Why Interoperable Reality Will Define the Next Phase of AI in Financial Services

Note :

This article was originally published on my website https://www.raktimsingh.com/ and adapted here for a financial services audience.

You can read the full article at https://www.raktimsingh.com/representation-utility-stack-interoperable-reality/

Introduction: AI in BFSI Has Reached an Inflection Point

Financial institutions have invested heavily in artificial intelligence over the past decade.

From fraud detection and credit scoring to customer service and compliance automation, AI has moved from experimentation to deployment.

Yet, a pattern is emerging.

Despite increasingly sophisticated models, many AI initiatives in banking and financial services struggle to scale reliably in production.

The issue is not always model performance.

More often, the problem lies deeper:

AI systems are operating on fragmented, inconsistent, and non-portable representations of reality.

The Hidden Constraint: AI Acts on Representations, Not Reality

AI does not interact with the real world directly.

It interacts with representations of customers, transactions, assets, and events.

If those representations are:

• inconsistent across systems
• outdated or incomplete
• differently defined across functions
• lacking provenance or confidence

then even the most advanced AI systems produce unreliable outcomes.

This explains why:

• A customer may appear differently across onboarding, lending, and compliance systems

• A transaction flagged as suspicious in one system appears normal in another

• A “verified identity” means different things across institutions

In financial services, these are not just technical issues.

They are risk, compliance, and trust issues.

Why the Current AI Conversation Is Incomplete

Much of the current enterprise AI conversation remains model-centric:

• Which LLM should we use?
• How do we improve accuracy?
• How do we reduce inference cost?

These are important—but insufficient.

Before any model generates an output, three foundational questions must already be resolved:

2. Was the correct signal captured from the real world?

3. Was that signal attached to the correct entity (customer, account, counterparty)?

4. Can that state move across systems without losing meaning?

If the answer to any of these is weak, AI becomes fragile in production.

From Data Interoperability to Reality Interoperability

The financial industry has already gone through multiple infrastructure waves:

• Core banking modernization
• API banking and open banking
• Data lakes and analytics platforms

But AI introduces a more demanding requirement.

Systems must not only exchange data.

They must exchange meaningful, consistent, and governed representations of reality.

For example:

Two systems may both label a customer as “high risk”
—but based on different definitions, data sources, and update frequencies.

Without shared representation, coordination fails.

Introducing the Representation Utility Stack

To address this, financial institutions need to think beyond models and data platforms.

They need a new infrastructure layer:

The Representation Utility Stack

A three-layer model that enables:

• Machine-legible reality
• Interoperable state across systems
• Governed, auditable action

Layer 1: Representation Utilities (SENSE Layer)

These systems maintain trusted representations of key entities:

• Customer identity
• Account and transaction state
• Counterparty relationships
• Asset ownership and movement

They answer:

• Who is this entity?
• What is its current state?
• What has changed?
• What is the confidence level?
• What evidence supports this state?

In BFSI, this is critical for:

• KYC and identity resolution
• Transaction monitoring
• Risk profiling
• Customer lifecycle management

Layer 2: Representation APIs (Interoperability Layer)

Once reality is represented, it must move across systems.

Representation APIs ensure that what moves is not just data—but meaningful state.

They carry:

• Identity
• State
• Provenance
• Confidence
• Context

This enables:

• Consistent decision-making across departments
• Coordination between banks, fintechs, and regulators
• Reduction in reconciliation overhead

Layer 3: Governed Execution (DRIVER Layer)

This is where AI-driven decisions translate into action:

• Loan approvals
• Fraud blocking
• Transaction authorization
• Claims processing

But in financial services, action must be:

• explainable
• auditable
• reversible

Governed execution ensures:

• Clear delegation of authority
• Verified representation before action
• Traceability of decisions
• Defined recourse mechanisms

Without this layer, AI introduces systemic risk.

Why This Matters Now for Financial Institutions

AI in BFSI is moving from:

Insights → Decisions → Actions

As systems begin to act autonomously or semi-autonomously, the cost of incorrect representation increases significantly.

Failures no longer remain analytical.

They become:

• financial losses
• compliance breaches
• customer trust erosion
• regulatory penalties

The Emerging Opportunity: Representation Infrastructure

This shift is likely to create a new category of players:

Representation Utility Providers

These could include:

• Identity and KYC infrastructure providers
• Cross-institution data and state synchronization platforms
• Provenance and audit infrastructure providers
• Regulatory reporting and recourse systems

These players will not compete on model performance.

They will compete on:

👉 making reality consistent, portable, and trustworthy

What Financial Leaders Should Do Now

Boards, CIOs, CTOs, and Chief Risk Officers should begin asking:

• Where is customer identity fragmented across systems?
• How consistent is transaction state across functions?
• What is our source of truth for key entities?
• Can state move across systems without reinterpretation?
• How do we validate representation before action?
• Where does recourse begin when AI is wrong?

These are not technology questions alone.

They are strategic infrastructure questions.

Conclusion: The Next Advantage Is Not Just Intelligence

The financial services industry has always been built on trust.

In the AI era, trust will depend on something deeper:

the ability to represent reality accurately, share it consistently, and act on it responsibly.

The next phase of AI in BFSI will not be won by:

• better models alone
• faster inference
• more automation

It will be won by institutions that invest in:

👉 interoperable, governed, machine-readable reality

That is the role of the Representation Utility Stack.

And it may become one of the most critical infrastructure layers for financial services in the coming decade.