Gen AI can prove to be a powerful tool for institutions that struggle to keep up with larger institutions but don’t possess the resources, and time to match their throughput.
Today’s micro case studies dive into how two CUs are improving their lending decision making and marketing efforts through incorporating Gen AI tools in their workflows.
1) How Commonwealth Credit Union is improving strategic decision-making in lending through Gen AI
When it comes to analytics, 61% of lenders find the large swathes of customer and lending data available in the market overwhelming and 73% report that their limited ability to leverage data impacts their competitiveness, according to research.
This points to a significant gap between data sources and FIs’ ability to extract useful insights. Lenders that can understand their positioning compared to their peers in factors like delinquency, chargeoffs, and interest rates are better able to provide competitive products.
Commonwealth Credit Union is particularly aware of how this impacts the firm’s ability to compete. “Our competition isn’t waiting weeks for data. They’re making decisions today on data that they got today,” said Jaynel Christensen, EVP, at Commonwealth Credit Union.
The backstory
Recently, the $2.5 billion, Kentucky-based CU decided to fill in this gap by integrating a tool by Zest AI called LuLu Pulse, which uses Gen AI to consolidate multiple data sources like NCUA Call Reports, HMDA, and economic data. This ultimately allows lenders to gain insight into how their products and services compare to their peers by querying the platform.
The recent integration of LuLu Pulse builds on the Commonwealth’s long standing partnership with Zest AI, through which the CU has also utilized underwriting resources and fraud protection tech.
The biggest value prop for the CU is the turn around time and efficiency.
“A manual underwriter for our organization is expected to underwrite in five to seven minutes, looking at 15-20 data points while probably answering a chat message, taking a phone call or talking to the person that’s walking behind them, Zest is making a decision in 2.4 second, looking at somewhere between 180 – 230 data points. That’s significant in the ability to determine risk, which has helped us be able to lend deeper and expand those that we can say yes to,” she said.
The masterplan
The launch of Gen AI-powered tools have required firms to go back to board rooms and build policies anew. Christensen recalls that, in late 2023, the Gen AI adoption curve required the CU to construct policies around what the technology could and could not be used for. Since then, the firm has revised its policies in some respects and prioritized identifying the best Gen AI tools to be utilized within the firm.
At the 2024 Governmental Affairs Conference (GAC), Christensen came across LuLu Pulse for the first time, and had been workshopping how the CU could leverage the tech for internal strategic and analytics improvements ever since. “Everybody is in a race to stay on top of AI and Generative AI technology. We’ve been very similar in that approach,” she said.
While Lulu Pulse is not used for credit underwriting, it does help CUs with improving their strategic decisioning in board rooms as well as understanding the twists and turns of the market.
Currently, the firm’s Finance and Data Analytics teams are using the tool along with its leadership.
“We have been working with the lending team and challenging them that the next step we want to take is, how do we start diving deeper and making those tweaks to adjust the risk tolerance and be able to make adjustments in the underwriting decision scoring model quicker,” she said.
2) How DUFCU’s marketing department is improving segmentation and targeting with AI
While Gen AI plays a significant role in content generation across the internet, it is unclear what part it plays in content creation for highly-regulated industries like financial services. But this may be changing.
The backstory
For small CUs in particular, Gen AI’s potential to improve workflows and efficiency is much greater.
“Smaller credit unions are resource constrained. There’s a desire to provide more personalized member engagement in the credit union space, but that runs up against having lean teams and limited resources,” said Vertice AI’s CEO Mitch Rutledge.
One CU that is experimenting with how the new tech can enable it to expand reach and build a stronger marketing funnel is Duke University Federal Credit Union (DUFCU), which recently integrated Vertice AI’s copywriting tool called COMPOSE.
For DUFCU’s Director of Marketing Jennifer Sider, purpose-built tools focused on the financial services space offer her a significant advantage over free Gen AI tools available to the public. It’s also better than the manual alternative of managing the whole copywriting process alone.
“To be able to have more control of the content and create our brand identity, consistent messages, personalization, and the compliance piece, it had all these different elements to it that would serve as, really like another part of the marketing team and getting us started with content,” said Sider.
