Why Banking 4.0 needs COBOL and a mainframe hybrid cloud environment

What’s so special about cloud-based banking?

The buzz is that the cloud is a cure for every banking malady. Granted, migration offers speed, flexibility and convenience. But, it can also mean threat, danger and complexity.

If you're conducting business on an assurance platform, shouldn’t your primary focus be safety, certainty and simplicity? Customers will be extremely unhappy if crucial data goes walkabout or they can't get to their cash. 

Clearly, next-gen banking must provide the best of both worlds.

It makes absolute sense to use the best processor (mainframe hybrid cloud) for the most important job. Systems of record work brilliantly on mainframes, and COBOL is the accepted language of finance.

Many consider COBOL to be a legacy system. However, “heritage” is a more appropriate descriptor, because the language’s pivotal, orderly and object-oriented configuration is the mainstay for over 40% of digital banking systems today.

The vintage programming language still underpins 80% of in-person financial services transactions, manages 95% of all ATM activities and processes US$3 trillion in daily commerce. There are over 220 billion lines of code, and 1.5 billion more are written annually. In addition, its matchless stability and processing power enable COBOL to ensure banks maintain apps and programs in existing architectures.

With Hogan, you can run your existing bank and partition or open up new partitions using Umbrella on the new code. Over time, you can move loans and mortgage types, doing so within the framework and keeping a common front end.

IBM is addressing the age and skill shortage issue by educating a new generation of COBOL engineers. Creating internal academies is a great idea. And a hybrid cloud environment helps you enthuse your future workforce by letting them know that they'll work across both mainframes and the latest cloud-native technologies.

However, although this long-term strategy is a powerful solution, changing 11 million lines of code to Java or waiting for a new COBOL generation to emerge is unworkable in the immediate term. If you have a time problem, just tackle that head-on; create an academy and bring in the new generation. It's safer, practical and far more cost-effective.

As far as I'm aware, no Tier 1/2 banks across retail and corporate have multiple product lines and cards at scale using 20-year-old Java. It's OK for a NEO retail bank to select 10X, Mambu, Thought Machine and the like, with narrow product lines, small markets and limited customer bases. They can benefit from the extra speed and efficiency cloud computing provides.

But as soon as you decide to increase product depth and complexity or expand into corporate retail and wealth management, the economic gain shrinks. The sheer weight of activity that mainframes lift every second, minute, hour, day, week, month and year beats wholesale cloud cost savings hands down. Plus, any expansion of Oracle’s Java licensing is bound to aggravate the TCO issue.

And, most importantly, once you’ve exited on-prem, backtracking is no walk in the park. 

Question: do these charts reflect the aims of your management team? 

How about customer-centricity?

And digital first? 

Hogan clients have always had access to the features shown in the modern platform-based architecture model (see above). How come? Because of the way Hogan is architected with Umbrella, and how clients have used API orchestration tools such as z/OS Connect and MuleSoft. They've always been able to create services.

Hyper-fast delivery is a Banking 4.0 requirement, which means adopting DevOps, modern tools and a specific mindset. But the barrier to front-end speed is internal processes, not Hogan. Some banks can release new code every other week; others have to wait an eternity for the cycle to run its course.

Hogan clients are always on as long as the architecture is set up and used correctly. They can bring partitions up and down for loans and maintenance, then replicate them in real-time.

Another vital requirement of Banking 4.0 is the real-time use of data, and the screening of live operational change data via Hogan is underway. Soon, once someone opens an account, the information will flow into an analytics database, and geopositioned, real-time dashboards will track which funds are moving where and which accounts are opening/closing. As technology evolves, mainframe hybrid cloud strategies will play a pivotal role in banking’s digital transformation, mapping a journey that pairs tradition with innovation, stability with agility and performance with efficiency.

A mainframe hybrid cloud architecture harnesses the proven reliability and security of mainframes while embracing cloud scalability and innovation. This synergy is critical in an industry where trust and innovation are central to attracting and retaining customers. And while mainframe hybrid cloud architecture is laying a secure foundation, it’s also preparing the bank for a future where agility, foresight and an unwavering commitment to customer service are table stakes for attracting and retaining customers.

