Why doesn’t Asset Management have an Industry Reference Architecture?

In my recent note, I explored how Asset Management firms can lay the right foundations for Enterprise AI—with Robust Data Strategy and Seamless Integration as the twin pillars.

But here’s the real question: Why, unlike Banking or Insurance, Asset Management never embraced a standard industry architecture (which would have made it easier)?

The answer is complex—and revealing:

1) Diverse Business Models

Asset Managers differ across:

• Asset classes (equities, fixed income, alternatives, private markets, (more recently) crypto)
• Client types (retail, institutional, sovereign, pension funds)
• Investment styles (active, passive, quant, ESQG, factor based)

A one-size architecture struggled to emerge because the operational value chain isn't uniform

2) Alpha Over Architecture

For years, technology was seen as a support function—not a differentiator. Investment IP, not platforms, was the moat. This mindset discouraged standardization, especially as the technology structure was perceived as a threat to alpha generation.

3) Fragmented Vendor Ecosystem

From OMS/EMS to risk engines and analytics, every vendor brings unique data schemas and integration patterns—making a neutral, vendor-agnostic architecture elusive.

4) Regulatory Lag

Unlike banks, asset managers faced late digitalization and real-time reporting pressures, delaying the push for standardization.

5) Function Over Form

Industry efforts defined what functions should exist, but not how systems should integrate or share data.

6) Legacy Complexity

Mergers, bespoke systems, and proprietary platforms built behind closed doors have left a patchwork of architectures.

So, is it time for change? Absolutely.

Today, the industry faces fee compression, regulatory complexity, data explosion, rapid digital expectations from clients, and—most importantly—AI-driven disruption. The need for a capability-driven, standardized architecture has never been greater.

A modern, loosely coupled Industry Architecture can unlock:

• Cost and margin optimization
• Seamless interoperability to share data, context & intelligence
• Standardized “plumbing” setup suited for innovation
• A solid foundation for Enterprise AI

Watch this space for how a capability-based reference architecture could help drive innovation, efficiency, and readiness for the AI era.

Original post on LinkedIn - here.

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