Interview

Where banks live today

‍Alan Katawazi: Michael, great to have you here. The mortgage and banking industry is seeing some big shifts due to technology. But before we get into that, let’s set the stage. Where do things stand today? Are banks really still stuck in the past?‍

Michael Gerstner: Alan, great to be here. And yeah, absolutely—most financial institutions are still entrenched in legacy systems, and I mean deep. We’re talking mainframes from the '50s and '60s that are still running core banking operations. And it's not just old tech—it's old regulations that are baked into the way these banks operate.

Compliance

Alan Katawazi: Right, and that’s not just a minor inconvenience. We’re talking major compliance risks, right?

Michael Gerstner: Exactly. Regulations like the Gramm-Leach-Bliley Act, the Dodd-Frank Act, and Basel III are dictating how banks handle financial data, lending practices, and risk management. And let’s not forget the CFPB, which is laser-focused on consumer protection. A lot of banks are struggling because these laws were written before cloud computing and AI were even a consideration. So, when a regulator asks, "How is customer data encrypted?" or "How are loan approvals being processed?" banks often have to go through a labyrinth of outdated processes just to give an answer.

Specifically some of these regulations made a huge difference for the books of these banks. So the Dodd-Frank Act was basically the government's way of saying, 'Hey, banks, you can't just go wild and wreck the economy again like you did in 2008.' It came in after the financial crisis to rein in risky banking practices and put up guardrails so we don’t end up bailing out Wall Street every time they make reckless bets.

One of the biggest things it did was force banks to keep more cash on hand—so if things go south, they don’t just collapse and drag everyone else down with them. It also created the Consumer Financial Protection Bureau (CFPB) to keep banks from pulling shady moves, like sneaky loan terms, hidden fees, or confusing fine print that screws over regular people.

Then there’s the Volcker Rule, which straight-up told banks, 'No more gambling with your own money just to make a quick buck.' They also started doing stress tests—basically, making banks prove they could survive an economic disaster before one actually happens.

And remember all those super complex, risky financial products (like derivatives) that nobody really understood but somehow tanked the economy? Yeah, those got regulated too, making sure banks play by stricter rules when dealing with them.

Now, not everyone was thrilled—smaller banks and credit unions complained that all these regulations made it harder for them to do business. So in 2018, parts of Dodd-Frank got rolled back, mostly easing restrictions on mid-sized and smaller banks. But the core rules? They're still in place, making sure Wall Street doesn’t send us into another meltdown.

Banks and financial institutions modernizing or refusing to modernize? 

Alan Katawazi: So, banks are basically being forced to modernize, not just for efficiency but to stay legally compliant?

Michael Gerstner: That’s exactly it. And the challenge is, when you have a massive institution like Wells Fargo or Chase, every change ripples through thousands of systems and processes. I worked with a mid-size bank that wanted to move their mortgage processing to the cloud, but they ran into a wall with the Home Mortgage Disclosure Act. That law requires incredibly detailed loan data reporting, and their old system wasn’t built for modern compliance tracking. What started as a simple plan to improve performance turned into a full-blown transformation project. And that’s where technology plays a huge role—not just in modernization but in staying compliant. Banks now rely on AI-driven tools, cloud computing, and big data just to keep up with all the regulations. A lot of legacy systems were never designed for real-time tracking or the level of detail regulators expect today. Machine learning is helping flag unusual transactions for anti-money laundering, blockchain is being explored for more secure records, and even AI is scanning legal documents to catch compliance issues faster. But the real challenge isn’t just the technology—it’s getting people on board. A lot of these banks still run on decades-old mainframes, and shifting to modern systems means retraining staff, reworking security, and changing how they operate at every level. What started as a simple cloud migration quickly became a total rethink of how they handle compliance, security, and customer data. At this point, banks aren’t just adopting new tech because they want to—they have to, or they risk fines, security issues, and getting left behind while fintech startups, which were built on modern systems from the start, keep pushing ahead.

Alan Katawazi: Wow. And it’s not just the big guys, right? Small banks and credit unions are also getting squeezed by these regulations.

Michael Gerstner: Oh, for sure. The big banks can absorb regulatory fines or spend billions on compliance teams, but smaller institutions? A single CFPB fine could put them out of business. A regional bank I consulted for had to completely rethink its document retention policies after a regulatory audit found they weren’t properly archiving loan agreements. They didn’t have bad intent—it was just that their 30-year-old system wasn’t equipped to handle modern compliance expectations. So yeah, modernization isn’t just an option anymore—it’s a necessity.

Alan Katawazi: Okay, so we know they need to modernize. What’s holding them back?

