How AI-enabled banking platforms are reshaping financial services

Artificial intelligence has become the “it” tech topic discussed in financial services, with banks delving into area’s on how AI can support digital banking, payments, fraud management, and customer engagement. While AI adoption continues to be the centre of attention across the industry, many traditional banking platforms were not originally designed to support the level of real-time processing, integration, and data orchestration that modern AI services require are struggling to keep pace.

At the same time, financial institutions are operating in an environment shaped by rising fraud sophistication, growing customer expectations for an always-on digital experiences, and mounting pressure to improve operational efficiency. Global online payment fraud losses are expected to exceed $362 billion between 2023 and 2028, while real-time payments and mobile-first banking experiences are becoming standard across many markets.

As a result, banks are increasingly shifting toward AI-enabled technology platforms that can support faster decision-making, more responsive customer interactions, and better operational visibility across digital channels.

Why are legacy banking platforms struggling to support AI services?

Many traditional banking platforms were not built to support the level of real-time processing and integration required by modern AI services. In many institutions, core systems still operate across fragmented environments, making it difficult to unify data, automate workflows, or introduce new digital functions quickly.

This creates a growing gap between customer expectations and what some banks are able to deliver. Younger and digital-native customers have a growing expectation that their banking experiences should mirror the speed, responsiveness, and personalisation they encounter across other digital platforms. Mobile-first users now expect seamless onboarding, real-time responses, and digital interactions that continue smoothly across channels. Research from Deloitte shows that customers now expect fast, end-to-end digital journeys, with consistent experiences and minimal disruption between interactions.

At the same time, fintechs and digital-native providers are introducing AI-driven features more rapidly, particularly in areas such as onboarding, personalisation, fraud prevention, and customer servicing. For traditional institutions, this places greater pressure on existing infrastructure that may not have been designed to support AI-led experiences at scale.

Newer digital banking platforms have since incorporated AI capabilities, allowing institutions to integrate intelligence more directly into customer interactions, transaction flows, and operational processes.

How are AI chatbots and AI assistants improving digital banking experiences?

AI is being embedded across digital banking platforms to support customer engagement, onboarding, and day-to-day servicing interactions. AI-powered chatbots and virtual assistants are helping financial institutions manage customer enquiries, document handling, transaction-related support, and guided onboarding journeys through more conversational and responsive interactions.

Unlike traditional scripted workflows, AI-enabled platforms are designed to deliver more adaptive and contextual engagement, adjusting responses and recommendations based on customer activity and interaction history. They analyse behaviour, transaction history, preferences, channel activity and real-time interaction patterns to help banks understand what a customer may need next, and when it is most relevant to present it.

This is where Recommendation systems and AI assistants are also becoming part of broader digital engagement strategies. While recommendation systems help banks deliver smarter segmentation, next-best-action guidance, personalised offers and relevant product suggestions across digital channels, AI assistants extend this engagement into more conversational and responsive service. Using natural language processing and machine learning, virtual assistants can understand customer queries, support multilingual interactions, automate routine requests, analyse data and source documents, and generate insights. AI-powered chatbots and virtual advisors can help the users find information faster, resolve service issues, understand transactions, receive timely prompts, and access more relevant financial guidance inside the banking journey. In addition, for the banks AI automation helps orchestrate interactions more consistently across channels and devices. This includes areas such as contextual payment experiences, transaction routing, personalised prompts, and more adaptive digital engagement throughout the customer journey.

How is AI improving fraud management and payment monitoring in banking?

The same shift is taking place in fraud management and transaction monitoring. AI continues to play a growing role as payment volumes increase, channels multiply and fraud patterns become more complex.

Fraud is no longer defined by a small number of obvious indicators. Decades ago, banks often worked with fewer parameters, simpler transaction flows and more predictable fraud scenarios. Today, suspicious activity can be distributed across accounts, devices, merchants, locations, behavioural signals, mule networks, payment instruments and digital sessions. The connections are harder to detect manually, particularly when fraudsters deliberately fragment activity to avoid rule-based controls. The value of AI is not only speed. It is the ability to interpret many variables at once, recognise emerging patterns.