Crucially, banks can utilize new cloud technologies while upgrading existing mainframe applications to work more holistically within a hybrid environment. This also ensures software realizes unique hardware potential, an operation many institutions struggle with.

Shifting to a mainframe hybrid cloud infrastructure means improved support for disparate teams, lower costs, greater control and scalability, sustained agility and innovation, business continuity, tighter security and enhanced risk management. 

^{Source: Celent, “Continuous Digital Transformation in the Cloud”}

Mainframes are the true center of critical business operations, with proven track records for reliability, scalability and processing power. Despite being considered “old skool” by some, mainframes offer qualities that make them a perfect fit for modern AI-driven applications. Here are just three reasons why:

• Mainframes have enormous reserves of precious structured/unstructured data that can be used to train AI models. This practice enhances algorithmic potency and authenticity, facilitating better-briefed decision-making and predictive outcomes.
• Mainframes are hard-wired to process prodigious volumes of transactions and handle complex computations. AI workloads involve fearsome calculations, e.g., training deep learning models or running complex algorithms, and that kind of compute power is indispensable. Banks can expedite training and inference by using mainframe processing strengths to cut time-to-truth.
• Mainframe characteristics include heavy-duty security and compliance, essential for handling sensitive data in AI applications. By using mainframes for AI, banks can exploit the built-in security capabilities to safeguard data privacy, ensure regulatory compliance and defend against cyber threats. IBM’s z17 system is developing quantum-safe cryptography to protect client systems and sensitive data against the potentially harmful use of advanced technologies.

Diverting AI to the data location is the most logical plan of action. Repurposing mainframes and using existing hardware/software for AI programs enables banks to sidestep heavy infrastructure investment.

A 2025 IBM and DXC webinar about AI on the mainframe showcased a proof-of-concept that can influence, score and analyze probability. It was carried out on the z15, with a couple of milliseconds of increased latency across millions of transactions. Using a z17 and Telum 1 will be even faster (IBM recently announced the Telum II, which may well be up to four times faster. Future editions of IBM mainframes will deliver even greater processing power with more quantum-safe applications.

For Tier 1 and 2 banks, their future banking platform is already here.

IBM’s Telum processor is geared toward real-time fraud detection/prevention at scale, and for evolving scenarios that employ deep learning inference. It incorporates on-chip acceleration for AI network training across transactions, a first for IBM. The Wall Street Journal article, "Mainframes Find New Life in AI Era ", revealed that the next iteration of the IBM Z mainframe series would include mainstream AI capabilities and large language models (LLMs).

The idea was that enabling AI applications would rejuvenate mainframe computing. This is especially attractive to governments and enterprises focused on data privacy and nervous about storing data in public clouds or relying on openly accessible data. A self-contained AI system could solve the issue.

Not everyone believes that “everything on the cloud” is the answer. The Digital Operational Resilience Act (DORA) specifically says don't get sucked into relying on a single cloud provider or cybersecurity intermediary. The recent massive global outage proved that in spades. Not one Hogan client missed a heartbeat. Some batch processing was delayed, and Windows stymied branch laptops, but cores never lost a thing. No clients had operational issues from running their core to process money.

It’s extremely rare to have a “severity-one” incident (a critical incident with very high impact). And it's never a core issue. We’ve known only one or two Hogan severity-one issues in the past four or five years.

And when it comes to forging ahead and increasing your options, research supports the notion that in-place modernization is a far more effective path for banks.

You could even start a greenfield bank with Hogan, but with all the hype, decision-makers are unlikely to say, “OK, it's a monolith, a legacy, and so on, but why don’t we start a greenfield bank with Hogan?”

Why not, indeed? However, some providers haven’t realized their investment potential at all, and owners must surely be querying when they’ll see a return on their investment. Therefore, ignoring their wholesale cloud adoption, major initiatives could be written off as attempted projects (people tend to change slowly). 

Banking 4.0 lists design thinking, technical feasibility and economic viability as particularly desirable characteristics. We’re developing one or two elements, but rest assured, Hogan is a comprehensive, highly feasible and economically viable solution. 

To learn more about how retaining COBOL and adding AI capabilities in a Hogan-driven mainframe hybrid cloud environment increases security while reducing risk and cost, visit our website. However, if you’d like to dig deeper and discover what Hogan implementation could do for your bank, contact us .