How Will AI Change Banking Alongside Instant Payments?

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Financial institutions face immense pressure to modernize as AI, open finance, and instant payments redefine the banking sector. Adapting legacy systems and embracing real-time data are essential for competitive survival. This technological shift impacts everything from customer experience to operational efficiency, demanding a strategic overhaul of core banking foundations. The integration of advanced tech is no longer optional but a fundamental requirement for growth and relevance.

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Financial institutions are at a critical juncture, navigating a complex technological evolution driven by artificial intelligence, open finance, and the demand for instant payments. The core challenge lies in reconciling decades-old infrastructure with the rapid pace of innovation and evolving customer expectations. This necessitates a fundamental re-evaluation of banking foundations, moving beyond superficial digital enhancements to embrace real-time data and truly modern systems.

The Enduring Challenge of Legacy Systems

A significant hurdle for many established banks is their reliance on outdated core banking systems. Roughly 70% of banks continue to operate on platforms that are sometimes mainframe-based and often 30 years old. These systems were not designed for the demands of the modern digital era, leading to fragmented, siloed, and often poor-quality data. Processing is frequently batch-based, making real-time data availability a significant challenge. This technological debt severely limits a bank’s ability to innovate, respond quickly to market changes, or meet the expectations of digitally native customers.

While banks have maintained relevance and market dominance for over 20 years despite predictions of their demise, the current wave of technological change, particularly with AI and instant payments, presents a more urgent threat. The slow pace of change within the financial industry, often attributed to the need for trust and stability, means that many institutions are still struggling with foundational issues while newer entrants rapidly advance. Without addressing the core, any attempts at modernization risk being mere “digital lipstick” – superficial improvements that do not tackle the underlying inefficiencies and limitations.

The Rise of Fintechs and Shifting Customer Expectations

The banking sector faces intense competition from a dynamic ecosystem of fintech companies and neo-banks, which are often the true innovators in the space. These newer players, unburdened by legacy infrastructure, can build on modern technology stacks from scratch, offering services that are inherently faster, more accessible, and often lower cost. For example, a company like Revolut, unknown 10 years ago, has become one of Europe’s largest institutions by leveraging this agility.

Customer expectations have dramatically shifted. Individuals now expect to access their financial services instantly, 24/7, through mobile applications. They demand quick, low-cost, or even no-cost payments and increasingly seek sophisticated investment products. Many customers, particularly for investments, are opting for neo-brokers over traditional banks due to superior service and user experience. This gap between what traditional banks can offer with their legacy systems and what customers now expect creates a growing vulnerability, as neo-banks continue to grow exponentially by meeting these demands.

Artificial Intelligence: Beyond the Surface

The advent of artificial intelligence, including generative and agentic AI, is widely discussed as a potential game-changer for banking. AI holds the promise of enhancing customer experience, automating processes, and providing deeper insights. However, its true potential can only be realized if banks have the right data foundation. AI solutions require high-quality, real-time data that is readily available and integrated.

For banks operating on legacy systems with siloed, fragmented, or “rubbish” data that is not available in real time, simply adopting AI tools as an overlay is insufficient. Such an approach can simulate innovation but will still necessitate manual workarounds and fail to deliver the full benefits of AI. To genuinely leverage AI’s power, banks must first ensure their core infrastructure can provide the clean, centralized, and instantly accessible data that these advanced technologies depend on. Without this foundational shift, AI adoption risks becoming another layer of complexity on an already struggling system, rather than a transformative force.

Open Finance and the Demand for Real-Time Data

The move towards open finance, often referred to as “Feeder,” represents a significant expansion beyond earlier initiatives like PSD2. While PSD2 focused on instant data transfer between specific players, open finance aims to make data available across virtually every area of financial services. This requires banks to provide instant access to customer data, not just for regulatory compliance but also for collaboration with other financial entities and to empower customers with greater control over their financial information.

The current reality for many banks, however, is that data is fragmented across various systems and often processed in batches. Even with T+1 settlement cycles in Europe, which theoretically allow for overnight processing, the need for instant data for exception management and real-time operations makes overnight processing insufficient. To participate effectively in an open finance ecosystem and support instant payments, banks need a core system that centralizes data, ensures its quality, and makes it available within seconds. The outdated concept of Extract, Transform, Load (ETL) layers, which involve extracting data to ingest it into new solutions, is no longer viable. Instead, banks need reliable APIs that allow data to flow instantly between systems.

Modernizing the Core: A Strategic Imperative

The path forward for banks lies in comprehensive core banking modernization. This is not an easy undertaking; it is a risky and complex exercise that involves migrating potentially 30 years of legacy data. Unlike fintechs that start with a “white piece of paper” and can build on new technology stacks, incumbent banks must contend with existing infrastructure and vast amounts of historical data. There is often resistance within the industry, partly due to the perceived risk and the lack of immediate, visible “celebration” for such internal projects.

However, the alternative is to fall further behind. Core modernization means moving towards a service-oriented architecture with real-time capabilities, a single database, and hundreds of APIs to communicate with the broader financial ecosystem. Some core banking providers have already made this transition, investing heavily – over a billion euros in some cases – to transform their solutions to be instant and API-driven, drawing on 35 years of experience while constantly innovating. This demonstrates that it is possible to combine deep industry knowledge with cutting-edge technology. For banks, understanding that the core needs fundamental attention, rather than just superficial upgrades, is the most important first step on this journey towards competitive survival and future growth.

Frequently Asked Questions

Why are legacy systems a significant problem for banks today?

Legacy systems, often 30 years old and mainframe-based, lead to siloed, poor-quality data and batch processing. This prevents banks from accessing data in real time, slows down innovation, and makes it difficult to meet modern customer expectations for instant services.

How do fintechs and neo-banks impact traditional banking?

Fintechs and neo-banks, built on modern technology, offer faster, more accessible, and often lower-cost services. They set new customer expectations for instant, 24/7 access via apps, creating strong competition that traditional banks with their legacy systems struggle to match.

What role does AI play in the future of banking, and what are its prerequisites?

AI can enhance customer experience and automate processes, but its effectiveness in banking is entirely dependent on high-quality, real-time data. Banks with fragmented data and batch processing will find AI adoption to be superficial unless they first modernize their core systems to provide the necessary data foundation.

What is 'open finance' and why is real-time data essential for it?

Open finance (or 'Feeder') expands on initiatives like PSD2 by requiring instant data availability across nearly all financial services. Real-time data is essential for banks to collaborate, empower customers, and support instant payments, as traditional T+1 settlement and batch processing are no longer sufficient.

Jacob S. Olsen

Jacob S. Olsen

Runs Tech Feed Watch, from Denmark

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