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‘Trust me, I’m an algorithm’: How fintech is rebuilding customer confidence in the age of AI

The financial services industry has always been built on trust. Artificial intelligence is editing the rulebook on what that means. As banks and fintechs are pushing to deploy AI across everything from fraud detection to personalized recommendations, they’re discovering that customers’ definition of trustworthiness has evolved far beyond traditional metrics like security and reliability.

Today’s consumers want to know not just that their money is safe, but how algorithms are making decisions about their financial lives. They’re requesting transparency about data usage, explainability in AI-driven recommendations, and proof that these powerful new tools actually serve their interests, not just institutional bottom lines.

We asked industry leaders across financial services, fintech, and their supporting ecosystem how they’re navigating this new trust landscape. Their responses reveal both the complexity of the challenge and the emerging strategies that are actually working.

The new trust equation

The numbers tell a stark story about consumer sentiment. According to recent research from Accenture, while banks remain the most trusted entities for protecting customer data, 84% of customers are concerned about how that data gets used. Even more telling: only 26% are comfortable with extensive AI usage for data analysis, even when it promises better personalization.

“Today’s customers are no longer just evaluating institutions on performance — they’re scrutinizing how their data is used, how decisions are made, and whether emerging technologies like AI act in their best interests,” explains Monica Hovsepian, Global Senior Financial Services Industry Lead at OpenText. “This shift demands a new trust contract: one built not only on accuracy and speed, but on transparency, explainability, and ethical AI deployment.”

The message is clear: personalization must be transparent and demonstrably beneficial. Financial institutions can no longer assume that faster, smarter service automatically equates to better customer relationships.

Beyond the algorithm: Human-centered AI

For companies serving underbanked populations, this trust challenge carries additional weight. Kelly Uphoff, CTO at Tala, emphasizes that AI innovations must solve real customer problems while protecting dignity and identity. “Not all customers will be dazzled by AI unto itself,” she notes. “The technologists building these new solutions don’t often come from the communities we serve.”

Tala’s approach involves co-creating technology with customers from day one: showing early prototypes, listening to pain points, and incorporating feedback throughout development. They’ve also made hiring from the communities they serve a priority, creating a diverse workforce that better understands customer needs.

This human-centered approach echoes across different sectors of financial services. As Taran Lent, CTO at Transact + CBORD, puts it: “AI doesn’t replace the human relationships at the heart of meaningful engagement, it enhances them by making every touchpoint more relevant, timely, and personalized.”

The fraud fighter’s dilemma

Most likely, nowhere is the AI trust challenge more acute than in fraud prevention, where the technology serves as both weapon and shield. Parilee Wang, Chief Product Officer at Alloy, describes navigating AI from two sides: “It’s being used both as a tool for fraudsters and a tool for fraud fighters.”

While generative AI has enabled fraudsters to scale attacks like synthetic identity fraud, Wang argues that the real innovation lies in moving beyond detection to action. “An AI tool that alerts you to fraud without taking action is like a home alarm that goes off when someone breaks in. If it doesn’t call the police or lock the doors, what’s the point?”

Yinglian Xie, CEO and co-founder of DataVisor, sees AI transparency as critical to maintaining customer trust in fraud prevention. “The ability to explain and verify how AI systems work and the data that drives their decisions is of utmost importance,” she explains. The most effective approaches leverage AI to increase fraud detection while ensuring frictionless customer experiences, proving that security and convenience can be complementary rather than competing priorities.

Practical trust-building strategies

Many concrete trust-building strategies are emerging from early AI adopters in financial services:

i) Label and explain: Public’s approach involves clearly marking all AI-generated content and emphasizing the need for independent verification. “By clearly indicating that content is AI-generated and emphasizing the inherent risks associated with such outputs, we help our members understand what they’re using,” says Rachel Livingston, Director of Communications at Public.

ii) Value at every interaction: Scott Mills, President of William Mills Agency, advocates for using AI to provide consistent value: answering customer inquiries, explaining complex situations, and offering tailored solutions. The key is eliminating friction while adding genuine utility.

iii) Human oversight by design: Derek White, CEO of Galileo Financial Technologies, emphasizes that there’s no “set it and forget it” approach to AI in financial services. “AI applications are only as good as the data that goes into them, and the human oversight and strategy used to guide and deploy them.”

