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02 Oct 2024
With the growing momentum of digitalisation impacting every facet of the industry, CACEIS’s Nicolas Godfrin and CIBC Mellon’s Cynthia Shaw-Pereira talk to Justin Lawson about the “back office of the back office” — reference data
Image: imagewell10/stock.adobe.com
How has the transition to T+1 settlement cycles influenced the management and utilisation of reference data within your firm?
Cynthia Shaw-Pereira: The shift to T+1 settlement cycles has significantly heightened the need for precise and timely reference data. At CIBC Mellon, this transition necessitates a more robust data governance framework to ensure that all data is accurate, up-to-date, and seamlessly integrated across our systems. By implementing advanced data validation and reconciliation processes, we can minimise settlement failures and enhance operational efficiency.
We have not seen any real spikes in settlement fails after T+1 was implemented, which is a testament to our organisation’s readiness and the strength of our technology. This focus on data accuracy and timeliness is critical in maintaining compliance, managing risks, and delivering superior service to our clients.
Nicolas Godfrin: Transitioning to T+1 settlement cycles was not a major challenge for our organisation in terms of managing reference data. All relevant reference data is already available in our systems, allowing us to process the trades received from our clients for the entire range of services we offer.
Can you elaborate on the challenges faced in ensuring real-time accuracy of reference data across global financial systems and how your organisation is addressing these challenges?
Godfrin: To ensure accuracy and consistency of reference data across CACEIS’s network, we use unified and centralised applications that provide a single source of truth. This setup is a key part of our strategy that ensures we disseminate accurate reference data throughout the group. These applications use automated controls and specific algorithms to validate the many internal and external sources of information. They are also interfaced with downstream systems to ensure any corrections are disseminated in near real-time. Achieving real-time operation for reference data management is not actually the target for our organisation, as certain critical or very specific datasets will always need to be reviewed by our in-house data experts prior to being injected into the systems.
Shaw-Pereira: Ensuring real-time accuracy of reference data across global financial systems is a complex challenge due to the sheer volume and diversity of data sources. CIBC Mellon addresses this by leveraging cutting-edge technology solutions that enable real-time data ingestion, validation, and synchronisation across our global network.
Our enterprise invests in advanced machine learning and algorithms to detect anomalies and ensure data consistency, while our integrated data platforms facilitate data sharing and collaboration across different geographies and systems. Continuous investment in technology and a commitment to data excellence are key to overcoming these challenges.
How is the adoption of emerging technologies, such as artificial intelligence, automation and blockchain, transforming the landscape of reference data management?
Shaw-Pereira: The rapid rise of generative artificial intelligence (AI) into not only the consciousness, but into the daily lives of both the business world and the general public though 2024, is the latest in a string of examples of how new technologies are accelerating exponentially and shaking up linear plans for how organisations expect to operate.
Emerging technologies are revolutionising reference data management. AI and machine learning enable us to automate data validation, cleansing, and enrichment processes, significantly reducing manual intervention and errors. Blockchain technology offers a secure, transparent, and immutable ledger for managing data, enhancing trust and reducing reconciliation efforts. Automation tools streamline workflows and improve efficiency, allowing our teams to focus on higher-value tasks. These technologies collectively transform our data management practices, driving greater accuracy, efficiency, and innovation.
The spike in opportunities and risks underscores the need for digitalisation and innovation. At CIBC Mellon, we are working to help clients at the forefront of these changes to help serve them better.
The asset servicing business is designed to resist change as clients expect us to mitigate risk, process vast amounts of transactions efficiently and help safeguard trillions of dollars of assets on behalf of investors. Clients nonetheless count on us to help them advance their businesses and deliver innovative new offerings. This intersection between relentless focus on governance while simultaneously fostering innovation on an industry-wide scale means we need to work with our clients to go beyond evolving any single product or system to understand and invest against the future needs of the industry as a whole. To move forward while delivering each and every day, we are working in closer collaboration with our clients, but also alongside technology firms, consultants, fintechs and in order to deliver a superior client experience.
