How modern data architecture is reshaping asset servicing
10 Dec 2025
Tahlia Kraefft examines how data architecture has emerged as a key strategic differentiator for asset servicing firms, enabling growth and client service, as the sector undergoes a structural change driven by increasing regulatory pressures, diverse client needs, and fintech-led market transformation
Image: phantip/stock.adobe.com
The redesign of data pipelines, models, and governance frameworks is integral in facilitating the adoption of automation across asset servicing.
Through re-engineering — which usually involves cloud-native platforms, metadata-driven systems, and unified data models — the sector is modernising its legacy infrastructure to meet demands for improved efficiency and transparency. Data architecture has emerged as a vital driver of digital transformation, enabling real-time reporting, secure client tooling and faster settlements.
A swift evolution: Data architecture’s role in changing asset servicing
Asset servicers are shifting from siloed, monolithic systems to modular, API-driven frameworks, to overcome limitations faced by traditional legacy systems. This comes against a backdrop of pressures from clients, regulators, and markets, as systems frequently deliver delayed data, reporting inconsistencies, and settlement bottlenecks. The redesign of data architecture, alongside digitalisation is a method to improve operational resilience while increasing levels of automation.
Tom Burke, president of global asset servicing at Broadridge, comments that the asset servicing sector has been transitioning within a short span of five years, “From siloed, batch-based systems to more integrated, event-driven frameworks”.
“Yet the idea of all processes running from a single, universal data source still remains out of reach in the complex, multi-party asset servicing ecosystem. The real opportunity lies in creating connected, trusted data networks — where interoperability, governance, and real-time exchange replace the need for a single repository.”
Dan Reid, chief technology officer and founder of Xceptor, notes that the sector’s data architecture has changed within the past half decade, from batch Extract Transform Load (ETL) feeding isolated tools to cloud-first automation spanning the value chain. He remarks: “Custodians now expect to ingest any format, normalise it, and apply rules within one governed layer.
“AI is embedded directly in these pipelines, shrinking manual processing and exception queues. Traceability, recoverability, and API connectivity have become baseline requirements, not optional enhancements.
Data architecture will remain or emerge as the leading priority for asset servicing firms in the coming years, according to Timothée Raymond, head of innovation at Linedata.
He states: “Data quality drives efficiency, the pressure on cost is still a significant driver, and the rise of AI as a way to gain efficiency requires high quality data.
“Some would say the constraint on the data being structured is less important than before because of how large language models work, but actually to get to the level of accuracy the industry requires, normalising the unstructured stash of data is still important for most Retrieval Augmented Generation based approaches today.”
Contemporary data management blueprint
Modern data architecture focuses on delivering agility, scalability, resilience, and enabling near real-time reporting, advanced analytics, artificial intelligence/machine learning (ML) adoption, lower operational risk, and greater data governance and lineage. Key components of the infrastructure include: cloud-first or hybrid models, event-driven workflows, standardised data models, API-centric communication, and scalable storage for ESG, alternatives, and digital assets.
Burke says contemporary data architecture is defined by integrated event-driven frameworks that prioritise efficiency, scalability, and client-centric services. He remarks: “Such system interoperability is crucial in today’s asset servicing landscape to enable seamless communication and data exchange across diverse platforms. Efficiency, client service, and compliance demands make interoperability a critical success factor in asset servicing.”
Interoperability is the new medium of efficiency, with smooth data exchange an essential requirement for: clients’ internal platforms, fintech partners, and data vendors, and market infrastructures and Central Securities Depositories (CSDs), and International Central Securities Depositories (ICSDs). Additionally there is an increased need for ‘plug-and-play’ integration in client onboarding which includes pre-built components (like APIs) to seamlessly add services (payments, data) into a firm’s platform.
Reid describes modern architecture as involving “cloud-first, data-agnostic, AI-enabled, and workflow-aware, with open interoperability across clients, fintech partners, and market infrastructures”.
He continues: “Platform-agnostic connections to internal and external systems must be paired with native AI services and composable components that plug into an orchestration layer without duplication. The SaaS model is popular because it provides scale, security, and predictable updates.