The content and communication challenge

As AI impacts how customers seek information, traditional marketing and communication strategies need updating. Anna Kragie, Account Director at The Fletcher Group, notes that with large language models changing how people look for answers, brands need “a smart AI content and PR strategy centered on content that builds trust with customers.”

This means pivoting toward more authentic, conversational content that directly answers buyer questions, while using media relations to establish authority on high-credibility news sites. In an environment where AI can generate massive volumes of low-quality content, human curation and authentic expertise become more valuable, not less.

Finding the balance

The self-driving car analogy keeps appearing in these conversations, and for good reason. As Brandon Spear, CEO of TreviPay, explains: “Just as autonomous vehicles require human oversight, AI-driven banking solutions must strike a balance between automation and necessary human intervention. The goal is not to replace human judgment but to enhance it with data-driven insights and improved efficiency.”

This balance requires what Transact + CBORD’s Lent calls “robust AI governance frameworks”, clear standards and best practices for both internal teams and vendors, combined with responsible piloting and focus on measurable outcomes over hype.

The trust dividend

Financial institutions that get this balance right stand to gain a significant competitive advantage. As Hovsepian notes, “In a digital-first world, where convenience is expected, trust has become the true differentiator, and the most valuable asset any financial institution can earn.”

The companies building trust in the age of AI are embedding security, privacy, and fairness into their AI models from the ground up, then communicating these efforts clearly to customers. They’re working to prove that AI can enhance rather than replace human relationships, and that transparency doesn’t have to come at the expense of innovation.

The financial services industry has always been in the trust business. AI isn’t changing that fundamental reality – it’s just raising the bar for what earning that trust requires.

This article features insights from members of Tearsheet’s monthly PR/Comms Working Group serving the best professionals in financial services and fintech. Contributions came from both in-house communications leaders and agency executives who represent major players in the financial services sector.

Become a member of Tearsheet’s monthly PR/Comms Working Group — reach us here.

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How Citizens Bank is building GenAI with a five-year vision, not just quick fixes

Investment in data is the hallmark of successful Gen AI implementations, according to Citizens’ Chief Data and Analytics Officer, Krish Swamy. 

Giving us a system wide view of how Citizens is leveraging Gen AI, Swamy joins the podcast to talk about harnessing the power of data to drive decision-making, enhance customer experiences, and navigate the complexities of digital transformation in the banking sector. 

Our conversation delves into the challenges and opportunities of building a data-driven culture within a traditional banking environment, and how Citizens is positioning itself at the forefront of financial innovation through strategic analytics initiatives.

Swamy, who also heads the firm’s Generative AI Council, shares his vision for the future of data in banking and the tangible ways Citizens is turning data insights into meaningful actions that benefit both the institution and its customers.

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Long term view of Gen AI implementations

Citizens’ approach to Gen AI is best described as cautious and optimistic. While the firm is not rushing into any use case and is instead taking a methodical approach to evaluating every time a process or task can be improved by Gen AI, it is also sketching out what role the technology could play in the future for its employees and customer experience. 

“We’re not just taking a process, or a component within a process, and applying Generative AI there. While that might be the starting point, the end game is always going to be: How does this function three or five years from now? How do we work towards that end game?” said Swamy. 

Strong data based foundation as a differentiator

Swamy is a firm believer in using a comprehensive data infrastructure as the scaffolding for new technological implementations. “When we invest in data, when we make data easily available, and when we teach people how to use data, I think they become a lot more effective at being able to self-serve. So creating that foundation is an area of differentiation,” he shared. 

One area where this focus helps the bank drive powerful results is fraud, which has seen a significant uptick since the pandemic, according to Swamy. “We’ve spent a lot of time overhauling the fraud infrastructure and the fraud platform itself. There are multiple sub components around fraud detection, claims processing, case management, which all are parts of the overall fraud value chain. And we made investments to improve the quality of those platforms,” he said. 