Michael Gerstner: Two big things: technical debt and compliance debt. The first one is obvious—these legacy systems are old, fragile, and deeply embedded. But compliance debt is just as bad. Regulations change, but these old systems don’t, so banks end up layering new compliance measures on top of outdated tech. It’s like patching holes in a sinking ship. At some point, you need a new ship.

Alan Katawazi: Are financial institutions handling this in-house, or are they looking outside for help?

Michael Gerstner: A mix of both. Every major bank has key personnel—what we call the "indispensables"—who have been there since the mainframe era and know how these systems work inside and out. But as these experts retire, that institutional knowledge is disappearing. That’s where firms like Product Perfect come in. We help bridge the gap by bringing modernization expertise while working closely with internal teams to ensure a smooth transition.

Cloud and how it affects or involves modernizing?

Alan Katawazi: With cloud computing becoming more prevalent, do big banks even have a tech footprint that’s ready to shift to the cloud?

Michael Gerstner: For the most part, yes. Many banks have already begun shifting workloads to the cloud, but it’s a slow process. Some applications just don’t migrate well, and banks have to carefully evaluate which parts of their infrastructure can move versus what needs to be rebuilt. There’s also a lot of concern around cloud security and regulatory compliance—moving to the cloud isn’t just about flipping a switch.

Alan Katawazi: What sort of mindset should IT leadership have when approaching this transformation?

Michael Gerstner: IT leadership needs to think about modernization as a strategic investment, not just an operational challenge. The cost of staying on legacy systems—both in terms of compliance risk and lost business opportunities—is huge. Leadership should also adopt an iterative approach—modernization doesn’t have to happen all at once. They should focus on high-impact areas first, like digital customer experiences, automation, and real-time data processing.

Alan Katawazi: How are newer technologies like AI and blockchain impacting mortgage and banking?

Michael Gerstner: AI is playing a huge role in risk assessment, fraud detection, and customer service automation. Chatbots, predictive analytics, and AI-driven underwriting are making loan processes faster and more efficient. Blockchain, while still in its early adoption phase, is being explored for smart contracts and more secure, transparent financial transactions. These technologies are changing how banks operate, but their effectiveness still depends on modernizing the underlying infrastructure.

Take JPMorgan Chase, for instance—they've rolled out their own AI tool, LLM Suite, to help with tasks like writing and summarizing documents. This tool is now in the hands of about 50,000 employees in their asset and wealth management division. Reuters

Another example is BNP Paribas partnering with Mistral AI to integrate large language models across various areas like customer support and sales. This collaboration aims to enhance client services using generative AI technologies. Reuters

Even Meta's Llama AI models are being utilized by big names like Goldman Sachs and AT&T for customer service and document review tasks. It's clear that these technologies are reshaping how banks operate, making processes more efficient and customer-friendly. Reuters

Alan Katawazi: What’s the role of compliance in all of this? Does modernization make compliance easier or harder?

Michael Gerstner: Both. On one hand, modernizing infrastructure makes it easier to implement automated compliance checks, data encryption, and better access controls. On the other hand, new technologies introduce new regulatory challenges. Banks have to make sure that their modernization efforts align with evolving compliance requirements—GDPR, CCPA, PCI DSS, and others. The good news is that cloud providers offer built-in compliance tools that help automate much of this process.

What are the early modernization planning and steps?

Alan Katawazi: So, for a financial institution looking to modernize, what’s the first step?

Michael Gerstner: The first step is an honest assessment of their current tech stack. Banks need to identify which systems are business-critical, which can be phased out, and which need to be modernized or replaced. It’s not just about upgrading for the sake of it—it’s about making sure the technology aligns with their business goals and compliance requirements. Many banks are still running on decades-old mainframes that weren’t designed for today’s digital banking expectations, so understanding where to start is crucial. It’s also important to build a roadmap that balances short-term wins—like improving digital customer experiences—with long-term infrastructure changes. We’ve seen banks get caught up in chasing the latest trends without a clear strategy, and that leads to wasted resources and missed opportunities. By identifying quick-impact changes, like streamlining online loan applications or automating fraud detection, while also planning for deeper modernization efforts like cloud migration, banks can ensure they’re moving forward without disrupting operations. Working with experienced consultants can help make this process more strategic and less overwhelming. Many financial institutions don’t even realize how many redundant systems they’re maintaining until they take a step back and get a fresh perspective.

Alan Katawazi: How else do we work with banks?

