Growth Drivers | February 5, 2025
The power of AI on the mainframe
By Chris Drumgoole, EVP, Global Infrastructure Services, DXC Technology
Mainframe computers continue to perform some of an enterprise's most essential tasks, from processing millions of customer transactions in seconds to supporting massive workloads without missing a beat.
And they’re finding new life in the AI era, moving beyond merely transactional platforms to ones that can extract valuable insights, reduce IT downtime and keep data secure.
Stopping fraud in its tracks
In the digital age, banks, retailers and other organizations are increasingly vulnerable to a plethora of fraud schemes that can cause significant financial losses and reputational damage.
But by running AI models directly on the mainframe where the transaction is executed and transactional data resides, the time it takes for data to be processed and made available is minimized. This means patterns can be analyzed in real-time, resulting in the ability to quickly identify—and flag for further investigation—potentially fraudulent activities.
Other activities like automated credit decision-making and loan modification can also be done directly on a mainframe platform, making AI insights and business decisions quicker and easier to assimilate.
By retrofitting the existing payment authorization component of DXC’s Hogan banking system with IBM's Telum chip, banks can perform AI fraud detection on 100% of their transactions.
This allows them to take swift action to prevent losses and protect their customers and can result in a $120M yearly savings for an average tier-1 bank.
An assistant by your side
In today's fast-paced digital landscape, the complexity of applications paired with a growing talent shortage can leave organizations unprepared for rapid spikes in demand or unable to quickly deliver the innovations their customers care about and need.
Due to their monolithic and complex design, mainframe applications are often hard for developers to understand and maintain.
However, by embedding AI directly into the mainframe development process, organizations can tackle service issues as they happen and allocate resources where they're needed the most.
This includes helping with understanding existing code bases, automating the restructuring of computer code, and testing the accuracy of a translated piece of code. This means that when code is converted from one programming language to another, the translated version performs the exact same functions and produces the same results as the original code, with no errors or unintended changes in logic or behavior.
Also, with a dwindling number of developers today able to understand COBOL and other programming languages, new AI tools can assist with older coding language work by enabling developers to automate testing processes, build applications faster and improve overall system performance.
This frees up developers to focus on more complex, creative and exploratory tasks.
An operations-minded approach for the future
Even small IT failures can cost millions and result in reputational damage.
But by integrating AI into their mainframe operations, organizations will soon be able to reduce the time it takes to recover from an outage and bring critical systems back online.
AI will also be able to predict when an event might occur, allowing for proactive measures to prevent or mitigate its impact on production.
In this vision of the future, the need for specialized expertise is diminished and a broader range of mainframe developers can be successfully deployed. The result is a more cost-effective approach as the labor pool expands and the pressure to maintain complex systems with a narrow skill set is alleviated.