“Interoperability remains critical: asset servicers need to meet clients where they are, minimise unnecessary data movement, and bridge heterogeneous environments to maintain service quality and grow their offering.”
Raymond defines contemporary data architecture as: “real time, API event based), normalised, internal or external standard, open and readable, and secure.”
Tackling data fragmentation
Data fragmentation persists across enterprises frequently caused by the diverse use of platforms across different regions, acquisitions, and legacy technology; often resulting in inconsistent data, hampered reporting, and operational bottlenecks. Modern data architecture aims to overcome inconsistency through: data virtualisation and metadata-driven integration, unified data models and single sources of truth, enterprise-side data governance and lineage, and Master Data Management (MDM) approaches.
Burke reasons a single system will not solve issues of fragmentation however: “By a modern, mutualised architecture and data standards that bring coherence across the ecosystem. By using shared data models, governed data lakes, and open APIs, firms can align front, middle, and back offices on consistent, trusted data — reducing duplication, reconciliation, and delays.
“True progress comes from standardised integration: market infrastructures, custodians, and technology providers must lead on standards, while asset managers and service providers adopt open, interoperable frameworks. An industry-driven, utility-enabled model builds the trust, transparency, and scalability needed to reduce fragmentation and power sustainable growth in asset servicing.”
According to Reid, “modern architecture reduces fragmentation by?standardising diverse upstream data and?centralising control. A data-agnostic platform ingests data from internal and external systems, applies business rules, and publishes clean outputs to downstream books of record with full lineage.
“Certified rule libraries replace one-off scripts, which brings order to corporate actions, reconciliations, tax and reporting. Document intelligence closes the gap on unstructured inputs.
“The result is a persistent source of truth with full lineage for both ops and IT.”
Raymond adds that data warehousing is an important means of custodians reducing fragmentation: “Defining the objects they work with, building unified view/data ownership for each piece of data is key. The evolution of market infrastructure — both on private and public markets — will also play a role, and the rise of standards or at least shared concepts such as open banking, electronic invoicing etc. will be both an opportunity and a challenge.”
Strategic pressures pushing data architecture transformation
Regulatory drivers are leading firms to reexamine their data architecture with increased transparency requirements around liquidity, ESG, and collateral reporting.
Market reforms are demanding data standardisation and real-time visibility. Client-driven pressures — such as demand for custom reporting, self-service analytics, 24/7 access to data, desire for seamless integration with clients’ systems — and market-driven pressures, including competition from fintechs and digitally native provides, costs connected with legacy system maintenance, and emerging markets for digital assets are requiring firms to upgrade their data architecture.
Burke argues that these converging regulatory, client, and market pressures, along with an explosive growth in transaction volumes (up over 23 per cent year-on-year) are demanding firms to fundamentally reconsider their data architectures.
He says: “Legacy systems can no longer manage the scale, speed, and visibility required for transparency, accelerated settlements, and real-time reporting.
“To address this, firms are considering strategies for standardised data ontologies, enhancing automation, and embracing cloud-based platforms. Such improvements will enable real-time insights, operational agility, and scalability, and will position modern data architecture as a strategic enabler for efficiency, resilience, and sustained competitiveness.
Raymond, notes of these three core pressures forcing data infrastructure evolution: “Regulation such as that of private markets in Europe, for example, is a mandatory driver, though the most important one is probably client pressure and the ability for asset servicers to differentiate though efficiency, reliability and cost.”
Reid explains core influences of the architecture transformation include: “New tax regimes such as FASTER and MiKaDiv demand clean, traceable data from capture to filing. Control frameworks now expect explainability and governance embedded in production workflows.
“Clients want faster onboarding, transparency on breaks, and seamless integration into their own platforms. Meanwhile, rising data volumes, private-asset diversity, and resilience expectations are pushing firms toward cloud scale and AI-enabled ingestion.”
A strategic lever in a competitive landscape
Custodians are pivoting from basic service providers to data-rich solutions partners.
They are engaging data as a competitive differentiator to deliver efficient onboarding and integration, enhanced client experience, improved insights and analytics and operational alpha, reduced errors and faster exception resolution.
