Can data standardisation drive regulatory harmonisation?
BY William Coen, Former Secretary General of the Basel Committee on Banking Supervision (BCBS) and Chief Regulatory Advisor at Suade AND Dr Maximilian Dyck, Financial Regulatory Expert at Suade. Published in June 2022.
Achieving regulatory harmonisation across industries and regions of the world would confer significant benefits for many different stakeholders, both private and official sector alike. Among the key benefits are cost savings and efficiency gains for organisations involved in cross-border transactions. Regulatory harmonisation would reduce duplication of testing processes for new products and services, as a certification regime in one jurisdiction would be valid in the chosen target market. Equally, organisations could save resources if their compliance programmes were applicable across borders. Despite the many benefits, achieving regulatory harmonisation continues to be a challenge. Significant progress has, however, been made through international collaboration, including through the Basel Committee on Banking Supervision (BCBS) in the sphere of financial services regulation.
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Data standardisation is a powerful tool that contributes to regulatory harmonisation. It leads to cost savings and efficiency gains, while ensuring comparability of outputs across jurisdictions. Data standardisation facilitates cross-border transactions and reduces maintenance costs. Like harmonised regulatory systems, standardised data ensures that data outputs are of a reliable quality, thus reinforcing trust. Data produced by banks for Pillar 3 disclosures and regulatory reporting under Basel III can be standardised in such a way as to contribute to regulatory harmonisation.
Data standardisation key benefits
- Cost savings
- Efficiency gains
- Scalability for products and services across different markets
- Cross-border compliance programmes
- Facilitate cross-border transactions
- Comparability and reuse of data
- Lower maintenance costs associated with data
- Trust in data reliability
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