Modern fraud platforms now rely substantially on behavioural profiling, machine learning models, link analysis, adaptive authentication, and real-time transaction scoring to identify suspicious activity more accurately while reducing false positives. For example AI-powered SmartVista Fraud Management support omnichannel monitoring across digital banking, e-commerce, cards, wallets, mobile, and instant payments within a unified environment. The platform combines supervised and unsupervised machine learning models with rule-based controls, case management, and AI-assisted investigation tools to help institutions manage fraud more efficiently at scale.

What is Agentic Commerce and how could it affect banking?

Once AI can personalise engagement, support customers and strengthen fraud detection, the next question is how far it can go inside the transaction journey itself. This is where the discussion moves from recommendation to controlled execution.

Agentic Commerce introduces the idea that AI agents can participate in parts of the transaction process on behalf of users while still operating within clearly defined rules and controls. The issue for banks is no longer whether AI can recommend a product or guide a customer through a journey. The more important question is whether a payment can be initiated, checked, confirmed and audited with the right controls around identity, consent, fraud, payee verification and customer protection.

Agentic commerce does not necessarily require a radically new payment rail. It requires controlled delegation across existing and emerging rails. An AI agent needs secure access to payment initiation, clear limits on what it can do, reliable confirmation that a transaction has taken place, and governance controls that define when human approval is still required.

BPC has developed an infrastructure layer for agentic commerce, designed to allow banks and merchants to introduce AI-driven transaction within structured governance frameworks. Rather than operating as standalone tools, this approach provides a white-label orchestration layer that manages how AI agents interact with payments, permissions, and transaction processes, while remaining compatible with emerging protocols. Within the layer, four operating models are offered:

Copilot checkout model - AI assists users through product selection and checkout steps, while the user retains control over the final payment authorisation. This provides a more guided purchasing experience without removing direct customer oversight.

A delegated autopay model - that allows users to define specific conditions, such as spending limits, categories, or geographic constraints, within which AI can execute payments automatically. This is particularly relevant for subscriptions, repeat purchases, and recurring payment scenarios.

Network agentic environment - supports AI agents operating across payment networks through performance such as tokenisation and cross-border infrastructure. This allows interactions to move more seamlessly across systems while maintaining interoperability and security.

B2B agentic procurement – extends these functionalities into corporate purchasing processes. Transactions operate within predefined approval structures, policy frameworks, and budget controls, allowing organisations to automate routine procurement activities while maintaining governance.

What comes next for AI-enabled banking platforms?

For banks, the challenge is no longer simply whether to adopt AI, but how to integrate it in a way that aligns with operational requirements, governance expectations, and existing infrastructure. This is particularly important in financial services environments where trust, oversight, and consistency remain critical.

Rather than replacing existing systems entirely, many institutions are focusing on extending and modernising platforms incrementally, introducing AI where they can deliver measurable operational and customer benefits.

The focus is likely to move further toward integrated ecosystems where AI supports not only analysis and engagement, but the orchestration and execution of services across the broader financial journey.

FAQ

What are AI-enabled banking platforms?

AI-enabled banking platforms are banking systems that integrate artificial intelligence capabilities directly into digital banking, payments, fraud management, customer servicing, and operational workflows.

How is AI used in banking and payments?

AI is used across areas such as fraud detection, onboarding, customer support, recommendation systems, payment orchestration, and transaction monitoring.

What is Agentic Commerce in banking?

Agentic Commerce refers to models where AI agents participate in parts of the transaction process, such as checkout, payment execution, or procurement, within predefined rules and controls.

How do AI chatbots improve digital banking?

AI chatbots help banks provide faster and more accessible customer support by assisting with onboarding, account enquiries, document handling, and routine servicing interactions.

How does AI help detect fraud in financial services?

AI-driven fraud systems analyse transaction behaviour, identify anomalies, and detect suspicious activity in real time using techniques such as behavioural profiling, link analysis, and transaction scoring.