Burke, labels it as a true differentiator: “According to a recent survey, more than two-thirds of market participants see data integration and interoperability as critical enablers for future competitiveness. The ability to integrate and mobilise data seamlessly is increasingly tied to efficiency, accuracy, and innovation capacity, and directly translates into stronger relationships and the agility to accommodate new asset class capabilities and servicing models.”
Raymond, argues that “most players are really far from mastering their data architecture, meaning a total refoundation is necessary, before even visibly competing, but obviously, investing in quick efficiency wins will allow players to keep their existing business and be allowed to play in a redefined marketplace where efficiency, but also automation and the ability to operate in a normalised ecosystem will be key”.
Competitiveness now depends on how fast firms turn messy external inputs into reliable records, according to Reid.
He states: “A unified automation layer shortens onboarding, boosts Straight-Through Processing and reduces exception ageing, which improves both margins and performance against service level agreements.
“Insight-driven dashboards and AI-powered remediation sharpen operational control. The broader the process coverage on one platform, the easier it is to expand productised services.”
Future state of data architecture
Firms are redesigning their data architecture to account for changes such as: increased use of digital identity and blockchain for transaction flows, convergence of securities services, payments, and digital asset workflows, rise of AI-first data models and automated data quality management, and a move toward harmonised data models across global operations.
Burke sees the emergence of tokenisation, private markets, and new data as key factors in driving firms to redesign their architecture to manage greater complexity and volume expansion. He says: “Tokenisation frameworks enhance liquidity and enable fractional ownership of real-world assets, while interoperability across blockchain networks supports efficient settlement and data consistency.
“Firms are also investigating how AI can automate data processing and create standardised, trusted records. Together with decentralised, algorithmic fund frameworks, these innovations are shaping more agile, scalable, and resilient asset servicing models, built to manage the complexity of new asset classes and deliver the transparency, capacity , speed, and innovation across markets that the industry urgently needs.”
Raymond adds: “One might argue that tokenisation brings simplification through automation and digitisation more than it complexifies what has been a manual work for decades, by having systems that are ready to embrace the synchronicity of the digital/tokenised world will be a huge challenge, both in terms of tech and in terms of culture and operation model.”
Data architecture has become an integral part of a firm’s strategy in today’s asset servicing landscape enabling them to develop interoperability, standardisation, and real-time capabilities, in turn giving them a competitive advantage. The strength and intelligence of the data that supports these capabilities, will largely determine a firm’s ability to lead into the future.
Through re-engineering — which usually involves cloud-native platforms, metadata-driven systems, and unified data models — the sector is modernising its legacy infrastructure to meet demands for improved efficiency and transparency. Data architecture has emerged as a vital driver of digital transformation, enabling real-time reporting, secure client tooling and faster settlements.
A swift evolution: Data architecture’s role in changing asset servicing
Asset servicers are shifting from siloed, monolithic systems to modular, API-driven frameworks, to overcome limitations faced by traditional legacy systems. This comes against a backdrop of pressures from clients, regulators, and markets, as systems frequently deliver delayed data, reporting inconsistencies, and settlement bottlenecks. The redesign of data architecture, alongside digitalisation is a method to improve operational resilience while increasing levels of automation.
Tom Burke, president of global asset servicing at Broadridge, comments that the asset servicing sector has been transitioning within a short span of five years, “From siloed, batch-based systems to more integrated, event-driven frameworks”.
“Yet the idea of all processes running from a single, universal data source still remains out of reach in the complex, multi-party asset servicing ecosystem. The real opportunity lies in creating connected, trusted data networks — where interoperability, governance, and real-time exchange replace the need for a single repository.”
Dan Reid, chief technology officer and founder of Xceptor, notes that the sector’s data architecture has changed within the past half decade, from batch Extract Transform Load (ETL) feeding isolated tools to cloud-first automation spanning the value chain. He remarks: “Custodians now expect to ingest any format, normalise it, and apply rules within one governed layer.
“AI is embedded directly in these pipelines, shrinking manual processing and exception queues. Traceability, recoverability, and API connectivity have become baseline requirements, not optional enhancements.
Data architecture will remain or emerge as the leading priority for asset servicing firms in the coming years, according to Timothée Raymond, head of innovation at Linedata.
He states: “Data quality drives efficiency, the pressure on cost is still a significant driver, and the rise of AI as a way to gain efficiency requires high quality data.
