For banks to drive the AI imperative, they must modernize core banking now

HFS heralded 2023 as the year of the digital dichotomy, where enterprise clients balanced the macroeconomic challenges against the big hurry to innovate. Investment slowed in the banking and financial services (BFS) sector, and the focus rested on prioritizing innovation and growth initiatives. HFS Research data from 2024 points to technology arbitrage as enterprises’ value creation lever for addressing the paradox of innovation and growth aspirations against the backdrop of slowed investments. Leading the charge among technologies is AI. A modern core is required for IT and business leaders in BFS firms to effectively pull artificial intelligence (AI) through complex processes and workstreams. As we launch the 2024 Horizons report for core banking modernization service providers, we closely examine the building blocks of a modern core for effectively integrating AI.

Nobody modernizes because they want to; they do so because they must

Core banking embraces all banking services, including deposits, lending, mortgages, payments provided to banking customers, and business functions like transaction processing, regulatory reporting, and risk evaluation necessary to carry out business. What we refer to as the modernization of core banking is transitioning the technology stack supporting banking services and business functions. It includes modernizing layers for infrastructure, systems, data, intelligence, decision making (business logic), engagement, operating models, and platforms through the series of complex initiatives in Exhibit 1 to deliver a next-generation core.

Exhibit 1: Core banking modernization is more than just updating technology; it impacts everything—technology, business, and operating models

Source: HFS Research, 2024

Traditional legacy technology stacks in banks were developed on a product-centric design for a single product line. Over the years, millions of lines of code and monolithic programs were written to hardcode user interfaces, business logic, business rules, and data. Additionally, the lack of proper technology architecture governance during mergers, acquisitions, and regulatory changes has left banks with an inflexible and enormously complex legacy core, making AI integration almost impossible.

A next-generation modern core is about pivoting from a verticalized product-centric operating model architecture to a horizontal customer-centric architecture. A customer-centric architecture creates a modular foundation where data, channels, business logic, rules, and processes are outside the core, and the key legacy stack can be modernized on a case-by-case basis.

In an AI-first bank, AI flows throughout various touchpoints in banking layers

An aspiring AI-first bank must recognize the importance of data management in the core banking modernization exercise; core modernization takes the pressure off the technology stack. Today, banking and financial services (BFS) firms use AI technologies to differentiate their core capabilities, control costs, and drive growth. Once AI is hardwired into BFS firms’ workstreams, high-value impact begins to percolate, starting with the baseline outcome of optimizing operations efficiency through extensive automation. Other benefits include blending banking and non-banking apps, facial recognition, analytics-backed personalization, fraud detection, human-like engagement, omnichannel capabilities, and launching new business models, products, and services.

Setting up a next-generation core for applying AI effectively

The decision to modernize is no longer a binary choice between doing nothing or going ahead with a complete overhaul. There are progressive forms of core modernization:

• Hollowing the core, which involves transitioning a collection of technology layers to a modern core while rationalizing redundant systems to reduce infrastructure footprint
• Decoupling the core banking layers from channels and downstream legacy applications and operating as a headless core, with the business logic remaining in the legacy to support the headless core
• Rewriting or refactoring the legacy code, where mining technologies make it possible to peer inside legacy code—regardless of language—to refactor the code, extract the business logic, or remove the code and replace the application with a microservice

Whichever approach the bank adopts to revitalize its legacy systems and shave years off technical debt, there are a growing number of fintechs, platforms, and vendors offering transform-to-operate support across these distinct progressive approaches, including Temenos, Thought Machine, FIS, Mambu, Fiserv, Oracle, TCS BaNCS, Infosys Finacle, and Finastra. These vendor solutions are cloud-native, microservices, headless, and composable with an open architecture canvas that can co-exist with legacy and cross-pollinate across banking service lines, products, and functions.

Each core banking layer has unique transformation requirements aligned with its purpose and objectives. In Exhibit 2, HFS defines the basic archetypes of a next-generation core to power an AI-first bank, from core tech to robust cybersecurity.

Exhibit 2: Many banks are in the early innings of core modernization, but there are compelling reasons to get on this journey, with AI leading the charge

Source: HFS Research, 2024

BFS leaders need to partner with service providers who can help build discretionary spending and enable core modernization and cost optimization to fuel each other

BFS firms face tight budgets when making upfront investments toward core modernization; many IT and business leaders and their C-suite peers have been looking for new ways to fund and enable core modernization initiatives within their banks. Additionally, BFS leaders seek more from their investments than mere improvements to their core technology plumbing—they want to create a lasting foundation for innovation and competitive advantage underscored by AI.

Partnering with a service provider is beneficial to BFS firms, not just for their capability enablement in the form of talent, technology, and ecosystems to build a lasting foundation but also because the contractual construct can have a positive impact on cash flows, thereby securing a discretionary spending provision to fund the core modernization journey.

The Bottom Line: Savvy bank leaders know core modernization is a requirement for seizing AI’s value as the technology matures. They are leveraging the ecosystem of service providers and core banking vendors to de-risk, accelerate, and enable the journey.

For banking leaders obliged to consider internal and external outcomes, transforming into an AI bank means they can eventually leapfrog competition using AI-driven intelligence to sell compelling value propositions in real time across customer journeys and engagement. Internally, this translates to AI-powered decision making, automated and streamlined processes, increased productivity, employees focused on high-value activities, and lower costs. Shifting from legacy to modern core does not require a big bang approach. Success will depend on how the bank is envisioned through core modernization and how AI capabilities are developed comprehensively across the different layers of banking.