Why the Current Architecture Isn’t Fit for the Future

Today’s capital-markets systems were built in another era. They are heavy, rigid, and highly custom-coded. The result: low agility, silos, high operational risk.

In its recent article on financial-data and markets infrastructure (FDMI), McKinsey & Company reports that from 2019-2023 the industry posted a 17% compound annual growth rate in total shareholder return, outpacing financial-services more broadly at 10%. McKinsey & Company. That success comes despite legacy friction: ecosystems still rely on old code, manual hand-offs and high-cost reconciliation.

That friction is a bottleneck at scale: new data types, investor behaviours and regulatory curves are outpacing the speed of traditional platforms.

The Modular AI Stack Defined

A modular AI stack breaks into four interacting layers:

1. Data & ingestion layer – flexible pipelines handling structured and unstructured data (investor signals, disclosures, market feeds).
2. Processing & orchestration layer – intelligent workflows that route, transform and validate data in real time.
3. Compliance & trust layer – enforceable rules, audit trails, governance baked into every transaction.
4. Insight & delivery layer – analytics, investor UI, syndicate dashboards and automated reporting.

Each layer is designed to be upgraded, scaled or replaced independently. That’s the key: composability rather than replacement. According to a report by Bank for International Settlements (BIS), regulators and policy-makers expect AI systems built with governance and modular design to become standard in financial markets infrastructure. bis.org

How Institutions Are Preparing

Leading banks and advisors are piloting modular deployments:

• Re-architecting KYC and AML checks into micro-services.
• Deploying “plug-and-play” investor-intelligence modules atop existing data warehouses.
• Using independent compliance engines that sit between deal desks and book-building systems.

Such pilots reflect a new dynamic: rather than trying to rip and replace legacy systems, institutions layer modular blocks onto them. Over time, the legacy core becomes the “least mission-critical” component.

McKinsey projects that global investment in AI infrastructure — including data centres and compute—will hit up to US $6.7 trillion by 2030 in order to support emerging workloads and composable architectures. datacentremagazine.com

The Advantage of Composability

Why does modular matter? Because:

• Speed: Upgrades can be isolated, reducing rollout risks and time-to-value.
• Flexibility: New features (e.g., generative-AI investor insights) can be plugged in without full platform rebuilds.
• Governance: Compliance rules can be managed at the layer level rather than being embedded in legacy monoliths.
• Cost-efficiency: Leverage cloud fabrics, third-party services and micro-services without large upfront outlays.

For funds, issuers and advisors competing in a faster, more fragmented investor universe, these are not incremental benefits—they are existential.

What This Means for Mageia

Mageia is purpose-built for this shift. Its architecture assumes modularity from day one. Rather than treating AI as an add-on, Mageia treats intelligence, compliance, orchestration and delivery as plug-and-play modules. This means clients can deploy step-by-step—starting with one module (e.g., investor intelligence) and layering further modules as they scale.

For financial institutions seeking to balance speed, compliance and sovereignty, Mageia’s modular approach offers a next-generation path: not a replacement platform, but a composable future fabric.

Key Takeaways

• The future of financial-markets infrastructure is modular and composable—not monolithic and legacy-bound.
• A four-layer modular AI stack empowers institutions to scale, upgrade and govern their infrastructure with agility.
• Deployments predicated on composability will capture cost savings, speed advantages and governance resilience.
• Mageia’s architecture is aligned with this paradigm—enabling enterprise clients to adopt intelligence rather than retrofit it.

Sources

• McKinsey & Company – “Financial data and markets infrastructure: Positioning for the future” (Jan 28 2025) McKinsey & Company
• Bank for International Settlements – “The use of artificial intelligence for policy purposes” (Oct 10 2025) bis.org
• McKinsey / DataCentreMagazine – “AI Infrastructure to Require $6.7 Trillion by 2030” (May 14 2025) datacentremagazine.com