Inside Pulse ID's Playbook for AI-Driven Banking with Alex Topaloski
Fresh out of the studio, Alex Topaloski, CEO and Co-founder of Pulse ID joined us in a conversation on his company's customer engagement infrastructure powering Visa's cardholder offers across Asia Pacific. Drawing on Pulse ID's recent white paper, The Age of Knowing, Alex unpacks the three forces reshaping bank loyalty: interchange, partnerships, and intelligence. He explains why banks have solved the data problem but still struggle with engagement, walks through agentic AI architectures and the minimum effective nudge principle, and lays out why Asia-Pacific diversity demands distinct playbooks for Australia, Japan, and Southeast Asia. Closing out, Alex argues the next 12 months belong to banks that prioritize the last mile — where ROI on a decade of data investment finally lands and lays out what great would look life for Pulse ID moving forward.
"So we are making that transition from being a system-of-record platform for engagement, loyalty, and rewards to being a system-of-action platform that drives measurable behavioral change. And I think that is quite a big step forward. The efficiencies that clients are able to get—the outcomes, the revenue, ROIs that it can get on interactions—it's something that people are now going to start experiencing." - Alex Topaloski
Profile: Alex Topaloski, CEO of Pulse ID (LinkedIn)
Here is the edited transcript of our conversation:
Bernard Leong: Welcome to Analyse Podcast, the premier podcast dedicated to dissecting the pulse of business, technology, and media globally. I'm Bernard Leong. Banks today have more data about their customers than ever, and most are still failing to act on it. The gap between intelligence and relevant action is where the next competitive battle in banking will be won. My guest today has spent nearly two decades building the infrastructure that closes that gap, powering Visa's cardholder offers across the Asia Pacific and creating the first hyper-personalized credit card in Japan. Alex Topaloski, CEO and Co-founder of Pulse ID. Alex, welcome to the show.
Alex Topaloski: Thank you, Bernard. Good to be here.
Bernard Leong: I did some reading, and of course we have spoken to each other through one of my investors, Joe, who set us up for today's show. I wanted to start by asking about your path from being a strategy manager at ANZ to becoming a fintech founder across Asia Pacific. It's not a conventional one. You have said the spark was a strategy paper you wrote for the ANZ board in 2010 on location intelligence, exploring fraud detection, customer onboarding, and merchant loyalty. What did you see in that paper that the bank didn't fully act on, and how did that gap become the business you built today?
Alex Topaloski: When we put together those board discussion items, you analyse a technology, identify the use cases, build a view on how a financial institution could benefit from those use cases, and there's always a recommendation and a roadmap of what you tackle first, second, third. That specific technology on location intelligence captivated me. It was about analyzing a very rich data stream of how your customers move in the real world all the time. I saw so many use cases available for a financial institution, and I saw an opportunity not just in that data stream, but in the ability to build a company around guiding enterprise clients and financial institutions through a journey of adopting that technology.
Bernard Leong: After that you left a senior corporate role to found Proximity in Australia in 2011, only to wind it down to focus entirely on Pulse ID in 2016. We've spoken about this — I also wound down my company, which was in the location-based advertising space. You have been building in this B2B fintech infrastructure space ever since, serving banks and payment networks rather than end customers directly. That's a very different kind of founder journey. What does it take to operate in an invisible layer for the last 15 years?
Alex Topaloski: Both of my companies have that in common — that invisible layer, what I call infrastructure platforms. It's actually interesting building products in that space, because infrastructure is a little bit like an organ. You don't know exactly how it works. You just need it to power some functions. You need it to work super reliably. You notice when it misfires. The challenge in building infrastructure platforms is that there isn't a roadmap on exactly what to build against, so there's plenty of opportunities to go down the wrong route.
Bernard Leong: I thought it would be much more focused, because you would know where the problem usually is. Or do you still have to do customer product-market fit at every kind of instance?
Alex Topaloski: You definitely do. You have to see how your clients are adopting and thinking about that technology, especially as the technology landscape changes. The hypothesis in year one may not stand in year four.