“Some would say the constraint on the data being structured is less important than before because of how large language models work, but actually to get to the level of accuracy the industry requires, normalising the unstructured stash of data is still important for most Retrieval Augmented Generation based approaches today.”
Contemporary data management blueprint
Modern data architecture focuses on delivering agility, scalability, resilience, and enabling near real-time reporting, advanced analytics, artificial intelligence/machine learning (ML) adoption, lower operational risk, and greater data governance and lineage. Key components of the infrastructure include: cloud-first or hybrid models, event-driven workflows, standardised data models, API-centric communication, and scalable storage for ESG, alternatives, and digital assets.
Burke says contemporary data architecture is defined by integrated event-driven frameworks that prioritise efficiency, scalability, and client-centric services. He remarks: “Such system interoperability is crucial in today’s asset servicing landscape to enable seamless communication and data exchange across diverse platforms. Efficiency, client service, and compliance demands make interoperability a critical success factor in asset servicing.”
Interoperability is the new medium of efficiency, with smooth data exchange an essential requirement for: clients’ internal platforms, fintech partners, and data vendors, and market infrastructures and Central Securities Depositories (CSDs), and International Central Securities Depositories (ICSDs). Additionally there is an increased need for ‘plug-and-play’ integration in client onboarding which includes pre-built components (like APIs) to seamlessly add services (payments, data) into a firm’s platform.
Reid describes modern architecture as involving “cloud-first, data-agnostic, AI-enabled, and workflow-aware, with open interoperability across clients, fintech partners, and market infrastructures”.
He continues: “Platform-agnostic connections to internal and external systems must be paired with native AI services and composable components that plug into an orchestration layer without duplication. The SaaS model is popular because it provides scale, security, and predictable updates.
“Interoperability remains critical: asset servicers need to meet clients where they are, minimise unnecessary data movement, and bridge heterogeneous environments to maintain service quality and grow their offering.”
Raymond defines contemporary data architecture as: “real time, API event based), normalised, internal or external standard, open and readable, and secure.”
Tackling data fragmentation
Data fragmentation persists across enterprises frequently caused by the diverse use of platforms across different regions, acquisitions, and legacy technology; often resulting in inconsistent data, hampered reporting, and operational bottlenecks. Modern data architecture aims to overcome inconsistency through: data virtualisation and metadata-driven integration, unified data models and single sources of truth, enterprise-side data governance and lineage, and Master Data Management (MDM) approaches.
Burke reasons a single system will not solve issues of fragmentation however: “By a modern, mutualised architecture and data standards that bring coherence across the ecosystem. By using shared data models, governed data lakes, and open APIs, firms can align front, middle, and back offices on consistent, trusted data — reducing duplication, reconciliation, and delays.
“True progress comes from standardised integration: market infrastructures, custodians, and technology providers must lead on standards, while asset managers and service providers adopt open, interoperable frameworks. An industry-driven, utility-enabled model builds the trust, transparency, and scalability needed to reduce fragmentation and power sustainable growth in asset servicing.”
According to Reid, “modern architecture reduces fragmentation by?standardising diverse upstream data and?centralising control. A data-agnostic platform ingests data from internal and external systems, applies business rules, and publishes clean outputs to downstream books of record with full lineage.
“Certified rule libraries replace one-off scripts, which brings order to corporate actions, reconciliations, tax and reporting. Document intelligence closes the gap on unstructured inputs.
“The result is a persistent source of truth with full lineage for both ops and IT.”
Raymond adds that data warehousing is an important means of custodians reducing fragmentation: “Defining the objects they work with, building unified view/data ownership for each piece of data is key. The evolution of market infrastructure — both on private and public markets — will also play a role, and the rise of standards or at least shared concepts such as open banking, electronic invoicing etc. will be both an opportunity and a challenge.”
Strategic pressures pushing data architecture transformation
Regulatory drivers are leading firms to reexamine their data architecture with increased transparency requirements around liquidity, ESG, and collateral reporting.
