchies
Case Study: robust improvement of customer address (location) data
Handling portfolio segmentations
Case Study: optimal portfolio segments for the IFRS 9 Impairment Model.
12.00 - 13.00 Lunch
13.00 - 16.30
The Adaptability Principle
Best Practices illustrated
Overcoming the data silo problem
On-demand and ad-hoc risk management reports
Exercises and Q&A
Other Principles: Timeliness, Clarity, Usability
Which innovative technologies might prove to be helpful?
Large-scale scenario simulations on cloud infrastructure