We are doing more and being more for our clients — investing in their growth and ultimately their goals.
Godfrin: We have always paid close attention to technological advancements that could potentially enhance processes, and ultimately client servicing, within our organisation. Concerning reference data, we are currently analysing the market offer — from smaller fintechs to larger technology providers with native cloud solutions — to ensure we are in a strong position to tackle industry challenges such as increasing volumes and complexity, expectations of timeliness, and clients’ needs for data including data services.
AI is currently a key area of interest for CACEIS and we are looking into solutions that can retrieve reference data from documents that lack standardised formatting such as a fund prospectus. We also believe that AI will play a role in improving certain verifications we perform on reference data.
What strategies have proven effective in achieving seamless integration and interoperability of reference data across disparate systems and platforms?
Godfrin: Reference data management requires specific expertise in IT aspects and business usage. For the management to be efficient and accurate, a centralised model is essential. Centralisation ensures consistency across our ecosystem and enables us to be more responsive to client requests, product team needs, and importantly, changing regulation. Having a centralised reference data management strategy does not prevent us from being flexible, and we can easily handle local market specificities. Furthermore, our strong data governance framework enables us to rapidly adapt to very precise needs — even permitting internal clients to directly submit data requests themselves.
Shaw-Pereira: One of the key challenges faced by clients is the blending of investment data from both public and private market assets and across many disparate providers and systems. Our solutions seek to address this challenge head-on, streamlining processes and making it easier for clients to connect, analyse and utilise diverse data sets.
By providing robust data blending capabilities and greater connectivity to our clients’ other providers, we empower clients to make informed decisions and drive superior outcomes.
Effective strategies for achieving seamless integration and interoperability of reference data include the adoption of standardised data formats and protocols, robust data governance frameworks, and advanced integration platforms.
We envision the future of investment data management with clients’ needs at the centre, connecting systems, data and experts in a flexible network that enables clients to transform and integrate, onboard, insource and outsource capabilities as their business needs evolve. Our open platform approach underscores our willingness to collaborate with leading fintechs, demonstrating how our platform can integrate with and support collaborative solutions for clients.
By fostering connections and embracing innovation, we pave the way for groundbreaking advancements in the industry. Our goal is to help clients future proof their businesses rather than locking long-term onto a single architecture or strategy they might later outgrow.
Clients rightly have high standards, and need data to be transparent, resilient, unified, scalable and timely. Thankfully, this has been an area where our clients have been very willing to collaborate, share and to work with our teams to chart out a collaborative vision.
Could you discuss the impact of LEIs on improving transparency and reducing risks in financial transactions?
Godfrin: Legal Entity Identifiers (LEIs) have brought a significant improvement to transparency and risk in the finance industry, in the same way as ISIN codes did (especially when they became mandatory in Europe in 2004). LEI adoption has streamlined information exchange with regulators, and to a certain extent with our clients.
Since LEIs were introduced, we have also noted a major increase in the quality of data from certain suppliers. We are also monitoring the progress of the Global Legal Entity Identifier Foundation (GLEIF) as we believe its freely available data is likely to be considered the reference for many datasets going forward.
How does your organisation approach the challenge of balancing data privacy with the need for accurate and accessible reference data, particularly in cross-border operations?
Shaw-Pereira: We understand that clients may have questions about the availability, implementation, and cost of our solutions. Additionally, concerns about data privacy, model accuracy, and regulatory compliance are paramount.
Balancing data privacy with the need for accurate and accessible reference data is a critical challenge, especially in cross-border operations. Any systems and processes need to be scalable and robust — so processes that are repeatable, optimised, and upgradeable.
Our data governance policies are designed to ensure that access to reference data is tightly controlled and monitored, with stringent data sharing agreements in place to comply with regional data protection regulations. By prioritising data privacy while maintaining data accuracy and accessibility, we can effectively support our global operations.
Data governance is never really ‘solved’, since clients need to be able to integrate new sources of information, as well as evolve their data strategies to meet shifting business needs. Data needs to be accessible, visible and traceable — giving clients the ability to monitor, understand and follow sources.