Bernard Leong: I actually have a contrary view. I find that moving from B2C or B2B2C is harder than now being in B2B — but that's my own point of view. Looking back across your entire arc, from the ANZ boardroom in Melbourne to building across 11 countries, what is the career lesson that took you the longest to learn that you can share with my audience?
Alex Topaloski: It's unique to my own journey. The lesson that took me a while to realize is: I don't want to build ingredient platforms. In the infrastructure space, it's tempting and common to be solving for a very specific use case. My first company was a geolocation platform really focused around a specific use case. But I've learnt with ingredient platforms it can actually be hard to commercialize, because you've got so many interdependencies on other systems that sometimes make it challenging to scale out. So that learning, within Pulse, we've really taken to be central in what we do and how we build: don't build ingredient platforms — build things end-to-end that solve a function. Over time, that means you need to be building quite a lot to really solve a problem, whether it's customer engagement, customer operations, or whatever it is end-to-end for an organization, as a better way to service that segment.
Bernard Leong: I wanted to ask — in your LinkedIn bio, you talk about choice, lifelong learning, relationships over automation, treating walls as steep learning curves. How do those beliefs show up in how you actually run Pulse ID day to day, and what conviction has been the hardest to hold onto?
Alex Topaloski: Probably the hardest one is lifelong learning as a mindset. Obviously we all say how much we love learning. It's probably the most common response we get in job interviews — people telling us how much they love learning new things. But the day-to-day reality of learning something actually unpacks a lot of challenges that people stay silent on. When you're learning new things, your brain literally builds new neural pathways. So if you turn up on Monday and you spent three hours learning something, you're tired. If you do the same on Tuesday, Wednesday, Thursday, by Friday your brain is cooked. Carrying that mindset week after week, month after month is challenging.
To the earlier part of your question — what we call steep learning curves often come across as walls and obstacles. The job of a new business, of a startup, of an entrepreneur is to find a way. To find a way where others have tried and failed, or find a new way where others have not gone down. Those obstacles that appear as walls, if they are approached as a learning curve, it means you're able to conquer them. There is a method and a process that works, of how you go around it.
Bernard Leong: I'm going to get to the main subject of the day, but I have to ask first — give me a quick overview of what Pulse ID is, and then we can go into the interesting stuff you're all doing.
Alex Topaloski: Sure. Pulse is building the future of customer engagement and loyalty for highly regulated enterprises — financial institutions, telcos, merchants. We're seeing a lot of change in that industry. It's a space we've been building in for a few years. That's central to our mission: build a better mechanism for how an enterprise is able to engage with their customers at scale.
Bernard Leong: What people don't realize is that you have clients like Visa. But I want to start with the white paper you recently published — The Age of Knowing and the Three Forces: why intelligence changes everything. There are now three forces reshaping bank loyalty: interchange, partnerships, and intelligence. Intelligence is where artificial intelligence is the most transformative, but also the one we are only at the beginning of understanding. What does the beginning of the intelligence era actually look like in practice, and what tells you when a bank has genuinely crossed into it — versus still operating on Force One or Force Two economics?
Alex Topaloski: This is the way we think about it. The era of intelligence — intelligent interactions — is just reducing the latency between when a signal from a customer happens and when a journey is sent and engaged with that customer: giving the right reward at the right moment, on the right channel. In an era of intelligence, that happens automatically in near real time, without necessarily the intervention of a human campaign manager. It's no longer feasible to do that at scale across millions of customers, mapping out every individual journey that makes sense for that customer. That is quite an engineering and technology challenge — many systems need to hold hands together to deliver.
Bernard Leong: Possibly also human in the loop, or on the loop, as well, right?
Alex Topaloski: Mentioning that a lot of this happens automatically doesn't mean there are no humans involved. In fact, it's about building that next generation of guardrails — to give full transparency of what the system is doing, and having the human be able to guide that system.