Market reforms are demanding data standardisation and real-time visibility. Client-driven pressures — such as demand for custom reporting, self-service analytics, 24/7 access to data, desire for seamless integration with clients’ systems — and market-driven pressures, including competition from fintechs and digitally native provides, costs connected with legacy system maintenance, and emerging markets for digital assets are requiring firms to upgrade their data architecture.
Burke argues that these converging regulatory, client, and market pressures, along with an explosive growth in transaction volumes (up over 23 per cent year-on-year) are demanding firms to fundamentally reconsider their data architectures.
He says: “Legacy systems can no longer manage the scale, speed, and visibility required for transparency, accelerated settlements, and real-time reporting.
“To address this, firms are considering strategies for standardised data ontologies, enhancing automation, and embracing cloud-based platforms. Such improvements will enable real-time insights, operational agility, and scalability, and will position modern data architecture as a strategic enabler for efficiency, resilience, and sustained competitiveness.
Raymond, notes of these three core pressures forcing data infrastructure evolution: “Regulation such as that of private markets in Europe, for example, is a mandatory driver, though the most important one is probably client pressure and the ability for asset servicers to differentiate though efficiency, reliability and cost.”
Reid explains core influences of the architecture transformation include: “New tax regimes such as FASTER and MiKaDiv demand clean, traceable data from capture to filing. Control frameworks now expect explainability and governance embedded in production workflows.
“Clients want faster onboarding, transparency on breaks, and seamless integration into their own platforms. Meanwhile, rising data volumes, private-asset diversity, and resilience expectations are pushing firms toward cloud scale and AI-enabled ingestion.”
A strategic lever in a competitive landscape
Custodians are pivoting from basic service providers to data-rich solutions partners.
They are engaging data as a competitive differentiator to deliver efficient onboarding and integration, enhanced client experience, improved insights and analytics and operational alpha, reduced errors and faster exception resolution.
Burke, labels it as a true differentiator: “According to a recent survey, more than two-thirds of market participants see data integration and interoperability as critical enablers for future competitiveness. The ability to integrate and mobilise data seamlessly is increasingly tied to efficiency, accuracy, and innovation capacity, and directly translates into stronger relationships and the agility to accommodate new asset class capabilities and servicing models.”
Raymond, argues that “most players are really far from mastering their data architecture, meaning a total refoundation is necessary, before even visibly competing, but obviously, investing in quick efficiency wins will allow players to keep their existing business and be allowed to play in a redefined marketplace where efficiency, but also automation and the ability to operate in a normalised ecosystem will be key”.
Competitiveness now depends on how fast firms turn messy external inputs into reliable records, according to Reid.
He states: “A unified automation layer shortens onboarding, boosts Straight-Through Processing and reduces exception ageing, which improves both margins and performance against service level agreements.
“Insight-driven dashboards and AI-powered remediation sharpen operational control. The broader the process coverage on one platform, the easier it is to expand productised services.”
Future state of data architecture
Firms are redesigning their data architecture to account for changes such as: increased use of digital identity and blockchain for transaction flows, convergence of securities services, payments, and digital asset workflows, rise of AI-first data models and automated data quality management, and a move toward harmonised data models across global operations.
Burke sees the emergence of tokenisation, private markets, and new data as key factors in driving firms to redesign their architecture to manage greater complexity and volume expansion. He says: “Tokenisation frameworks enhance liquidity and enable fractional ownership of real-world assets, while interoperability across blockchain networks supports efficient settlement and data consistency.
“Firms are also investigating how AI can automate data processing and create standardised, trusted records. Together with decentralised, algorithmic fund frameworks, these innovations are shaping more agile, scalable, and resilient asset servicing models, built to manage the complexity of new asset classes and deliver the transparency, capacity , speed, and innovation across markets that the industry urgently needs.”
Raymond adds: “One might argue that tokenisation brings simplification through automation and digitisation more than it complexifies what has been a manual work for decades, by having systems that are ready to embrace the synchronicity of the digital/tokenised world will be a huge challenge, both in terms of tech and in terms of culture and operation model.”
Data architecture has become an integral part of a firm’s strategy in today’s asset servicing landscape enabling them to develop interoperability, standardisation, and real-time capabilities, in turn giving them a competitive advantage. The strength and intelligence of the data that supports these capabilities, will largely determine a firm’s ability to lead into the future.
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