The masterplan
COMPOSE’s integration is already changing how Sider is allocating resources within the organization. “In the past, I would most likely outsource the content, and I still do a bit of that, but now I can do more in house. The content is already aligned with our brand, and that’s helping in the workflow, speeding things up, in crafting our messages,” she said.
Before CUs start using COMPOSE, the tool first learns what the firm’s brand voice and identity is, either from a brand guide or the firm’s website. COMPOSE also stays updated with NCUA requirements, helping compliance and marketing teams ensure that their messaging aligns with changing regulatory stipulations.
For DUFCU’s Sider, the integration’s success is going to be demonstrated by the turnaround time and volume produced by the marketing team, as well as the engagement with its messages. “It could be as simple as likes and clicks, it could also be inquiries, calls, and product adoption – so various metrics that we can use along the way to see if the messages are resonating with our members,” she said.
Post-integration, Sider is focusing more on building messages that focus on member growth. She feels her efforts now may be more effective due to the tool’s ability to provide better segmentation and targeting.
“The marketing team can prioritize delivering high-quality content that drives new member growth. COMPOSE is equipping us to elevate our standards of excellence, while streamlining our efforts, ensuring our acquisition campaigns are highly personalized, on-brand and efficient,” she said.
Banking digital assistants may be common now, but in 2017, Bank of America was one of the first to be thinking about how they make the firm’s customer experience more powerful. The answer was an in-house build of a digital assistant that required the firm to hire PhDs in linguistics and build a collaboration structure that could facilitate teams from different departments.
In 2024, BofA clients interacted with Erica 676 million times bringing its total interactions since its launch in 2018 to 2.5 billion.
On the show today, Hari Gopalkrishnan, who leads Bank of America’s Consumer, Business & Wealth Management Technology team, joins us to tell the tale of how the firm built its industry-leading digital assistant, Erica.
Hari shares how the firm has gradually expanded Erica’s remit beyond consumer banking to also include multiple lines of business and individual and corporate clients across the firm’s global footprint.
It’s a dive into what it takes to push the boundaries in this industry, how the firm thought about development, testing, expansion, and how Erica’s capabilities can be expanded with the recent innovations of Gen AI.
CX as the impetus behind Bank of America’s investment into building Erica
Back in 2017, Hari Gopalkrishnan’s team realized that despite the expanding remit of the BofA app, customers were still flocking to traditional channels like banks and call centers before they tried self-serving. A dissection of this behavior served as the catalyst for the Erica build.
“We were putting tons and tons of features into our app, a five inch screen, and our customers were still walking into the branch, calling into the call center … It’s hunting and pecking, and it was hard to navigate once you put more than 10 or 15 key features. And so our first insight was, you want to be able to have your customers interact with a platform in a way they choose, not the way you choose,” said Hari.
Why Bank of America decided on a DIY approach to build Erica
Although the industry is pretty well-versed in building, deploying, and improving chatbots now, the landscape was quite different in 2017, when BofA had first thought of developing Erica. There was no blueprint for what a banking digital assistant looked like and technology providers were few and far between. The slim pickings in the marketplace, as well as the limited applicability most software had in the financial services, fueled an internal build.
“There were a bunch of startups that were coming out, we spent a few weeks with them and either they disappeared or were going to get gobbled up. So it was a very volatile marketplace for acquiring software that did this. People were trying to play in this game, but weren’t quite getting it. The ones that understood some level of NLU (Natural Language Understanding) didn’t understand financial ontology,” he said.
“We did an assessment, and we looked at third parties, like we always do, but at the end of the day, it just wasn’t going to work out. At the same time, we found a couple of really good open source NLP engines. They were strong, solid, and very well regarded in the industry. We actually hired up a team. We always had good technical engineers. But we also actually hired people with PhDs in linguistics to work on this. Then we started to work with our teams to figure out: what is the digital experience going to look like?”.
How Bank of America structured teams across the org to build Erica
Building Erica required BofA to think across organizational silos, and really invest into creating a collaboration framework that would allow Erica to improve CX without compromising on risk tolerance.
