Programme in preparation

Data Management & Data Quality for Risk and Finance (BCBS 239 RDARR) - Virtual Classroom

Objectives

One of the most significant lessons learned from the global financial crisis is that banks’ IT and data architectures were inadequate to aggregate and report risk exposures quickly and accurately at the bank level, across business lines and between legal entities. In response to this, the Basel Committee published BCBS 239 RDARR – Risk Data Aggregation and Risk Reporting (January 2013) providing banks with a set of principles designed to strengthen their risk data aggregation capabilities and adaptability of their risk reporting practices. The latest progress report (BCBS – 29th of April 2020) showed that none of the banks are fully compliant yet and still struggling.

COVID 19 showed once again that a lot of banks face difficulties to aggregate risk data in an accurate, timely and consistent way in times of crisis. As reliable data is a fundamental precondition for a comprehensive risk management and for adequate decision-making in banks, supervisors will continue to pay close attention to institutions’ data quality, risk data aggregation and risk reporting capabilities. If needed appropriate measures will be taken.

Last but not least, changes in business and technology including developments such as fintech and cloud technologies (increasing costs and complexity), are an additional challenge for outsourced data-related processes against the backdrop of a growing use of third-party support.

By the end of this course participants will be able to:

  • Grasp the importance of high quality data and reporting
  • Understand the regulatory requirements and supervisory assessment process
  • Gain a better knowledge of the challenges banks are facing when aggregating risk data following the BCBS 239 principles
  • Install a data management framework for high data quality
  • Implement an appropriate data governance with clear definition of roles and responsibilities
  • Learn about the practical steps (including tools and architecture)
  • Gain insight into the key success factors and how to overcome challenges
  • Understand the road towards a more harmonised reporting
  • Get familiar with the latest developments of big data, RPA and AI

Target Group

This course is primarily intended for those working in the following areas: Risk, Finance, IT, Data, Internal or External Audit, Supervision, etc… However we welcome anyone to whom the training would be of benefit. Having an understanding of risk management, finance and banking fundamentals is a plus.

Virtual Classroom : 16 hours in total, split over 4 sessions. Each session will cover 3 hours of concepts and 1 hour of practice.

Detailed programme Explode

I. Introduction to Data Management

1.1 Role of Data: “Data is the new water”

1.2 Collection and storage of data

1.3 Importance of Data Quality

 

II. Data Management and Risk Regulation / Financial Reporting

2.1 Overview of Global Regulatory Roadmap

2.2 BCBS 239 – 14 principles

2.3 Links with the Basel Framework – Pillar Two / SREP

2.4 Progress of implementation

2.5 Regulatory initiatives related to data quality

 

III. Data Management Framework

3.1 Dimensions of Data Quality

3.2 Definition of Data Elements and Data Perimeters

3.3 Data Traceability: business traceability and technical lineage

3.4 SLAs between providers and receivers of information

3.5 Dimensions of Data Quality

3.6 Data quality controls and indicators

3.7 Data Quality: costs and benefits; the link with capital and liquidity + examples

3.8 Data quality dash boarding + examples

 

IV. Data Governance

4.1 Roles & Responsibilities: CDO, Data Managers, DQ Analyst, Operational teams, …

4.2 Three Lines of Defence

4.3 Reporting governance

 

V. Business Case: implementation

5.1 How to organize a self-assessment?

5.2 What are the tools to support data governance?

5.3 How to organize the governance, remediation and escalation of data quality issues

5.4 How to transform a complex architecture into a data hub / data lake approach?

5.5 How to set up a RDARR program in an organization?

5.6 How to organize reporting and stress testing in times of crisis (e.g. COVID 19)?

5.7 What are the challenges of implementation? How to move to a Business as Usual “BAU” mode?

 

VI. Latest Developments

6.1 RPA, machine learning and artificial intelligence

6.2 Big data revolution and business intelligence

6.3 Data management in an agile world

6.4 Towards more harmonization in EU reporting: BIRD, ERF, SDD, IREF

6.5 High stakes for CFO, CRO and CDO

 

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