BCBS 239: Effective risk data aggregation and reporting
View AgendaKey reasons to attend
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Apply best practice to achieve aggregated view of data quality
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Understand how to manage data risk across the business
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Focus on sustainable and profitable ways to integrate and utilise BCBS 239
Customised solutions
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About the course
This comprehensive learning event will enable participants to develop an understanding of the supervisory overview for data risk compliance and the impact of BCBS 239 on financial institutions.
Subject-matter experts will provide valuable insight into managing data risk and enhancing data reporting frameworks. Participants will also explore the BCBS 239 principles of data accuracy, completeness and timeliness, and learn how to best manage their data architecture and aggregation.
Interactive discussions and a case study will include practical examples of BCBS 239 and exploration of the key challenges and opportunities that arise after implementation.
Flexible pricing options:
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Early-bird rate: book in advance and save $200
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3-for-2 group rate: book three delegates for the price of two and save more than $2,000
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Season tickets: book a team of 10 or more and save up to 50%
Learning objectives
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Identify an effective data framework for data governance
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Explore the consequences of non-compliance to BCBS 239
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Employ best practice approaches to overcome challenges in risk reporting
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Identify and define the principles for effective data aggregation
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Learn how to speed up decision-making by improving information availability
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Discover how artificial intelligence can enhance data aggregation and reporting
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Mitigate the severity of losses resulting from risk management weaknesses
Who should attend
Relevant departments may include but are not limited to:
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Reporting
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Enterprise data management
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IT strategy
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Data architecture
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Credit/Market/Liquidity risk
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Operational risk
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Compliance and regulation
Agenda
October 30–November 1, 2023
Live online. Timezones: Emea/Americas
Sessions:
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Overview of BCBS 239
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Risk data reporting
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Data architecture and aggregation
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Managing data quality
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Artificial intelligence and machine learning application in data aggregation and reporting
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Case study: challenges and opportunities after implementation
Tutor:
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David Power, Director, Credit Suisse
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Randall Gordon, Head of data governance, Cross River
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Navin Rauniar, Partner, Tata Consultancy Services
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Jay Krish, Head of AML and FCC data governance, State Street
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Ali Adeli, ESG project manager, Janus Henderson Investors
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
Pre-reading materials
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