Bernard Leong: One striking diagnostic in the paper: banks have actually solved the data problem from input to decisioning, but they still have problems with engagement. Insights have accumulated faster than they can turn into action. What broke down between those two stages, and why has it taken the industry such a long time to think about this gap?
Alex Topaloski: You're right to frame that as two stages, because they're two separate problems that are interconnected, and they need to be solved sequentially. The first is being able to build some form of unified view of what a customer does — the data, their interests, the way they transact with you. For a financial institution, that is a really big ask, because these are systems that have been layered over a number of decades and need to work in highly regulated environments at different points of the lifecycle. Some have been purchased in the last 12 months and integrated. Other systems have been around for a couple of decades.
The first phase of that is financial institutions investing a lot in their data architecture, and I believe that phase has been largely successful. A lot of investment has gone in by financial institutions, and now their internal data architecture is in a good place. The second part of that problem is: once you've got that data and those insights, how do you translate that into a customer experience? How do you pair it with a reward? How do you price it in the right way? How do you make the economics of customer engagement work? That is now the next phase of innovation that is going to be needed to get an ROI on a lot of that data investment. So it's not that anything broke down. They just need to be solved in a sequential way.
Bernard Leong: It's actually a more gradual shift, just that the second part is less taken care of — that's why you think of it as a two-stage process. The competition for banks is not just from other banks offering richer rewards. It's also from super apps and lifestyle platforms offering smoother journeys. Think about GrabPay in Southeast Asia — it appears at the instant you tap on a coffee subscription, and it feels like a daily win. What is the correct diagnosis of why those experiences feel so different from what most banks deliver, and why is it so hard for banks to replicate that experience?
Alex Topaloski: We work with banks. We work with payment networks. We also work with fintechs, telcos, and newer businesses. For a super app, they've been able to start with a much simpler system architecture. They've been able to commission systems built recently — being able to pull that data and build very smooth journeys on top of it.
The reality for financial services is that there's a lot more data, a lot more products, they've been operating for many decades, and it's a highly regulated environment. Every institution needs to think very carefully as they're making system changes — because of the obligations they have to regulators and to customers. The upside of getting something right is often a lot lower than the downside of getting something wrong. So it is prudent for financial institutions to step through this journey in a careful, thoughtful manner. The task is a lot greater for them in terms of needing to transform themselves.
Bernard Leong: The white paper maps four stages of intelligence: capture, connect, understand, and engage. You mark the first two — whole-bank data ingestion and the unified data layer — as more on the data lake side. Real-time intelligence is actually in progress. But triggering things like contextual offers at the right time, right channel, right value still remains a goal state. We've both been in the location-based advertising space — I always tell people it's not so simple to just get to that correct location with the correct offer. The gap between understand and engage is essentially the business you are in. What is actually blocking a lot of banks from completing that final stage?
Alex Topaloski: It goes back to the earlier part of our conversation — being able to use the data assets that are now in place, that perhaps were not there five years ago, and being able to trigger customer experiences and rewards at scale. Many loyalty platforms and vendors are not designed to actually absorb the new data signals available inside the bank environment. Loyalty and engagement systems could be built to ingest payments data, but if you want to throw another 20 or 30 different types of signals at them, it's actually not that easy for them to come up with the right experience.
Bernard Leong: That is where the white paper had an interesting line — the smallest, most effective nudge, not the biggest reward. It's behavioral economics. It's not the most frequent touchpoint — it's the minimum effective intervention. What does your system actually have to learn in order to find that minimum, for a given customer at a given moment?
Alex Topaloski: You need to be able to reason on an individual customer's data trail, and be very deliberate on when you choose to trigger something. Today I woke up, went downstairs, and used my card to have a coffee. I then took a Grab to see you. I've got my salary coming in this afternoon. I've got direct debits for all my bills. If a bank sent me a nudge on all of those signals, it would be a nightmare. That is a horrific experience. So much of it goes around being able to understand when there is a signal to act on, versus a signal that you're just going to put into your learning loop.