Helping the fraud team stay ahead of bad actors, is the firm’s move to the cloud, which should be completed by the end of this year. “We are almost 80% migrated to AWS, and it makes it easier to get access to data and we are able to bring better data when it comes to our fraud defenses,” he said. 

Having a centralized source for the data also ensures that fraud teams that include analysts and contact center employees are working from the same source of information. This allows these teams to be more effective and coordinated when trying to spot trends and undertake fraud mitigation strategies, he shared. 

Another area where the firm is applying data-led Gen AI strategies is the call center. “A lot of the customers’ questions tend to be fairly narrow, almost esoteric and [call enter employees] have to reference procedure documents to be able to give that answer,”  he said.

In the past, call center employees have used keyword search to access this information, but now the firm is using Gen AI and helping call center agents learn how to prompt more effectively to reach information,” he said. 

Similarly, the firm is also using the tech to help its developers take care of some of the most frustrating parts of coding: documentation and testing. “Those are areas where we’ve been able to find a lot of leverage from giving software development engineers the right tools to be able to do the testing, documentation, sometimes even writing code, and become more efficient at that,” he shared. 

Citizens’ partnership strategy 

When it comes to assembling the right technology partners, Swamy believes building consistency across the organization is the golden rule. “For instance, there are multiple teams that need the ability to have machine learning platforms, and it is conceivable that everybody then goes out and figures out their own thing. That would be a really bad outcome, because I think that would lead to proliferation of costs and would lead to loss of control,” he said.

“What you do need to do is make sure these solutions are all integrated with all of the other solutions, which is a lot of work for sure. The place where we have spent a lot of time on homegrown solutions is on managing our data. Those are critical assets which are unique to us, which we would not be comfortable leaving completely in the hands of a commercial solution or a bought out solution,” he said.

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Temenos CPTO Barb Morgan on measuring ROI, step by step modernization, and AI-enabled banking

Banks have a challenging time responding to technological leaps like AI primarily because of their compliance-comes-first approach. Financial institutions must also manage the technological debt of their legacy systems when approaching modernization. 

On this episode of the Tearsheet Podcast, Temenos Chief Product and Technology Officer, Barb Morgan, offers a refreshing perspective on how financial institutions can embrace technology while maintaining their human touch. Her insights reveal how banks, particularly regional institutions, are balancing innovation with customer service and regulatory compliance.

Morgan’s approach emphasizes “augmented intelligence” over artificial intelligence, positioning AI as a collaborative tool for these firms. Her view of AI’s potential in this industry stems from her deep experience working with regional and large banks at Temenos, as well as her time at firms like FIS and Capital One. 

The conversation highlights how Temenos is helping banks modernize at their own pace by  offering flexible solutions that can be implemented module by module. It also dives into how these firms are measuring their ROI on modernization initiatives, a must-have in this market. Lastly, Barb shares how her firm partners with its banking clients to work on unique ideas. 

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Industry Changes on the Horizon

Morgan predicts that AI-enhanced customer experiences will soon become standard. “Five years from now, [AI] will be the expectation,” she notes, adding that banks must move beyond simply wrapping digital interfaces around legacy systems to fully integrate their data for AI capabilities.

Data at the center

While many banks have focused on digital transformation, Morgan identifies data integration as the next critical step for institutions looking to leverage AI effectively and deliver modern CX. “As FIs improve the data… the positive outcome will be that they will be able to leverage data differently,” she shares, emphasizing how consolidated data systems will enable banks to better serve their customers and implement new processes and workflows enabled by AI. 

Augmentation not replacement with AI

Morgan envisions AI as a side-by-side agent that enhances customer interactions. “AI should be a side by side agent that offers personalization, offers the ability to create a more human experience,” she explains, noting Temenos’ development of a banking copilot that helps agents understand customers before conversations begin.

Flexibility in Modernization

Understanding that each bank has unique infrastructure needs and technical capabilities, Temenos has developed a multi-option approach to system modernization that accommodates various technical environments and strategies.  “We’re getting great conversations with our clients, almost an appreciation because we understand that doing a full core banking overhaul may not be in their year’s plan,” Morgan says, explaining how institutions can start with smaller, targeted modernization projects before considering a complete core replacement.

