DATA MANAGEMENT
The ABS Standing Committee on Data Management (SCDM) was established in November 2019 to:
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Promote collaboration across the banking sector within Singapore and beyond on data management issues faced by member banks;
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Facilitate engagement of relevant stakeholders to enable the effective use of data for better delivery of financial services by member banks through:
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Work with MAS and appropriate government agencies for industry-level engagement to drive data-driven innovation in Singapore and support the growth of Singapore as an international financial centre.
SCDM prioritised its future work and the key area of focus was on a framework to facilitate data sharing between banks and ecosystem partners. This Data Sharing Handbook For Banks and Non-Bank Data Ecosystem Partners ("Handbook") is a guide for Banks and their ecosystem partners to adopt a standardised approach and common language for the sharing of data to ensure that data sharing in the financial services industry is underpinned by trust and security, and in line with existing regulations, with the aim of encouraging greater collaboration and data-driven innovations in the banking sector.
1) The Handbook covers:
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An example of the Data Sharing Journey that banks and ecosystem partners typically embark on when looking to share or acquire data, and key considerations along the way.
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Data Sharing Principles that can help ground the decisions that organisations, both banks and non-banks, make along their data sharing journeys.
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Guidance on the types of data that banks have, the classification of shared data according to sensitivity, as well as examples of data sharing mechanisms and privacy preserving technologies.
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Key legal and regulatory considerations that organisations should take into account when looking to engage in data sharing with banks in Singapore, as well as guidance on the elements and types of data sharing contracts.
2) In July 2021, the ABS as the Chair of Committee on Cooperation in Finance, Investment, Trade and Technology (COFITT), led an ASEAN-level Taskforce comprising the local national banking associations under the umbrella of ASEAN Bankers Association (“ABA”), curated an interoperable data framework for the ASEAN banking community. The objective of this framework was to:
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Foster innovation in financial services - It aims to improve the financial inclusion through greater exchanges of information through trusted data flows driving transparency in credit worthiness and risks. With the innovation fostered, it increases customised services and processes to cater for different market segments.
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Establish interoperable standards for data sharing – the standards help to improve integrity of information shared with agreed standards of data quality and metadata. Such data standards increase consistency for data collection and data processing and drive greater accountability and transparency on use of data.
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Foster collaboration - this encourages a culture of collaboration to drive efficiencies and effectiveness through data sharing, such as in financial crime and fraud detection etc.
We are now pleased to share the publication of this ASEAN Banking Interoperable Data Framework.
3) Generative AI (Gen AI) has empowered banks to enhance their creative capabilities, make their processes more efficient, and explore innovative solutions across their businesses. To manage the risks and potential challenges of GenAI use, members of the Association of Banks in Singapore (ABS) Standing Committee for Data Management (SCDM) came together to collectively identify guardrails to address the specific risks that may arise when rolling out Gen AI.
This Whitepaper will serve as an initial guide based on the experience of ABS SCDM members to-date, and will continue to evolve with new developments in Gen AI and its associated risks. This document eventually will support the development of the AI Governance Handbook as part of Project MindForge, an industry AI initiative supported by the Monetary Authority of Singapore (MAS).
The guardrails Whitepaper and supplementary materials will be published in the first quarter 2025, that will provide in depth how banks may select the appropriate guardrails for their situation based on characteristics and level of risk of their use of Gen AI. Additionally these materials will articulates where and how the respective controls could be used along the Gen AI system lifecycle, and illustrates a range of specific implementation controls that a bank can apply in practice.