Bernard Leong: How do you prevent the system from optimizing for engagement metrics at the expense of genuine customer value over time?
Alex Topaloski: Engagement shouldn't be the primary KPI. The primary KPI should be: what do you want to do with this customer in terms of deepening the relationship, improving the services you're providing, incentivizing them to take that next action? Just going on engagement will get you in trouble and actually destroy customer trust. It's about understanding the right moment where it does make sense.
In some way, this is the same product learning we had in the geolocation era. People would say, "Oh my God, if you drive through 50 geofences — I want to make sure that a customer doesn't get 50 push notifications." It's about being subtle and having the intelligence of knowing what that right moment is.
Bernard Leong: For geolocation we also had to think about how high-touch the customers we were dealing with were at that point in time. Moving forward, there are four signal categories that banks now harness: banking data, digital behavior, life signals like salary, credit, savings milestones, and AI outputs like propensity scores and churn risk. If you aggregate all those signals at a level of precision, you can also create what is called a real privacy risk. Specifically in Japan — which your playbook describes as a market with high savings orientation and trust sensitivity — how do you build personalization and customer experience as recognition rather than surveillance? This is the number one question on every customer's mind.
Alex Topaloski: You've nailed one of the key challenges. Just because a bank has access to that data now as a new asset doesn't mean they're able to share it with third-party vendors and partners. A lot of that data is private, sensitive, regulated. That distills down to a product challenge: how are you able to act on signals without knowing the full context, and without needing a lot of that data?
The good thing is that technologies like AI are coming up with new solutions. A couple of weeks ago we launched our MCP — Model Context Protocol. That allows a standard protocol for us to be able to get instructions for the next generation of bank AI systems and models, where they don't need to share with us the full data stream that we need to understand of why a decision has been arrived at — but we just need to be able to execute on that in real time. There are other practices too, to minimize data sharing — anonymization of data, and so forth. That is another reason why, in this space, you have to really step through and think through these things very carefully.
Bernard Leong: You have a long-term partnership with Visa for over five years. You power their offers exchange marketplace across Asia Pacific. You also work with GCash in the Philippines, and with JCB in Japan. These are very extraordinary anchors for a B2B startup. What does it actually take to earn and maintain relationships like that — commercially, technically, and in terms of security and regulatory compliance? People underestimate the amount of certifications B2B companies have to go through.
Alex Topaloski: It distills down to very old-fashioned concepts of reliability and delivery week after week. As you mentioned, a lot of our partnerships are multi-year relationships. This is not about running a successful pilot, or running a successful implementation — it's really about having the organizational structure and focus to be able to deliver time after time, across different markets and different use cases. One of those tickets to play is being able to do this at bank-grade security. The regulatory journey has been quite extensive — to be able to ensure that everything we do is penetration tested, compliant to all the regulations. That's really the hill you have to climb if you want to work in our space. Ultimately it comes down to having the organizational commitment to just go through that step.
Bernard Leong: The Visa partnership got you an innovation award, but I suspect there were definitely hard moments — every time you work with such a big organization, you have to deal with a lot of what is required there. What was the toughest thing you had to face or prove in order to keep the relationship intact? I always admire companies that can have very long-lasting partnerships with big companies.
Alex Topaloski: With Visa, we're launching new things. The mindset that a large organization has when launching a new capability with clients is very different to a smaller organization or a startup. The tolerance for things going wrong is a lot lower. We've had to really understand that and appreciate why it needs to be like that — mainly because the impact can be so much greater. Maybe step through the partnership with a bit more empathy. That often doesn't come easy for a startup, because it does go against a lot of the startup principles of "move fast and break things." We haven't been able to carry that mindset.
Bernard Leong: No, you have to be a stable structure.
Alex Topaloski: If we broke things, we wouldn't really be able to operate in the financial services space.
