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Disruption at the Door: AI and the Re-Architecture of Commercial Banking

The Trust Layer for AI-Native Banking and Payments
Zobaria Asma
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19 January, 2026

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5 minutes

Key Takeaways

  • Automation in financial services is critical: AI-native fintech are capturing trust by validating transactions in real time, reshaping how customers choose where to move money. 
  • AI in banking and payments is moving from back-office support. Agentic systems now initiate, validate, and route transactions autonomously across authentication, AML, and fraud controls within milliseconds. 
  • Legacy, rule-based stacks break at machine speed. Fraud, AML, and authentication tools still operate sequentially, while agentic workflows evaluate identity signals, device behavior, and transaction intent as a single decision. 
  • Agentic AI enables fraud prevention inside the transaction flow. For example, a payment initiated by an AI agent can be authenticated through behavioral biometrics, device intelligence, and policy constraints before execution 
  • The execution layer is where trust is enforced. Orchestrated AI workflows make decisions explainable and auditable. Turning compliance from friction into a real-time control advantage. 
  • Pilot-first adoption reduces risk while proving value fast. Banks can test agentic fraud prevention in weeks, integrating intelligence across existing systems without replacing the core.

Over the last five years, U.S. commercial banking has undergone its most consequential transformation since the introduction of the ATM. Real-time payments, mobile-first experiences, and API-driven ecosystems have rewired how money moves at scale, at speed, and with minimal human intervention. 

Digital wallets now dominate e-commerce payments and are gaining ground at physical points of sale. Account-to-account (A2A) payments are projected to reach $5.7 trillion by 2029, as frictionless transfers become the norm (Juniper Research, 2024). The global rollout of instant-payment rails, including the U.S. Federal Reserve’s FedNow, is accelerating adoption by reducing costs and settlement times compared with traditional card networks (Federal Reserve, 2026). 

With money movement becoming instantaneous, traditional banks are facing the prospect of being reduced to the substrate for a new, tech-driven financial ecosystem. Neobanks, embedded finance platforms, fintech lenders, and digital wallets are unbundling core banking services, capturing millions of U.S. consumers with streamlined accounts, early access to wages, and AI-driven financial insights (CB Insights, 2025).  

Banking-as-a-Service (BaaS) platforms are further accelerating this shift, enabling non-bank companies, such as retailers, fintech companies, and SaaS providers, to embed financial services directly into their products. Customer relationships and decision-making are increasingly moving outside traditional banks, leaving incumbents primarily responsible for regulatory compliance and balance sheet support. 

Artificial intelligence is amplifying the disruption initiated by alternative banking solutions. AI does more than automate processes; it redefines decision-making, credit assessment, and predictive financial management. In commercial banking, AI platforms can continuously analyze cash flows, dynamically price loans, and provide predictive treasury insights. SMBs no longer wait for manual approvals or static covenants; AI models deliver instant credit decisions, early-warning risk indicators, and liquidity recommendations (McKinsey, 2024). 

This shift moves the intelligence layer away from banks, eroding traditional revenue streams. AI enables faster decisions, automated controls, and frictionless verification at scale, while real-time payment rails and API-driven settlements remove latency from money movement. Fraud windows that once lasted hours have collapsed to milliseconds, and adversaries now have access to the same generative and automation tools. Real-time fraud detection has therefore become a critical survival threshold. 

AI-enabled fraud is projected to reach $40 billion by 2027 (Deloitte, 2024). The future of banking depends on whether intelligent automation can be operationalized safely and whether systems are resilient enough to withstand machine-speed attacks. In this rapidly evolving landscape, banks that fail to embed AI and secure real-time operations risk being relegated to the role of invisible infrastructure, while tech-native platforms control the customer relationship and strategic insights. 

" Money will migrate toward whoever protects it best while keeping it effortless. And that doesn't have to be banks. "

Why Did Speed Outpace Resilience?

Banks optimized for digital convenience, not for AI-enabled adversaries operating at machine speed. The first response to AI-driven disruption was velocity. Real-time payment rails like FedNow and RTP now settle transactions in milliseconds, yet fraud detection systems were never architected to operate at this pace. As money movement accelerated, control systems remained sequential. 

Most banks still operate fragmented technology stacks: onboarding, AML, fraud detection, login protection, and machine-learning transaction monitoring. Each system is optimized for a single function, with automation applied in isolation. Alerts are generated, but contextual insight is often lost. AI-driven fraud detection may flag individual anomalies, but it cannot trace multi-step attack chains across accounts, channels, and payment rails. These tools were never designed to interoperate at machine speed, a gap that becomes more urgent as agentic AI adoption in commerce expands (Visa, 2025).  

Legacy banks face a paradox: their controls are built to authenticate humans, not AI agents executing payments, booking transactions, or routing funds autonomously. Real-time fraud detection now requires simultaneous evaluation of identity, intent, device behavior, and transaction context, a level of interoperability that rule-based systems cannot deliver at machine speed. 