Time to live doesn’t have to span years

Lengthy implementation timelines can be a significant deterrent for banks that are thinking of undertaking modernization efforts or deploying new tech like AI. But the Temenos team has developed modular processes that dramatically reduce deployment time. “We had a new bank go live on our core system, and we were able to get them up and adding accounts in less than three months,” Morgan adds. 

Co-Design with Customers

Rather than developing solutions in isolation, Barb says Temenos actively engages with banks through user groups and design partnerships to ensure new features address real market needs and banking requirements. “When we get that momentum and we do a bit of co-design with our customers in our User Group forums, and it becomes obvious whether or not it’s something that we should build and move forward with,” she explains. When a client wants a unique feature or process implemented the company works with its technology partners as well as a group of regional banks to test the efficacy and experience of building such a feature out. 

The following excerpts were edited for clarity

What’s top of mind for banks with tech and AI

Customers are number one, at the heart of what banks are thinking about. Number two is regulatory and compliance. AI is really raising that expectation that there will be new regs and compliance, and it’ll just get tougher. But the growing complexities, new rules, proposals coming forth are definitely top of mind. We’re seeing some of our customers who are actually looking to allocate in the back half of the year. They need to save some funding for those types of initiatives, because they see them coming and if they want to play in the AI space, they have to be ready for it. And then operational efficiencies. When we think about AI it has been around for a long time, but previously, it’s been a lot of chat bot type things, automation of singular processes. 

Successful AI-based improvements stem from investing in data

I think five years from now, AI will be the expectation. So helping banks to be able to create that human experience leveraging AI is going to be critical. I also think we’re going to see this in the data space. We saw the digital transformations happen, and a lot of banks use digital as a wrapper around legacy systems. 

Now what we’re seeing is that data evolution has to occur side by side for them to be able to leverage AI, and so I think we’re going to see them improve the data. We spend a lot of time on this in our conversation with customers. If you have five systems right now, we have to get that data together so that you actually have your full picture. But then the positive outcome of that is being able to leverage data differently. 

AI as a banking copilot

I talk a lot about augmented information, or augmented intelligence, and it often leads us into the conversation around how AI should be a side by side agent that offers that personalization and the ability to create a more human experience. When you call a bank, you’re trying to get something resolved, you get put on hold, then you get transferred to another department. The process just goes on and on. With AI and having that side by side agent to help them, they can gather that data instantly and at speed. 

We’ve been working on a banking copilot, and we have a bit of a private preview right now with some customers.

Customized modernization pathways

By giving that flexibility and choice to our customers, we’re really getting a positive reaction. They say, hey, actually, I’m really happy with my retail banking. It’s great, but I want to up my game in the payments space. Can I just upgrade my payments? The flexibility that either we run it for them in a SaaS environment, so that they can focus on their customers and not infrastructure, or if they have a strong infrastructure team for them to be able to put in their own cloud, and then lastly if they are more comfortable running on premise, that’s okay, too. So it all comes back to flexibility and choice. We’re getting great conversations with our clients, almost an appreciation that we understand that doing a full core banking overhaul may not be in their year’s plan.

How banks measure ROI on modernization initiatives

For many years, banks have really tried to understand the cost of their legacy systems and now, I think they have a better understanding of really what the costs are. We have one of our customers in the US, in particular, who’s saying, we actually want to first go forward with deposits, get that up and running, and be able to actually measure turning off the legacy. And then we’re going to move forward with loan originations. 

What that allows them to do is both – measure and make sure that their flexibility and their customers are taken care of, but also they can really nail down that return on investment of moving forward with their modernization. Sometimes it’s good for them to be able to go to their board and say, hey, look, we did this portion. Here’s what we saw out of it. Now we want to move forward.

