Banks are adding AI to a model that AI makes obsolete

Date:
Thu, 24 Sep 2026 11:00:38 +0000

Description:
Banks are adding AI to old systems and limiting what it can actually do.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter For decades, banking technology has been built around a simple sequence: a person makes a financial decision and the banks technology processes it. A customer decides to open an account, move money into savings or apply for a loan, and the banks systems execute that instruction.

Artificial intelligence can potentially reverse that sequence. Instead of waiting for a human to specify an action, AI can interpret the persons goals, understand the financial context around that goal, and determine what happens next. It could recognize that a customer is likely to face a cash shortfall, identify the available ways to address it, and potentially execute the appropriate action. Dmitry Volkov Social Links Navigation

Founder of Molit.ai and Social Discovery Ventures. McKinsey estimates that generative AI could create $200 billion to $340 billion in annual value for the banking industry. Yet much of the industry is adding AI to systems designed for the old model, not rebuilding the model around AI. Latest Videos From TechRadar Watch full video here:

The problem is that AI inherits the same product silos and process
boundaries. Banks gain another layer of technology, but not the cross-system decision-making needed to realize AIs full potential. The first wave is still about better processes The most visible applications of AI in banking are often the easiest ones to deploy. Banks are using AI tools to improve
customer service, automate fraud detection, personalize recommendations, summarize documents and accelerate credit decisions. Lloyds Banking Group,
for example, says more than 50 AI use cases were rolled out across the group in 2025, generating around 50 million in value, with more than 100 million in additional value expected in 2026. You may like Why financial institutions need a clearer approach to AI governance Is the FCA underestimating the AI fraud threat? When AI inherits your technical debt

McKinsey has made a similar observation based on the industry's experience with generative AI. Simply adding AI on top of existing processes will not produce transformational change and can instead create another layer of technical debt.

The difference between an AI-native architecture and a chatbot attached to an existing system can be tested with three questions. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.
Is the AI following a fixed sequence of instructions, or can it choose and coordinate actions within a defined system of guardrails, trusted data
sources and approved tools? Is the process designed for the agent to act,
with human review and every decision recorded for analysis? And are permissions, monitoring and regulatory controls built into the workflow, particularly for critical functions such as compliance and fraud prevention? If the answer is no, the company has added an AI interface without
redesigning the underlying process. From products to outcomes The more significant transformation begins when the bank starts with the customers objective, not with the banking product.

Consider a customer who wants to maintain a certain level of liquidity while earning as much as possible on excess cash. An intelligent banking system could continuously monitor their balance, upcoming payments, income,
available credit and other relevant information, then determine whether money should remain liquid, be invested elsewhere or be used to reduce borrowing. What to read next 'Weve removed the biggest barrier to customer AI adoption: writers block' Starling Bank AI boss on why agentic AI could be the key to help customers manage their money better 'The test isnt whether AI can do something. Its whether it can make the process measurably better': We hear
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While the system can continue making decisions as the customers circumstances change, that does not require customers to surrender control immediately. Adoption can begin with low-risk actions, such as moving excess cash into savings or setting aside VAT for future tax payments, before expanding into more consequential decisions.

This is already beginning to appear in financial institutions, although
mostly in bounded applications. Deutsche Bank, for example, has deployed an agentic AI system for third-party risk management in which several AI agents retrieve relevant controls, analyze supporting documentation, and propose assessment outcomes. Human assessors remain responsible for reviewing or overriding those recommendations.

Like a new employee , an AI agent should receive defined permissions. Transparent activity logs, alerts, approval thresholds and the ability to override decisions would make that principle visible in the product and enforceable by regulators. The bank becomes a continuous decision system
Under this premise, the bank becomes an intelligent execution layer that continuously manages financial activity to accomplish a defined objective.

This shifting structure is also visible outside traditional banking. Visa and Mastercard are both building infrastructure for AI-initiated payments, allowing agents to act on behalf of consumers and businesses. Visa
Intelligent Commerce is designed to let AI agents find and purchase products on a users behalf, with tokenized credentials, authentication and spending controls built into the payment flow.

As agents move from recommending actions to executing them, banks will need
to ensure transactions remain within the customers intent and risk tolerance. The institution remains responsible for keeping the agent within those
limits. The architecture has to change with the AI Deloitte found that integration with existing systems and tools is the top modernization
challenge for 77 percent of banking executives deploying AI, ahead of security, compliance and cloud interoperability concerns.

Traditional banking systems separate payments, lending, accounts and compliance across different applications. AI agents need to work across those boundaries, combining data and actions from several systems to make a single decision.

Banks therefore need an orchestration layer that allows AI to access those systems without requiring another custom integration for every use case. The core platforms can remain systems of record, while more of the
decision-making happens above them.

This gives AI-forward fintechs such as Revolut or Ramp, as well as new entrants designing their infrastructure from scratch, an advantage over institutions that must retrofit deeply embedded systems. If regulation
remains broadly unchanged, the first major financial institution built around continuous decision-making could emerge within five years.

It may not be a bank in the strict regulatory sense, but it could perform an increasing share of a banks functions, allowing customers to manage their finances.

To thrive, I believe banks need to become institutions organized to make continuous decisions for the customers benefit, not simply to follow instructions. And when it comes to AI, they must stop treating it as a fancy tool to add and start treating it as something to build around. We've
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