Bernard Leong: Move fast and break things is easier for B2C, but in B2B it is totally the opposite — and people don't appreciate that when they go into that space. On the funding levers — there's smart pricing, where AI allocates incentives at high-propensity moments. There's reward debt through tiered relationship status. There are partner-funded offers where merchants bear the cost. And subscription bundles that create predictable revenue. In your experience deploying across so many markets, which lever is actually the most underutilized by banks, and why do they keep defaulting to the least sustainable one?
Alex Topaloski: The hardest model to get right is smart pricing. Grab and Uber have surge pricing — we'll see how the same trip costs very different depending on demand and drivers around us. If you try to extrapolate that for a financial institution with many different pricing points and many different products, it's very difficult to get right. It also becomes really hard: how do you market and communicate that to customers? If you're going to be marketing a certain product, customers need to understand what some of the benefits are. If the mindset is, well, it's going to change depending on who you are and the circumstances, that can also impede the adoption of the product. The hardest one is probably smart pricing — and yet I think it's inevitable that the industry is moving in this direction, because it's now able to make different pricing decisions at a more granular level than previously was possible.
Bernard Leong: You operate across Singapore, Japan, Australia, New Zealand, the UAE, and Oman — multicultural contexts simultaneously. What is the one quality that separates the people who thrive in that environment from those who don't, and how has your own leadership style evolved as the company has scaled from a startup to a global platform?
Alex Topaloski: You have to be able to work with people from different cultures and backgrounds. That's a given. We look for other traits in individuals — what they enjoy, what their values are when it comes to their job, what type of skills they bring in, and what type of skills and mindsets they want to be developing. We've been able to carry a playbook of how we enter markets that is not necessarily constrained to where you live or your cultural background.
Bernard Leong: What's the one thing you know about Pulse ID and what it is building towards, that very few do?
Alex Topaloski: The future is going to be very different. The future of customer engagement, from a system perspective and from an experience perspective, is going to be very different to the way we experience it right now. Building towards that future is the priority, and guiding clients and organizations on that journey is going to be our number one mission. The future is a lot more intelligent, and the type of technologies that need to work together are only now becoming more apparent and obvious.
Bernard Leong: I'm going to go straight to the point. You talk about intelligence — so we ought to go with AI at the core. The winners, as you've talked about, is that the age of knowing will allow AI to choose the timing and the price of the nudge, and let an event-driven stack do the heavy lifting. What does the architecture actually look like when AI is making those timing and pricing decisions autonomously, and what kind of guardrails would you need to exist to prevent it from drifting toward outcomes that serve the bank but not the customer?
Alex Topaloski: We're learning that, in that statement, AI will actually work in multiple points of the stack. This is not about necessarily having one AI brain that manages the experience end-to-end. We're learning that we need to work with different implementation models for clients. Some clients want to use internal models for specific use cases where they have very personal and sensitive data they're simply not able to share. For other data already plugged into a platform, they actually want to rely on our intelligence and our own AI and agentic capabilities. In some ways, a little bit like an open Claude model — being able to choose the brain for an individual task. We see an opportunity in that. From an architecture perspective, dependent on the client and the use case, the platform is intelligent enough to be able to select what brain is actually going to do the reasoning.
Bernard Leong: How do you ensure the guardrails that go in there?
Alex Topaloski: The guardrails are not fully understood yet, because a lot of these use cases are yet to be properly tested in production. It's about having guardrails through your implementation plan — to be able to build and identify how you go about managing for all of those scenarios. There are certain guardrails — for example, do not let an LLM actually analyze the data, mainly because it hallucinates and is going to give you the wrong information maybe two to five percent of the time, which is really not acceptable. You can't operate in that customer engagement space where the accuracy of the data is 95 to 98 percent. One of our platform principles is: have the AI being able to reason on what tools it's able to select, but it's not actually going to be analyzing the data directly. That's just an example.
Bernard Leong: That also has other things you have to watch out for, like degraded output as well from the AI. A lot of people are talking about agentic AI — it's also the direction everyone is going toward. Systems don't just recommend, they also act. From a loyalty context where you operate, what does it mean when the AI is not just curating an offer, but autonomously sequencing the cardholder's entire engagement journey across products and life stages? My question is a bit deeper here: at what point does the system actually stop being a tool and start becoming something closer to a participant in the customer relationship — with its own form of judgment about what matters to a person?