How Temenos approaches unique ideas

Making a single one-off customization is not efficient. The way that our applications are built, clients can build on top of their application. So if there’s something that’s truly unique, then we would pair them with one of our trusted partners and have them build out that customization. But oftentimes, when an idea is brought forth, we say let’s go tease this out. Let’s do a bit of a design partnership and get five or six regional banks and see if this is truly regional. And then when we get that momentum and do a bit of CO design with our customers in our user group type forums. 

We have a unique ability to co-design using a couple of very simple questions. Here’s the problem. Did we get the problem right? Yes or no. It’s a very simple process, but you end up with really rich products out of it that you can incrementally roll out, versus spending 12 – 18 months building something. They’re invested from day one and they like seeing that customization come to life. In a way, it’s part of them as well.

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How AI is disrupting financial services and how companies can respond — with Publicis Sapient CEO, Nigel Vaz

Nigel Vaz, Publicis Sapient

As advances in artificial intelligence impact the financial services landscape, banks and financial institutions face a critical inflection point. AI has been a part of banking operations for years, but the emergence of generative AI is creating unprecedented opportunities — and challenges — for innovation and business transformation.

In a wide-ranging conversation on the Tearsheet Podcast, Nigel Vaz, CEO of Publicis Sapient, discusses how AI is fundamentally changing the financial services industry. Nigel shares his deep insights on how financial institutions can navigate this technological disruption, from enabling broader access to wealth management to AI-driven credit models in mortgage lending, and on why some banks are better positioned than others to capitalize on AI’s potential.

Publicis Sapient is a digital business transformation company, focused on helping companies survive and thrive in a world that is increasingly digital. With expertise spanning Strategy, Product, Experience, Engineering and Data & AI (SPEED capabilities), Publicis Sapient helps businesses sustain relevance by adapting to change and capturing value through digital.

In more than two decades with the company, Nigel has acted as a strategic advisor on complex transformation initiatives across industries and geographies, including AI advances in the context of clients’ broader transformation requirements. Nigel is also author of the bestselling business title ‘Digital Business Transformation – How Established Companies Sustain Competitive Advantage from Now to Next’, based on years of partnering with clients to harness the power of digital.

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A New Era of AI in Banking

The financial services industry is experiencing a shift from predictive AI to generative AI, which is creating original content and handling specific tasks to enhance the traditional workforce and accelerate business. As Vaz explains: “We’ve gone from AI in the context of machine learning models to predictive AI to what is now, essentially, creation. Gen AI has brought to financial services the creation of original content, an understanding of natural language and adaptation to tasks that were otherwise considered the purview of people, not machines.”

Data Quality as Foundation

For financial institutions looking to implement AI, having the right data infrastructure is crucial. Vaz emphasizes this point: “Start with ‘What is the state of your data?’ Banks who’ve invested in connecting different data sets and organizations that have leveraged their data infrastructure to build an AI strategy are in a very different place to organizations who’ve simply started with AI implementation.”

Real-World Impact

AI implementations are moving beyond experimentation to delivering tangible business value – through cost-out innovation or growth-oriented value creation. One striking example Vaz shares demonstrates this impact: “In one case, a migration that was scheduled to take 10 years is now being done in three years, and this is from legacy COBOL to Java. These kinds of implementations are creating significant value, not only in the context of time to market but also in how they’re able to take costs out of their business.”

Workforce Evolution

Rather than replacing workers, AI is transforming how financial institutions approach talent and skills development. Vaz believes that upskilling and reskilling initiatives empower employees and ensure organizations remain agile in the face of change: “We often use this frame of learn, unlearn and relearn. More and more in organizations today, the shift in roles is going to need the creation of new roles focused on optimizing AI systems, analyzing data and insights and developing algorithms.”

Future of Financial Services

Looking ahead, Vaz envisions a fundamental reimagining of financial services and how the industry positively impacts people’s lives. He describes a future of democratized financial services: “Rather than an organization essentially trying to sell you a series of products, they will start to provide personalized financial services, where the organization understands that what I’m interested in talking about is not a mortgage rate, but that I’m interested in buying a home. As you start to get that personalized, unique perspective about the person that you’re advising and serving, you create a whole new opportunity to democratize the traditional definition of what it means to be a financial services institution.”