We remain committed to providing attentive support and guidance as clients continue their journeys, especially as they seek to incorporate more modern tools from our enterprise. Whether it is blending proprietary data into the models or auditing and testing information for accuracy.
Godfrin: This is an area we are addressing as part of our initiative to further strengthen data governance throughout our network of entities. We are leveraging new technologies to handle the increasingly complex task of data management that demands a strong data ownership framework, accurate documentation, precise data lineage, strict usage, and dissemination rights, etc.
For CACEIS, the Digital Operational Resilience Act (DORA) presents an excellent opportunity to promote the adoption of best practices by our partners. The cloud is another key consideration and can play an important role in mitigating risk associated with cyber-attacks, data breaches, and events like the recent CrowdStrike incident which impacted many organisations worldwide.
How do you foresee the role of reference data evolving in the next decade, especially with the ongoing digital transformation in the financial services industry?
Godfrin: In the past, reference data management was perceived as the ‘back office of the back office’ in many organisations. Nowadays, as a result of the increasing digitisation of processes, reference data is considered a critical asset and the foundation of most of our services. It ensures we perform our tasks efficiently and with a level of timeliness, accuracy and flexibility that our clients expect.
Reference data is also essential for new product development efforts — especially those that leverage new technology services that are becoming available on the market.
Shaw-Pereira:The role of reference data is poised to evolve significantly over the next decade, driven by ongoing digital transformation in the financial services industry. As data volumes continue to grow and regulatory requirements become more stringent, the demand for high-quality, real-time reference data will increase.
Emerging technologies such as AI, blockchain, and advanced analytics will further enhance data management capabilities, enabling greater automation, accuracy, and insights.
Clients are at various stages of their transformation journeys, but the consensus is clear — digitalisation, data management and analytics are all increasingly in focus, and there is a growing need to do more.
At CIBC Mellon, we know that almost everything our clients do requires data. Each data attribute contributes to the way our clients make decisions, operate and report.
We anticipate that reference data will become even more integral to decision-making, risk management, and compliance efforts, ultimately driving innovation and competitive advantage in the financial services sector.
The market continues to move quickly, and we are investing to bring new technology capabilities to Canada to better support our clients’ needs.
Cynthia Shaw-Pereira: The shift to T+1 settlement cycles has significantly heightened the need for precise and timely reference data. At CIBC Mellon, this transition necessitates a more robust data governance framework to ensure that all data is accurate, up-to-date, and seamlessly integrated across our systems. By implementing advanced data validation and reconciliation processes, we can minimise settlement failures and enhance operational efficiency.
We have not seen any real spikes in settlement fails after T+1 was implemented, which is a testament to our organisation’s readiness and the strength of our technology. This focus on data accuracy and timeliness is critical in maintaining compliance, managing risks, and delivering superior service to our clients.
Nicolas Godfrin: Transitioning to T+1 settlement cycles was not a major challenge for our organisation in terms of managing reference data. All relevant reference data is already available in our systems, allowing us to process the trades received from our clients for the entire range of services we offer.
Can you elaborate on the challenges faced in ensuring real-time accuracy of reference data across global financial systems and how your organisation is addressing these challenges?
Godfrin: To ensure accuracy and consistency of reference data across CACEIS’s network, we use unified and centralised applications that provide a single source of truth. This setup is a key part of our strategy that ensures we disseminate accurate reference data throughout the group. These applications use automated controls and specific algorithms to validate the many internal and external sources of information. They are also interfaced with downstream systems to ensure any corrections are disseminated in near real-time. Achieving real-time operation for reference data management is not actually the target for our organisation, as certain critical or very specific datasets will always need to be reviewed by our in-house data experts prior to being injected into the systems.
Shaw-Pereira: Ensuring real-time accuracy of reference data across global financial systems is a complex challenge due to the sheer volume and diversity of data sources. CIBC Mellon addresses this by leveraging cutting-edge technology solutions that enable real-time data ingestion, validation, and synchronisation across our global network.
