Old banking cores weren’t built for AI in banking. They weren’t built for real-time anything, honestly. And yet, they’re still the default under the hood of thousands of banks trying to keep up in 2026. The result? Slow rollouts, patchwork integrations, and a whole lot of duct tape holding together a crumbling foundation while the fintech world zips ahead.
We know what this feels like. Your team’s ready to launch a new AI-powered feature. The business case is clear, the demand’s real, and the solution’s been prototyped. But then the backend says, “Hold up.” Suddenly, months go by, budget gets eaten up, and the product’s already outdated before it ships. Not because the idea was wrong. Just because the infrastructure couldn’t move fast enough.
That’s the bottleneck Lisa Pent flagged in The Fintercept’s 2026 predictions piece, and we’re grateful they featured her perspective. There’s real clarity in her callout: core modernization isn’t some quiet IT project. It’s the story in banking this year. Because AI in banking isn’t theoretical anymore. It’s live, it’s learning, and it’s pushing every bank to rethink what “core” really means.
Quick hits from 2026’s crystal ball
- Most banks want AI, but their legacy systems can’t handle it.
- AI in banking matters because it’s shaping fraud prevention, lending, payments, and customer experience.
- The myth? That AI can just bolt onto what already exists.
- Reality: without modern APIs and composable cores, banks stall out.
- The shift: banks embracing change gain speed, clarity, and cost control.
- New belief: core modernization is the most practical path to future-ready banking.
- Bonus: it’s not as painful or risky as it used to be.
You can’t bolt the future onto the past
Here’s the thing nobody likes to admit: AI doesn’t just fit in. It’s not a plug-and-play tool that magically improves operations. It demands context, data access, and speed that older cores weren’t designed to support. Imagine trying to stream 4K video on a dial-up modem. Doesn’t matter how great the content is, it’s just not getting through.
A lot of banks still believe they can integrate AI in banking through middleware or side systems, keeping their legacy core untouched. That worked, kind of, for chatbots and some analytics. But today’s AI tools want real-time customer behavior, transaction monitoring, fraud signals, and personalization, all in one flow. Legacy cores can’t keep up. They weren’t meant to.
And no, this isn’t a knock on the engineers who built them. Those systems did the job for decades. But the job changed. Fast. And now it’s time to rebuild where it matters most.
A product team stuck in tech traffic
Think about a mid-sized regional bank with ambitious digital goals. Their product team has mapped out a new feature that uses AI to monitor spending patterns and offer proactive financial advice. It’s supposed to be subtle, smart, and personal. Think less clunky chatbot, more helpful whisper in your wallet.
They’ve got the team. They’ve got the funding. But the moment they try to access live data from the core, everything slows down. API calls take too long. Data has to be batch processed overnight. By the time insights are generated, they’re stale. Everyone’s frustrated. The project either gets watered down or scrapped entirely.
That same team watches newer digital-native banks pull off the same idea in weeks. Customers notice too. They start asking, “Why can’t we get that here?” It’s not a lack of creativity. It’s a lack of core capability.
The good kind of control freak
Now here’s what happens when banks upgrade. With a modern core that supports real-time APIs and microservices, product teams don’t need to beg IT for every tweak. AI systems can access transaction data instantly, adapt offers on the fly, and plug into fraud tools that act before bad actors strike. That’s not hype. That’s table stakes in 2026.
A composable, cloud-ready core gives banks flexibility without sacrificing control. Want to test an AI feature in one region first? Easy. Need to swap out a fraud engine without disrupting other services? Done. Suddenly, it’s not just the biggest banks with the deepest pockets leading innovation. It’s whoever moves smart and fast.
And “fast” isn’t just speed to launch. It’s speed to adapt when things change. Which, in AI-land, happens constantly. Training models evolve. Regulations shift. User expectations leapfrog. A modern core makes change feel normal instead of terrifying.
Why now? Because AI is forcing the issue
There’s a reason The Fintercept brought 17 fintech leaders together to talk shop. Everyone’s feeling the same pressure. AI in banking is no longer a nice-to-have. It’s in fraud detection, loan underwriting, trade credit, personalized finance, and customer service. And it’s learning fast.
Look at this spread:
| AI Use Case | What It Needs from the Core | Why Legacy Cores Struggle |
| Real-time fraud alerts | Instant transaction visibility | Data batch windows, limited access |
| Personalized offers | Behavioral analytics + fast APIs | Slow data syncing, no unified view |
| Smart savings tools | Event-driven triggers | Rigid logic, hard-coded workflows |
| Autonomous payments | Permission management, security | Weak API governance, manual review |
The common thread? Timing. AI needs things to happen now. Legacy cores are stuck in then.
What’s actually needed to modernize a core?
It’s not about ripping and replacing everything. Modernization can mean different paths, depending on where your bank is starting. Some might go full cloud-native. Others layer in middleware and slowly sunset old modules. But the key features to look for include:
- API-first architecture that allows external services to interact safely and quickly
- Real-time data streaming instead of overnight batches
- Modular systems that let you swap pieces without breaking the whole
- Support for AI tools to run adjacent models or trigger automated actions
- Simple developer tools that empower your team, not just vendors
Banks that adopt these not only meet today’s needs but build a base that’ll adapt to tomorrow’s surprises too. The tech isn’t science fiction anymore. It’s affordable, scalable, and increasingly expected.
Big changes, small bank? Still possible.
PentEdge works with banks of all sizes, and we’ve seen how modernization looks different depending on size, strategy, and existing tech debt. There’s no single path forward. But we’re confident about this: staying still isn’t the safer choice.
Modernizing your core doesn’t mean abandoning everything familiar. It means rethinking where your systems help or hold you back. And in 2026, the most common thing holding back AI in banking is a legacy system that was never meant to talk to an AI in the first place.
Got questions about how this actually works? Want examples, costs, or success stories? Contact us. We’re happy to dig in and get specific.
Future-forward finance: let’s zoom out for a second
- Legacy cores were built for a different era and can’t meet the speed or agility AI needs.
- Modern APIs and flexible architectures unlock real-time data that AI depends on.
- The shift to agentic commerce, instant payments, and autonomous finance makes core modernization urgent.
- Bolting on AI to old systems leads to delays, frustration, and failure to compete.
- Banks that modernize gain flexibility, cost savings, and happier, stickier customers.
The future’s not waiting around. AI in banking is already here, reshaping how people spend, save, and interact with money. Whether it’s fraud prevention, smarter lending, or agent-driven finance, the pace is picking up. Your core’s either ready to handle that—or it’s standing in the way.