Michael Gerstner: We also have met with the parent companies of some banking and financial firms, helping them sort through the technology footprints of their acquisition targets or recent acquisitions. This is a huge challenge in the financial sector, especially with the amount of mergers and acquisitions happening. When one financial institution buys another, they’re not just inheriting customers and assets—they’re inheriting entire IT infrastructures, often with multiple overlapping systems that don’t integrate smoothly. Our guys have gone in and created these amazing and beautiful wall-sized diagrams of all their applications and databases. It’s really quite amazing to see it all in one print-out. It’s one thing to hear that a company has dozens of different loan processing platforms or multiple CRMs handling the same customers, but when you physically see it mapped out—all the connections, redundancies, and inefficiencies—it becomes impossible to ignore. Sometimes it takes up an entire wall to show it all, but it fits as a scaled-down diagram, providing a clear, visual representation of just how tangled things have become. These diagrams aren’t just for show—they become actionable blueprints that leadership teams can use to decide what needs to stay, what should be consolidated, and where they can streamline operations. We’ve worked with private equity ownership to parse through their portfolio companies several times, and I know they have been very grateful. In many cases, they’ve suspected inefficiencies but didn’t have a concrete, data-driven way to prove it until we stepped in. Our work has helped them cut unnecessary costs, reduce tech debt, and even improve security by identifying weak points in their infrastructure. It’s incredibly rewarding to help financial institutions take something that was once chaotic and turn it into a clear, structured plan that makes sense.

What about artificial intelligence? Does that expedite modernization? If so, how much?

Alan Katawazi: What role does AI have in the pre-work for modernization?

Michael Gerstner: Well, AI can help in so much of it. AI is a game-changer when it comes to modernization. It’s not just about speeding things up—it’s about fundamentally changing how modernization is approached. Traditionally, modernization projects involved long discovery phases, endless stakeholder meetings, and a lot of manual effort to map out existing systems, identify inefficiencies, and define future strategies. AI compresses all of that, bringing automation, intelligence, and predictive insights into the equation.

AI can scan through massive datasets, analyze infrastructure, and even recommend optimizations in ways that would take human teams months to accomplish. Whether it’s helping C-level executives structure their strategic vision, generating comprehensive technology roadmaps, or assisting IT teams in code refactoring and migration, AI is turning what used to be a slow, incremental process into something far more dynamic and precise.

But the real question isn’t just whether AI expedites modernization—it’s how much it does. And that depends on how it’s leveraged. When used effectively, AI can cut planning time in half, reduce redundant work, and help banks and financial institutions make decisions faster and with more confidence. It’s not just about doing the same things faster—it’s about doing them smarter and with fewer roadblocks along the way.

It's almost overwhelming to think about. First, let's target the philosophical realm. The AI models (generative ones, for example) can easily help put together a thought model or thought framework for the board of directors or C-level executives to adopt, and then from there, work through that model to create artifacts… then those artifacts can get worked on. This is a huge shift in how strategic planning happens because AI doesn’t just give raw data—it can synthesize insights, highlight key trends, and even simulate different modernization scenarios to help decision-makers weigh their options. Instead of leaders spending months debating different approaches, AI can present structured options in a fraction of the time, based on real-world data and predictive analysis. AI can also analyze past modernization efforts across the industry, identify what worked and what didn’t, and suggest best practices tailored to that bank’s specific needs.

Alan Katawazi: What sort of artifacts are we talking about?

Michael Gerstner: Well, it depends on the size of the organization and the audience, but let's start with some basic modernization project planning documents, charters, and, let's say, strategic documentation. These could include roadmaps, executive briefings, competitive analysis reports, and risk assessments—all of which AI can assist in generating based on existing company data and external market insights. AI can even help draft technology rationalization plans, which outline what systems should stay, which should be replaced, and how they all fit into the bigger picture. For highly regulated industries like banking, AI can pre-screen compliance considerations and flag potential regulatory challenges early on, saving weeks of manual review. And once the big-picture strategy is in place, AI can help craft detailed implementation plans, from timeline projections to resource allocation recommendations. In larger organizations, where multiple teams and departments need alignment, AI-generated reports can personalize messaging for different audiences—one version for executives focusing on ROI and risk, another for IT teams detailing infrastructure shifts, and another for compliance officers ensuring regulatory adherence. It really acts as a scalpel for clarity in an otherwise overwhelming process, ensuring that everyone involved in modernization has the right insights, the right plans, and the right context to move forward efficiently.

I think if we zoom out, really - the best input is to document all your systems well, don’t wait until you’re forced to modernize, and aggressively budget for modernization, of course, including the use of AI along the way and throughout the process. The financial industry is moving faster every day toward AI, and institutions that proactively embrace modernization using AI will be the ones that thrive. I really believe that.