Alex Topaloski: We're actually surprised at how fast that transition is happening. When you start introducing agentic and AI capabilities, it can surprise you how far it can go in recommending the experiences. As we are testing the new version of our platform, it's moving a lot faster than even we anticipated. 2025 feels like 100 years ago.
Bernard Leong: 2026 is even faster.
Alex Topaloski: That's exciting. A bit terrifying. Overall, what is the transition we're making? We are making that transition from being a system-of-record platform for engagement, loyalty, and rewards, to being a system-of-action platform that drives measurable behavioral change. That is quite a big step forward. The efficiencies that clients are able to get, the outcomes, the revenue ROIs on interactions — that's something people are now going to start experiencing.
A couple of interesting points: there was an NVIDIA report I looked at last week — the State of AI in Financial Services. Customer engagement was the number one AI use case for banks right now, and it's also the use case with the strongest ROI in a business case. There's a lot of change in this space, and as industry participants, it's important that we're part of that process.
Bernard Leong: For financial services, AI is actually the earliest penetration in. There's a lot more understanding in financial services on AI. Even when I was working in AWS, a part of engagement was always the banks, because they really have a very clear use case, specific to the AI side.
Alex Topaloski: Why do you think that is?
Bernard Leong: First, in financial services there's a lot of data. There's also clearly unstructured data insights, where people want to find patterns, find precision points where they can target. It's a combination of that. Where they get tripped the most is trying to create their customer 360 — the data lake that everyone sells to them. That takes them a little bit more time. But they took their time to build it, and what's happening is that some banks who did that reap the rewards, and some who didn't, didn't.
It is precisely because in banking, everything is about numbers and precision. AI is actually a better fit. I call it product-market fit for AI to actually enter the sector much earlier than the rest of them. These days we talk about ChatGPT, but if we go back in time, banks have been using AI — which usually surprises me when people say, "Oh, no, it's now going into banks and everywhere." No, no, they have been there all along.
Alex Topaloski: That's a good point.
Bernard Leong: We underappreciate that all the time. Banks and payment networks you serve are all investing heavily now in their AI capabilities. As foundation models — ChatGPT, Claude, DeepSeek — commoditize, every financial institution can run inference on transaction data. What remains proprietary about what Pulse ID does, and where does your moat actually sit in a world where the model layer is becoming widely accessible?
Alex Topaloski: The technology is widely available. What is super valuable is a system of action that works with a guaranteed ROI, with a specific configuration that can be repeated across use cases and across markets. That framework is carrying immediate ROI for clients — that's one point.
The second is being able to bring in an ecosystem of partners — merchants, subscription companies that can be part of that rewards ecosystem for an organization. That has a direct impact on the funding and the economics of the program. The moat is shifting. It's shifting around: how do you... what is that system of action that is generating experiences that are yielding results? An organization like us is able to get those learnings across the network and defray costs across the network, whereas any individual organization themselves has a more limited data set to be able to make those learnings and arrive at those decisions.
Bernard Leong: The white paper maps out the Asia-Pacific landscape with pretty good precision. Australia and New Zealand face a thin and declining interchange, so the playbook is more merchant-funded rewards and tiered relationship benefits. Japan and Korea require what you call quiet recognition — fee waivers and rate boosts as predictable entitlements, because emotional assurance beats flashy rewards in a high-trust, high-sensitivity culture. Then you have Southeast Asia, which is super-app competition — routines live outside the bank app entirely. Which of the local playbooks is furthest from where most banks in those markets actually are today?
Alex Topaloski: There's a lot of diversity in terms of the environments banks operate in when it comes to engagement and rewards. There are opportunities to improve across all of those. Probably the most challenging is environments where you're competing with much newer businesses that are now hitting scale — whether you call them fintechs, super apps, or e-commerce marketplaces. The reason that's most challenging is that the trait of those organizations is just so different. They start with a simpler architecture. They're able to build smoother journeys. That is the most challenging space.
