The global banking industry is approaching a strategic crossroads – one that will prove expensive for those who choose the wrong direction. Financial institutions stand to lose USD 170 billion in profits over the next decade if they do not adapt rapidly to the evolution of artificial intelligence, according to the McKinsey Global Banking Annual Review 2025. Yet the report’s most provocative insight isn’t about AI itself, but the infrastructure required to leverage it effectively.

Agentic AI has the potential to reshape banking at its foundations. Early adopters will strengthen long-term advantages, potentially boosting returns on tangible equity by up to four percentage points. On the other hand, laggards face structural declines in profitability. The difference between these outcomes won’t be determined by who adopts AI first, but who has the architectural foundations to implement it effectively. Increasingly, those foundations are modular.

From Generative to Agentic AI: Revolution not Evolution

To understand why architecture matters so deeply, we must distinguish between the two paradigms reshaping financial services.

Generative AI, the star of 2023-24, excels at creating content: automated reports, document summaries, customer-service response, and so on. It is powerful, but fundamentally reactive. GenAI requires human prompts and produces outputs that must still be reviewed and acted upon by humans.

Agentic AI represents a step-change. These systems combine autonomous reasoning, planning, and execution. They don’t only generate recommendations, they act on them. An Agentic AI system can autonomously manage an entire loan-approval workflow: collecting documents, verifying information, assessing creditworthiness, checking regulatory compliance, and making approval decisions, all without human involvement at each step.

The impact is already measurable. MIT Technology Review Insights found that 70% of banking leaders are implementing agentic AI through production deployments (16%) or pilot projects (52%). Deloitte reports early adopters achieving 30–50% cost reductions in specific workflows. McKinsey anticipates the emergence of a “disruptive agentic business model” within three to five years, with potential cost reductions of up to 70% in some categories. But the benefits are far from evenly accessible.

Why Monolithic Architecture are Incompatible with AI

The uncomfortable truth is that most banks are attempting to deploy twenty-first-century AI on twentieth-century infrastructure. And it doesn’t work.

Legacy systems still absorb around 60% of banks’ technology budgets, according to a 2024 Bloomberg Intelligence survey. These monolithic architectures were never designed for the rapid iteration, continuous integration, and granular governance demanded by AI deployment.

Monolithic systems require release cycles lasting months; AI models require continuous retraining and fine-tuning based on real-world performance. The mismatch is structural. Modern Agentic AI relies on orchestrating multiple specialised agents… One for data collection, another for risk evaluation, a third for decision execution. Monolithic architectures struggle to support this level of inter-system communication.

Governance is another barrier. AI systems require differentiated risk controls depending on the level of autonomy. A fully autonomous fraud-detection agent needs different guardrails than a customer-service chatbot. Monolithic systems offer all-or-nothing governance, not graduated controls.

Financial institutions cannot transform everything at once; they need incremental adoption. Starting with high-impact use cases, learning, then expanding. Monolithic architectures force “big-bang” transformations that almost never succeed.

This architectural misalignment explains why so many AI initiatives stall in pilot purgatory, never reaching production scale.

Modular Architecture as an Enabler of AI

Modular, service-based FinTech architecture solves these problems by design. Instead of monolithic platforms, modular systems are composed of independent, interoperable functional blocks connected via APIs. Each module can be developed, updated, or replaced without affecting the whole.

The key is the concept of the service: a module that does not expose standardised technical interfaces simply does not function. Services are the technical objects enabling interoperability:

• A compliance module exposes services for regulatory checks,
• A data-ingestion module exposes services for data collection and structuring,
• An Agentic AI module exposes services for executing autonomous workflows.

This architecture creates an ecosystem where each component has clear responsibilities and well-defined interfaces.

For AI deployment, this translates into concrete advantages. Banks are implementing Agentic AI systems into specific processes – KYC/AML screening, credit-memo generation, collections monitoring, intelligent communication routing – without rebuilding their entire stack. Service-based modularity allows AI agents to be activated on circumscribed workflows, with impact measured before expansion.

Because agents operate within discrete modules, failures remain contained. A malfunctioning fraud-detection agent does not propagate into customer-facing systems. This isolation allows institutions to experiment more boldly.

Service-based architectures also enable integration of best-of-breed AI solutions. One module may use Anthropic’s Claude for document analysis, another Google’s Gemini for customer interaction, a third proprietary models for highly specialised credit scoring. Monolithic systems lock institutions into single-vendor dependencies.

Different modules can carry different levels of AI autonomy, aligned with risk profiles and regulatory requirements: high autonomy for customer-service bots, human-in-the-loop supervision for lending decisions.

As McKinsey notes, the winners of this transformation will practise “precision over heft”- implementing AI surgically where it generates measurable bottom-line impact. Service-based modular architecture is the technical manifestation of such precision.

Techfin vs FinTech: When Architecture Comes First

There is a fundamental difference between starting from finance and adding technology, and starting from technology and specialising in finance.

In the first case, solutions are built top-down – gather functional requirements, then find the technology to satisfy them.

In the second, solutions are built bottom-up – design the architecture before the functional requirements, optimising for flexibility rather than feature completeness.

When designing wealth- and asset-management platforms – such as FundWatch or 360 FUNDS – this distinction becomes tangible. Being AI-ready does not mean adding an ‘AI layer’ on top of an existing platform. It means the modular architecture allows AI capabilities to be integrated precisely where needed.

Modularity operates along two dimensions:

• Process modules (compliance, analytics, reporting, client engagement) that can be activated independently;
• Target modules tailored for different market participants: custodians, asset servicers, alternative-fund managers, wealth advisers—each activating different module combinations.

AI governance is embedded in the architecture, not layered on top. A fully autonomous reconciliation agent operates under different guardrails than a semi-autonomous investment-recommendation agent—different approval workflows, audit trails, and supervision requirements.

This approach does not remove the need for transformation, but it changes its rhythm. Instead of three-year platform-replacement projects, institutions can transform progressively: start with a high-impact module, prove value, learn from deployment, scale outward.

The key managerial shift is conceptual: the question is no longer “When will our digital transformation be finished?” but “Which module do we activate this quarter, and what do we learn?”

The $170bn Question

McKinsey’s warning – USD 170 billion of potential profit erosion – is not inevitable. Avoiding it requires strategic decisions today about the technology architecture of tomorrow.

The institutions that will thrive are not necessarily the largest or the earliest adopters of AI. They will be those building modular infrastructures engineered for precision, capable of integrating AI surgically, experimenting rapidly, scaling intelligently, and governing rigorously.

They will recognise that AI is not merely a technological deployment, it is an architectural imperative. And they will understand the deeper truth: in the Agentic AI era, precision beats scale.

The question faced by every financial institution is not whether to adopt AI, but whether its architecture can support it. For most legacy systems built on monolithic foundations, the honest answer is no.

The modular imperative is clear. The question remains: are you building for yesterday’s challenges or tomorrow’s opportunities?

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