“We actually took the Agile construct to the next level. We had teams set up in [different] regions which were actually in the room. It was the engineering team, the UX team, the appropriate legal team, all opining day in, day out, on all aspects of the platform. The sprints were not just engineers running off and UX coming in weeks later. It had UX teams embedded. In fact, when I used to visit the teams, it was sometimes hard to tell who was in the design team and who was in the engineering team. That was actually the power of how this came together,” Hari shared.
The fork in the road: User testing proved presumptions wrong
Designers and developers have a conceptual map of the software they are building and they also spend time trying to understand their users’ behaviors and pain points. But there is no silver bullet for getting everything right, right off the bat. Hari shares how testing motivated a major pivot in Erica before it launched.
“We thought we would have to invest just as much in voice as in text -– that people would half the time talk into the app, and half the time they would type into the app. Then we go to a small group of customers, and we get more feedback from this. And the feedback we got there was people were just typing in text 90% of the time. They were rarely using voice.”
Gen AI potentialities for Erica
Bank of America is not sleeping on Gen AI – it’s just chosen to stay quieter than most. “As we look at the emergence of Generative AI, we actually see that classification can actually get a lot better. You can actually talk even more naturally in a natural language. So that is just a natural sort of expansion of where we go with Erica. We have about 25 different proof of concepts right now, many of them are actually about to get into production, which use a Large Language Model in some way, shape or form, to continue to enhance the work that we’ve been doing,”.
—
The following excerpts were edited for clarity
BofA’s blueprint for Erica’s expansion into multiple lines of business
Some of it actually is using fit for purpose language models that are pre-trained on certain things. So, for example, for employees there are available technologies that actually are trained on things like integration into your HR system and integrating into your help desk system. We don’t want to go build a whole bunch of things that actually have been built by somebody before.
The reason we built what we built is because nobody was building that before. When it came to employees, we realized that we can leverage all the goodness we have on NLU (Natural Language Understanding) and User Experience. We also found that there are available models that actually do a really good job of NLU to service intent and to calling of existing HR platforms.
We had to figure out case by case, do we start a native build? Do we integrate with existing models when it comes to Erica for business banking there? We had to go to a different set of data sources. You had to make sure those sources were clean. You had to make sure, in some cases, that there was an API available to make that interaction happen … in some cases, many of the services in the past may have been built for a specific User Experience or a specific application, we had to make sure that they get rebuilt or reimagined to be invoked by a chat bot, because sometimes you may need clarifying steps. You may have a multi-step process before you actually call an interaction. That also helped us become better in our core platforms, because that helps us now be ready for the future.
How BofA is balancing ROI, risk, and innovation when it comes to Gen AI
We have an AI Council. Even though we’re obviously a very large company, we try to work in a very integrated fashion and look to learn from everything that’s going on across the company. There are lots of parts of this company, and we come together. You could say it slows us down, but we’re okay with that. We ask, what are the pilots and POCs you want to run? And why do we want to run those POCs and pilots? The people involved in that council involve senior business leaders, senior strategy leaders, senior risk leaders. We’re asking, does this thing align with our risk framework? We have a risk framework that has 16 points of risk. You can imagine bias, intellectual property, transparency, and explainability, in there.
Is the work you’re going to do, going to abide by those risk frameworks? Is there an adequate human in the loop so that you can make sure that the thing doesn’t run away? How do you measure the performance? What guardrails are you going to implement? Those are the things we look at as we implement any of these proof of concepts and eventually take them to commercial use.
The second part is we also look at, what is the mindset you have on the ROI generation of doing this work, because none of this stuff is cheap. This is something everybody is wrestling with. There’s so much hype out there that people are throwing out, I’m going to spend a billion dollars. I’m going to spend $5 billion and when you ask the question, tell me what your bottom line is going to be, what are you going to get in return? The answers are a little bit more diffused.
So we’re taking an approach of saying, we want to understand how work gets done. We want to understand activities, jobs, tasks. We want to understand what part of those tasks cost, what money. And then, when you implement solutions like this, what’s the ROI?
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
Notes from the desk:Hello and welcome to The Quarterly Review, where I dive into what executives from some of the best brands in financial services are focusing on in this quarter. In the last year, we have surveyed executives from both banks and fintechs about their intentions and goals for the year. With the new year, I am excited to bring you another “review” in this series. It’s where we compare the exec’s goals with results and see how well his plans stood the test of time.