As of late 2024, only 8% of banks were investing in end-to-end intelligent automation (IBM, 2024).

8% of banks were investing in end-to-end intelligent automation (IBM, 2024)

The limitation is not lack of interest but structural drag: over 60% of IT spending remains locked in “run-the-bank” operations, with legacy maintenance consuming more than 10% of revenue (BCG, 2025). 

At the same time, pressure to act is mounting. While 51% of banks recognize that AI is fundamentally reshaping their business, only 26% report meaningful revenue impact today (KPMG, 2025). Governance gaps, fragmented data, and unclear ROI have kept AI adoption tactical rather than operational. 

This is the gap banks now confront: AI is no longer optional, yet without an execution layer that governs intelligence end-to-end, scaling it safely and effectively remains elusive. 

How Did Fintech Get So Far Ahead?

While banks debated guardrails and governance frameworks, fintech rebuilt the transaction stack for real-time fraud detection and adversarial environments. 

Fraud Prevention Became Pre-Execution 

The divergence was structural. Fintech platforms were designed for instant settlement, automated threat management, and limited recourse once funds moved. Fraud prevention became a pre-execution responsibility, embedded directly into transaction flows rather than applied through post-processing or manual review. 

Consolidating Capabilities for Speed 

To operate at this velocity, fintech consolidated capabilities that banks historically deployed in isolation. Behavioral biometrics, device intelligence, and machine learning were integrated into unified, real-time decision engines. Risk rules could be updated dynamically, often via natural language interfaces, allowing platforms to respond to emerging threats without lengthy approval cycles or system downtime. 

Legacy Banking Assumptions Exposed 

Traditional banking infrastructure evolved under different assumptions. Core systems and networks, such as SWIFT, were designed for inter-institutional trust, batch processing, and delayed settlement. Time itself functioned as a control mechanism. Real-time payment rails remove that buffer entirely, exposing the limitations of architectures built for sequential review and post-transaction intervention. 

Crypto and Fintech as Early Innovators 

Crypto-native and early fintech platforms encountered this constraint first. Operating in environments defined by irreversible transactions and persistent attack pressure, they developed techniques to assess behavioral intent rather than static anomalies. More than digitizing banking, fintech architected systems for high-risk environments from day one. What crypto exchanges built to survive has become the standard that all real-time transaction platforms now need. 

Modern Fraud Capabilities Emerged 

Capabilities now considered essential in AI-driven fraud detection, including device fingerprinting, interaction pattern analysis, and continuous risk scoring, emerged from this necessity. Banks, by contrast, remain constrained by vendor fragmentation, manual exception handling, and slow rule-change cycles. As payments accelerate and attack surfaces expand, these limitations increasingly shape competitive outcomes. 

Market Signals and Competitive Urgency 

These architectural differences are no longer abstract. Apple Pay alone processes $8.7 trillion globally and controls over half of U.S. mobile wallet transactions (Research Nester, 2025).

$8.7 trillion globally and controls over half of U.S. mobile wallet transactions (Research Nester, 2025)

Digital wallets more broadly are expected to account for 52% of North American online payments by 2030 as account-to-account rails mature (Financial Brand, 2025). 

52% of North American online payments by 2030 as account-to-account rails mature (Financial Brand, 2025).

As transaction velocity and irreversibility increase, banks are being forced to confront a structural mismatch: legacy fraud controls, optimized for review and remediation, are operating within an ecosystem that now demands real-time intent assessment. 

" The competitive threat is existential. Money flows toward the institutions or platforms that protect it most effectively while keeping it effortless, and that no longer defaults to traditional banks. "

Learn More: How Web 3.0 and Blockchain are Transforming FinTech?

What Does the Guardrails Era Demand?

The industry is crossing a structural threshold. AI is no longer confined to recommendations or analysis. It is beginning to initiate, authorize, and route transactions. 

Visa declared that 2025 will be the end of solo checkout, with hundreds of agent-initiated transactions executed through its Intelligent Commerce framework (Visa, 2025). Mastercard’s Agent Pay and PayPal’s Agent Ready similarly enable AI copilots to pay, book, and transact within predefined constraints (Mastercard, 2025)  

Across these platforms, judgment that once unfolded across minutes or hours is now compressed into milliseconds. That compression is both an opportunity and a risk. 

Agentic systems increasingly span authentication, fraud detection, AML screening, and payment routing as a single, continuous decision. When validation becomes autonomous, compliance can no longer live in backlogs or post-event reviews. It must operate in-flow, during execution, with outcomes that are explainable and auditable by design. 