Bernard Leong: It's interesting that you also mentioned, for example, SMBC or Liv in Japan integrating their payment, savings, and lifestyle benefits into one single flexible ID. Commonwealth Bank's Yello in Australia is blending app-personalized offers and merchant partnerships in response to low interchange. GoPay in Indonesia is doing rewards across rides, food, payments in real time. Are these actually outliers, or leading indicators of where the whole region is moving?
Alex Topaloski: The three examples there are organizations that are very successful in their markets. They have tens of millions of customers, and they have put in a lot of thought in how they want to shape that next era of engagement for all of their customers. I would see them as leading indicators, not outliers. They could be outliers in being further ahead on a specific use case at this moment in time. They're able to get there because of the focus they've put in to building those organizational muscles, and they have the scale to drive real impact.
Bernard Leong: The conclusion from the paper was that this is going to be a direct challenge to the boardroom. The winners won't be those who reward more — but those who understand more and act fast in the moments that matter. If you were advising someone sitting in a banking board meeting in Singapore, Bangkok, Manila today, what's the one decision a board needs to make in the next 12 months while most are still deferring?
Alex Topaloski: Our advice would be: don't forget about that last mile. Getting the data and the insights could be 80 percent of the work, but getting the ROI on all of that really comes in ensuring that the 20 percent also gets prioritized — because that is actually what shapes the experience that goes down to the customer. The opportunity to unlock a lot of ROI on all of that investment, not just in the last three years but maybe in the last 10 years, is contingent on that last step — being able to have systems in motion that can ingest a variety of bank signals. That's not just for banks, but for many enterprises and telcos that are on a similar journey.
Bernard Leong: What's the one question you wish more people would ask you about Pulse ID, what you are building, or anything about loyalty, personalization, or AI in banking, that they don't?
Alex Topaloski: It's really about being more proactive and talking about the future.
Bernard Leong: What is the future, then?
Alex Topaloski: The future has fewer systems — more capable systems. The architectural decisions we should be taking now, to be able to power that, require a bit of courage. They require participation in the market. That's why we're happy when we see NVIDIA reports around it being a popular use case. Ultimately, this is not about loyalty or customer engagement — it's about being able to shape behavior. That is a really important part of any B2C business.
Bernard Leong: My traditional closing question. What does great look like for Pulse ID in the next few years?
Alex Topaloski: Great for us would really be to be one of those platforms that powers experiences in this era of intelligence — being able to work with great clients, reputable clients, and to continue delivering in a trusted way to them. That will be a challenge because of the amount of change in motion right now. That is the mission we take on.
Bernard Leong: Alex, many thanks for coming on the show. Really interesting insights. This is probably one of the more in-depth conversations I've had thinking about loyalty, AI, and the context of where things are going in the banking industry. In closing — any recommendations that have inspired you recently?
Alex Topaloski: I've read The Fountainhead by Ayn Rand a couple of times. I read it recently. It's a classic, written in the 1930s. I can definitely recommend that.
Bernard Leong: How can my audience find you and your work, and learn more about Pulse ID? I'll definitely put a link to the report I read.
Alex Topaloski: We want to make the report complimentary for your audience. You can reach me at alex (at) pulseid.com. You can go on our website, pulseid.com, and find us on LinkedIn.
Bernard Leong: You can find us on any podcast platform, on YouTube, on Spotify, and on the main site. Alex, many thanks for coming on the show, and let's continue to talk.
Alex Topaloski: Sounds good. Thanks very much for having me.
Podcast Information: Bernard Leong (@bernardleong, Linkedin) hosts and produces the show. Proper credits for the intro and end music: "Energetic Sports Drive" and the episode is mixed & edited in both video and audio format by G. Thomas Craig (@gthomascraig, LinkedIn).