Our review articles in this series are an exclusive offering for our TS PRO subscribers. If you want to dive into the juicy stuff and read the details of their labors and fruits —beyond the executive summary below— please consider becoming a TS PRO subscriber.
In this edition we will check back in with Current’s CTO, Trevor Marshall.
Executive Summary
In September of last year, I sat down with Current’s CTO Trevor Marshall to speak about his plans for the rest of 2024. At the time, Marshall was completely focused on continuing to drive the firm’s momentum, as well as optimizing the firm’s underlying technology infrastructure.
In this new year, Marshall is back to report how the firm has done a deep dive into model improvement to reap significant product and performance benefits:
i) By focusing on model-driven deployment of features, Current was able to improve the value it delivers to consumers that use its Paycheck Advance. Recently, the firm made it possible for more customers to be approved for the product as well as increase the amount they are able to take home.
ii) In the transaction risk detection space, Current has been able to improve its AI models through heuristic deployment to significantly improve its model’s performance and curtail the risk of third party fraud.
The Full Review
The Reviews in The Quarterly Review series are a member-exclusive product. If you would like to keep reading please consider becoming a TS Pro subscriber by clicking below.
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.
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.
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.
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.”
Over the past few months we have seen fintechs leverage their apps to reach more customers as well as make their business models more resilient. Here are three micro case studies from Public, Acorns, and Copper Banking that show how these firms are using their apps strategically.
1) How Public is increasing its reach by bringing its investment-focused chatbot into a standalone app
Late last year, online broker Public launched its Gen AI powered chatbot as a standalone app called Alpha (named after the chatbot itself). Alpha is available worldwide while Public’s investing app is available to customers in the US only.
The launch is an interesting strategy that extends Public’s presence beyond its current app and audience while serving as an engine for the firm’s commitment to incorporate Gen AI more heavily into its identity and product.
The backstory
Last year Public’s co-CEO and co-founder Leif Abraham said that the firm is planning on actively leveraging younger investors’ DIY preference.
“As they grow their wealth, only parts of their portfolio will be managed. We expect that these managed products will look more like “guided products”, where the investor decides themselves which strategy to pursue, with help from the platform and AI,” he said.
It’s this change that Public wants to address as it develops its platform and product strategy. But the chatbot’s capabilities right now as well as market readiness and regulations are not quite where they need to be to make this happen. “Currently Alpha, our AI assistant, is solely used to provide insights into the markets, public companies, and other assets. In the future, Alpha will expand to help people manage their portfolio,” he shared at the time.
While the new app doesn’t quite achieve this goal, Public is consistently making incremental improvements to how deeply Alpha is tied into the Public investing experience. Over the course of a few months last year, the firm expanded the chatbot so that it pinged customers when relevant events like a dramatic stock move were happening with explanations on its causes.
With the recent launch, the new app allows Alpha to learn from a much wider audience and also brings in new customers to engage with Public.
The master plan
The new app is free for Public customers but comes with an introductory offer of $1/week for everyone who’s not currently signed up.
“Where the Public app which is a fully fledged, powerful multi-asset trading platform offering stocks, treasuries, corporate bonds, crypto, options and more – Alpha is focused only on your watchlist and context around it,” said Jannick Malling, co-CEO and co-founder of Public.
The Alpha app is only available to iPhone users at the moment and is listed as an experiment by the company. It is unclear at the moment how long the firm plans on keeping the standalone app as part of its digital footprint.
One thing is clear however: Public puts a lot of stock in Alpha’s current capabilities, and opening it up globally will allow the LLM behind Alpha to get better at its job and also may enable the company to open up its fractional investing platform to a global audience down the road.
2) Multiple apps to rule them all? Acorns bets on kids-focused financial products through the Acorns Early app
Sarah Hoffman is Principal AI Evangelist at AlphaSense.