Why Orchestration Becomes the Strategic Wedge 

This shift exposes the limits of traditional control models. Fragmented tools, sequential checks, and human-in-the-loop reviews cannot govern agentic transactions moving at machine speed. Enterprises require a different approach, one that assembles intelligence across existing systems without dismantling core infrastructure. 

The guardrails era therefore demands orchestration. Discrete, specialist agentic processes must govern individual risk domains, coordinated through a shared decision fabric that enforces policy, preserves auditability, and allows intervention before value moves. 

This is where competitive pressure sharpens. Platforms architected for real-time, policy-constrained automation are setting new expectations for speed and trust.  

Banks that adopt agentic workflows with robust guardrails do more than modernize internal operations. They create trust infrastructure, enabling safer innovation across fintech partnerships, embedded finance, and digital commerce. While retaining regulatory integrity and institutional control. 

" The next advantage in banking stems from trust when agents are the ones moving the money. "  

Learn More: Rethinking Risk: Why Forecasting Alone Won’t Protect the Modern Enterprise 

Can Banks Architect for AI Speed Without Losing Control?

U.S. banks face growing pressure to keep pace with fintech companies and digital-native platforms. 

Banks cannot outpace fintech on deployment velocity. Digital-native platforms update fraud rules in seconds, whereas banks wait weeks for vendor approvals.  

Fintech operates with modular, composable systems: swap a fraud vendor, integrate a new authentication method, and test an AML model, all without rewriting core infrastructure. Banks, bound to monolithic cores and vendor dependencies, can't pivot at that pace. 

However, banks hold a unique advantage: institutional trust, regulatory relationships, and access to the full financial graph. The challenge lies in translating these strengths into agile, policy-compliant decision-making at machine pace. 

The path forward lies in architectural modernization. By introducing lean, modular workflows that orchestrate intelligence across existing systems, banks can retain control while responding to AI-native threats as fast as fintech. 

" The innovation gap is about redesigning the interface between risk, compliance, and operations; creating a layer where AI can act decisively within defined guardrails. " 

Learn More: Redefining Industrial Resilience: How AI Infrastructure Delivers Adaptive Advantage

The Application Assembly Layer: A New Interface for Banking 

What banks need is a layer that governs, orchestrates, and unifies intelligence across existing infrastructure. The Application Assembly Layer (AAL) acts as that interface, connecting legacy cores to AI workflows while enforcing policy, auditability, and regulatory compliance. 

With AAL, agentic AI can make sub-millisecond decisions across fraud detection, authentication, AML, and payment routing, all within the transaction flow. Behavioral signals, transaction context, and risk policies are evaluated simultaneously, allowing banks to respond to zero-day threats and operational anomalies without slowing down customers.

The strategic advantage is immediate and measurable. Banks retain control over every AI-driven decision while accelerating response times, reducing false positives, and strengthening compliance.

By orchestrating intelligence rather than replacing infrastructure, AAL turns existing systems into a competitive asset rather than a limitation. This is how banks compete, by owning the orchestration layer that makes AI both fast and governable.

What Does Winning Look Like in an AI-Native World?

Commercial banking is competing against the speed at which trust can be automated. 

The institutions that operationalize AI through governed assembly layers control the interface where money moves. In a world where 81% of US consumers expect to transact through AI agents (BCG, 2025), controlling that interface means owning the future. 

Money migrates toward whoever protects it best while keeping it effortless. Banks built the rails. Fintech built the trust layer. The question now is whether banks can build the orchestration architecture that makes AI both fast and safe. 

The answer determines who owns trust in the next era of banking. And trust, ultimately, is what money follows. 

How Can Banks Start Without Replacing Everything?

The banking industry’s architecture has evolved over decades in response to expanding regulatory oversight and consumer protection requirements. A similar maturation cycle is likely to occur for digital and challenger banks in the coming years. However, waiting for that convergence carries risk. During this period, digital banks continue to accumulate consumer data and refine products and experiences at a pace traditional institutions struggle to match. 

Banks do, however, have an alternative. Rather than awaiting market maturation, they can orchestrate AI across existing infrastructure with discipline. A pilot-first, agentic framework allows banks to incrementally introduce intelligence without destabilizing core systems. CodeNinja’s Application Assembly Layer (AAL) provides a unified orchestration fabric that coordinates fraud detection, AML monitoring, authentication, and payment routing as a single decision layer, enabling sub-millisecond risk decisions in real time.  

It transforms the legacy systems into a governed, auditable, AI-native backbone, where agentic processes handle transaction validation and compliance automatically. In six to eight weeks, pilots prove measurable outcomes: reduced fraud, automated sanctions resolution, and accelerated onboarding. 

The strategic difference: banks assemble, govern, and scale AI workflows modularly, turning existing infrastructure into a competitive advantage at AI speed, without replacing core systems. 

Launch a 60–90 Day AI Pilot to secure transactions, automate compliance, and scale trust at machine speed. Contact Us

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