Our enterprise invests in advanced machine learning and algorithms to detect anomalies and ensure data consistency, while our integrated data platforms facilitate data sharing and collaboration across different geographies and systems. Continuous investment in technology and a commitment to data excellence are key to overcoming these challenges.
How is the adoption of emerging technologies, such as artificial intelligence, automation and blockchain, transforming the landscape of reference data management?
Shaw-Pereira: The rapid rise of generative artificial intelligence (AI) into not only the consciousness, but into the daily lives of both the business world and the general public though 2024, is the latest in a string of examples of how new technologies are accelerating exponentially and shaking up linear plans for how organisations expect to operate.
Emerging technologies are revolutionising reference data management. AI and machine learning enable us to automate data validation, cleansing, and enrichment processes, significantly reducing manual intervention and errors. Blockchain technology offers a secure, transparent, and immutable ledger for managing data, enhancing trust and reducing reconciliation efforts. Automation tools streamline workflows and improve efficiency, allowing our teams to focus on higher-value tasks. These technologies collectively transform our data management practices, driving greater accuracy, efficiency, and innovation.
The spike in opportunities and risks underscores the need for digitalisation and innovation. At CIBC Mellon, we are working to help clients at the forefront of these changes to help serve them better.
The asset servicing business is designed to resist change as clients expect us to mitigate risk, process vast amounts of transactions efficiently and help safeguard trillions of dollars of assets on behalf of investors. Clients nonetheless count on us to help them advance their businesses and deliver innovative new offerings. This intersection between relentless focus on governance while simultaneously fostering innovation on an industry-wide scale means we need to work with our clients to go beyond evolving any single product or system to understand and invest against the future needs of the industry as a whole. To move forward while delivering each and every day, we are working in closer collaboration with our clients, but also alongside technology firms, consultants, fintechs and in order to deliver a superior client experience.
We are doing more and being more for our clients — investing in their growth and ultimately their goals.
Godfrin: We have always paid close attention to technological advancements that could potentially enhance processes, and ultimately client servicing, within our organisation. Concerning reference data, we are currently analysing the market offer — from smaller fintechs to larger technology providers with native cloud solutions — to ensure we are in a strong position to tackle industry challenges such as increasing volumes and complexity, expectations of timeliness, and clients’ needs for data including data services.
AI is currently a key area of interest for CACEIS and we are looking into solutions that can retrieve reference data from documents that lack standardised formatting such as a fund prospectus. We also believe that AI will play a role in improving certain verifications we perform on reference data.
What strategies have proven effective in achieving seamless integration and interoperability of reference data across disparate systems and platforms?
Godfrin: Reference data management requires specific expertise in IT aspects and business usage. For the management to be efficient and accurate, a centralised model is essential. Centralisation ensures consistency across our ecosystem and enables us to be more responsive to client requests, product team needs, and importantly, changing regulation. Having a centralised reference data management strategy does not prevent us from being flexible, and we can easily handle local market specificities. Furthermore, our strong data governance framework enables us to rapidly adapt to very precise needs — even permitting internal clients to directly submit data requests themselves.
Shaw-Pereira: One of the key challenges faced by clients is the blending of investment data from both public and private market assets and across many disparate providers and systems. Our solutions seek to address this challenge head-on, streamlining processes and making it easier for clients to connect, analyse and utilise diverse data sets.
By providing robust data blending capabilities and greater connectivity to our clients’ other providers, we empower clients to make informed decisions and drive superior outcomes.
Effective strategies for achieving seamless integration and interoperability of reference data include the adoption of standardised data formats and protocols, robust data governance frameworks, and advanced integration platforms.
We envision the future of investment data management with clients’ needs at the centre, connecting systems, data and experts in a flexible network that enables clients to transform and integrate, onboard, insource and outsource capabilities as their business needs evolve. Our open platform approach underscores our willingness to collaborate with leading fintechs, demonstrating how our platform can integrate with and support collaborative solutions for clients.
By fostering connections and embracing innovation, we pave the way for groundbreaking advancements in the industry. Our goal is to help clients future proof their businesses rather than locking long-term onto a single architecture or strategy they might later outgrow.