Have you ever said “please” or “thank you” to ChatGPT or Gemini? Recently, OpenAI stated that chatting with AI like a person can result in misplaced trust, and the high quality of the GPT-4o voice may make that effect worse. Even without voice, these systems are highly responsive and seem to “understand” user needs, making some people treat them almost like humans. We are seeing this trend across different industries, applications and even personal relationships, like the possibility of marrying AI, and beyond interpersonal relationships, such as viewing AI as a higher power. But AI isn’t human. It’s built on data, algorithms, and code. While AI can mimic human interactions, it doesn’t possess intuition, emotions, or the ability to make moral decisions.
However, with human guidance, it can be an extremely valuable tool. At AlphaSense, we’ve seen the immense demand for generative AI-driven search. Since launching our first generative AI feature in 2023, customers reported that they are saving 11 to 50 additional hours per month. McKinsey estimates generative AI will add over $200 billion in value for the banking sector, and 43% of financial services companies use generative AI.
Does that mean we should start thinking of AI as a colleague, capable of taking on everyday tasks just like a human?
Keeping AI in Check
Imagine a seasoned financial analyst facing a complex market decision. Beside them, an AI system rapidly sifts through data, spitting out predictions in seconds. Tempting as it is to lean entirely on AI, there’s a nagging feeling that something doesn’t add up—a geopolitical event, perhaps, or an emerging market trend that hasn’t been fully quantified. This scenario underscores one of the biggest risks of anthropomorphizing AI: over-reliance.
Financial teams require vast amounts of data to guide their success, and leveraging generative AI that can not only source, but can extract insights from, that data is critical. That said, AI lacks the contextual understanding and critical thinking that financial professionals bring to the table. How do we leverage AI’s strengths without falling into this trap?
Financial institutions need a structured approach to implementing AI. First, it’s crucial to define AI’s role clearly. Rather than viewing AI as replacements for human workers, teams should see them as powerful tools that enhance human capabilities. Start by identifying specific, well-defined tasks that AI can handle, and implement a regular review process. AI needs to be monitored, and its outputs should be audited regularly to ensure accuracy and reliability. Training your team is equally important. Financial professionals should be educated not just on how to use AI but on when to trust its recommendations and, most importantly, when to rely on their own judgment.
What Generative AI Can Do for Financial Teams
While AI shouldn’t replace human decision-making, it’s incredibly effective in taking on routine tasks that free up time for more strategic and creative work. Also, generative AI can spark innovation and accelerate learning in ways once thought impossible. Some examples:
Streamlining data analysis for investment decisions: Consider the flood of financial data, including broker research, global news events, and earnings calls. Generative AI can process this information at lightning speed, highlighting key trends and insights that might take analysts days to uncover. At AlphaSense, our AI capabilities are layered over premium, pre-vetted content so that users can not only uncover information quickly but can also have the peace of mind that the insights they are seeing are trustworthy and reliable. In a high-stakes industry, neither speed nor accuracy should be sacrificed.
Boosting creativity and innovation in investment strategies: Generative AI can serve as a powerful brainstorming tool for financial professionals, helping to generate new ideas and perspectives. AI can simulate various market scenarios, analyze historical trends, or identify patterns that may be missed by human analysts, sparking new ideas for investment approaches and highlighting potential risks.
Accelerating financial learning:Generative AI can also act as personalized tutors, rapidly synthesizing complex financial information, news, regulatory updates, and market insights to help professionals keep up. For instance, rather than simply analyzing data, AI can break down emerging trends, explain the impact of new regulations, or summarize the key points of lengthy reports. AlphaSense’s first generative AI tool, Smart Summaries, exemplifies this. The tool provides highly accurate summaries pulled from millions of documents across equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own content. This allows finance professionals to quickly grasp new concepts, deepen their expertise in specialized areas, and expand their knowledge in an industry that’s constantly evolving.
A Tool, Not a Teammate
AI is here to stay, and its role in financial institutions will only expand. But as powerful as generative AI is, it is still a tool—not a teammate. By recognizing the technology’s limitations, establishing clear guidelines for its use, and training teams on how to collaborate effectively with AI, institutions can fully take advantage of generative AI’s potential.
As we move further into the age of AI, financial institutions have the opportunity to become more efficient and innovative. Understanding where AI fits—and where it doesn’t—in the team dynamic is an important step in driving long-term growth. The future of AI isn’t about making it human but using it to enhance our own human creativity and strategic thinking.