Clients rightly have high standards, and need data to be transparent, resilient, unified, scalable and timely. Thankfully, this has been an area where our clients have been very willing to collaborate, share and to work with our teams to chart out a collaborative vision.
Could you discuss the impact of LEIs on improving transparency and reducing risks in financial transactions?
Godfrin: Legal Entity Identifiers (LEIs) have brought a significant improvement to transparency and risk in the finance industry, in the same way as ISIN codes did (especially when they became mandatory in Europe in 2004). LEI adoption has streamlined information exchange with regulators, and to a certain extent with our clients.
Since LEIs were introduced, we have also noted a major increase in the quality of data from certain suppliers. We are also monitoring the progress of the Global Legal Entity Identifier Foundation (GLEIF) as we believe its freely available data is likely to be considered the reference for many datasets going forward.
How does your organisation approach the challenge of balancing data privacy with the need for accurate and accessible reference data, particularly in cross-border operations?
Shaw-Pereira: We understand that clients may have questions about the availability, implementation, and cost of our solutions. Additionally, concerns about data privacy, model accuracy, and regulatory compliance are paramount.
Balancing data privacy with the need for accurate and accessible reference data is a critical challenge, especially in cross-border operations. Any systems and processes need to be scalable and robust — so processes that are repeatable, optimised, and upgradeable.
Our data governance policies are designed to ensure that access to reference data is tightly controlled and monitored, with stringent data sharing agreements in place to comply with regional data protection regulations. By prioritising data privacy while maintaining data accuracy and accessibility, we can effectively support our global operations.
Data governance is never really ‘solved’, since clients need to be able to integrate new sources of information, as well as evolve their data strategies to meet shifting business needs. Data needs to be accessible, visible and traceable — giving clients the ability to monitor, understand and follow sources.
We remain committed to providing attentive support and guidance as clients continue their journeys, especially as they seek to incorporate more modern tools from our enterprise. Whether it is blending proprietary data into the models or auditing and testing information for accuracy.
Godfrin: This is an area we are addressing as part of our initiative to further strengthen data governance throughout our network of entities. We are leveraging new technologies to handle the increasingly complex task of data management that demands a strong data ownership framework, accurate documentation, precise data lineage, strict usage, and dissemination rights, etc.
For CACEIS, the Digital Operational Resilience Act (DORA) presents an excellent opportunity to promote the adoption of best practices by our partners. The cloud is another key consideration and can play an important role in mitigating risk associated with cyber-attacks, data breaches, and events like the recent CrowdStrike incident which impacted many organisations worldwide.
How do you foresee the role of reference data evolving in the next decade, especially with the ongoing digital transformation in the financial services industry?
Godfrin: In the past, reference data management was perceived as the ‘back office of the back office’ in many organisations. Nowadays, as a result of the increasing digitisation of processes, reference data is considered a critical asset and the foundation of most of our services. It ensures we perform our tasks efficiently and with a level of timeliness, accuracy and flexibility that our clients expect.
Reference data is also essential for new product development efforts — especially those that leverage new technology services that are becoming available on the market.
Shaw-Pereira:The role of reference data is poised to evolve significantly over the next decade, driven by ongoing digital transformation in the financial services industry. As data volumes continue to grow and regulatory requirements become more stringent, the demand for high-quality, real-time reference data will increase.
Emerging technologies such as AI, blockchain, and advanced analytics will further enhance data management capabilities, enabling greater automation, accuracy, and insights.
Clients are at various stages of their transformation journeys, but the consensus is clear — digitalisation, data management and analytics are all increasingly in focus, and there is a growing need to do more.
At CIBC Mellon, we know that almost everything our clients do requires data. Each data attribute contributes to the way our clients make decisions, operate and report.
We anticipate that reference data will become even more integral to decision-making, risk management, and compliance efforts, ultimately driving innovation and competitive advantage in the financial services sector.
The market continues to move quickly, and we are investing to bring new technology capabilities to Canada to better support our clients’ needs.
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