Artificial intelligence (AI) is no longer just a buzzword – it is essential to financial services’ development. The keys to unlocking this nascent technology’s full potential are relevant applications and concrete use cases. To identify what these use cases
are, we must look beyond the hype.
To find out more, Finextra spoke with Temenos’ chief product and technology officer, Barb Morgan.
The limitations of one-way personalisation
According to a report from the Economist, over
75% of bankers believe Generative AI (GenAI) will significantly impact their sector.
But today, personalisation is about analysing the past. It involves studying historic data, in order to tailor financial services to customers’ preferences. Often, however, the personalisation becomes limited – placing large swathes of customers into one-size-fits-all
buckets – with dynamics introduced only when it comes to pricing.
“Over my 25-year career, I’ve seen how the right technology architecture can empower institutions to meet their clients’ expectations,” Morgan said. “The journey toward cloud and AI is no longer optional.”
GenAI: Three areas for application
Ultimately, AI should not be used merely for optics – it should solve a business’ top challenges. But how does this work in practice?
Here are three areas where GenAI can help to re-imagine the banking value chain.
1. Customer-centricity
“From operational efficiencies to proactive decision-making, predictive AI and GenAI are becoming foundational for building smarter SaaS solutions that anticipate customer needs,” stated Morgan. “They will change how customers interact with their banks.”
The process might unfold like this:
- The bank asks its GenAI function which customer demographic is its most profitable. Factoring in attrition and lifetime value, the model recommends that the bank targets 25–35-year-olds.
- The next question is, what exactly should this group be offered? The AI’s answer – drawing on customer data, transaction histories and behavioural patterns – recommends, for example, a high-yield savings account or a credit card.
- The final question would be around how the bank’s 25–35-year-olds use cards, and the AI could produce details of spending habits.
This simple example reveals how a GenAI assistant can provide banks with new product ideas, as well as an optimal target market, and the areas in which to provide it. Clearly, acting as the interface between customer data and the business, GenAI has the
potential to draw greater customer satisfaction and boost banks’ margins.
2. Flexibility
Once the product is distributed, GenAI can step in to introduce further tailoring options for consumers.
“For our clients, these AI advancements translate into real, tangible benefits. Let’s imagine a customer is seeking finance for a new car,” Morgan said. “GenAI can rapidly mine the individual’s financial data and put together bespoke packages that would
not otherwise have been available from the pre-made product catalogue. From here, the customer can tweak the offering further, until their needs are met – whether this be adjusting the length of the lease, requesting regular expenditure reports, and so on.”
Such end-to-end solutions promise customers the best of both worlds – the speed and convenience of digital banking, with the tailoring and conversational element of relationship-based banking.
“This combination has not quite been achieved before,” Morgan added. “What previously took hours can now be done in minutes.”
3. Responsibility
As banks get more comfortable with AI, they will be able to gather more information, faster. However, they need to balance speed and convenience with trust and security.
In the past, AI has lacked transparency around how its decisions are made – leading to accusations of discrimination. Responsible AI will be able to reveal how certain decisions were reached, in easy-to-understand language; such as how a credit score was
generated and what impact that had on the approval process.
“At Temenos, we believe in responsible, customer-centric AI that empowers banks to innovate on their own terms,” explained Morgan. “We’re helping banks to unlock the future of financial services – where data-driven insights meet unparalleled performance
and customer-centric innovation.”
The importance of cloud and SaaS
AI feeds on data, which is often siloed, rendering it uncollated or unusable. By leveraging cloud and software-as-a-service (SaaS) banks can access the agility and scalability necessary to take advantage of the models. The combination allows banks to transform
data into real-time insights, while retaining full control over their information.
Though SaaS is primarily a delivery mechanism, it also plays a critical role in delivering functionality that everyone can then leverage, such as continuous updates, customer-focused features, and scalability, without banks needing to manage the underlying
infrastructure.
“As financial institutions face mounting regulatory demands and pressure to innovate from their own customers, they need a platform that can scale with them, adapt quickly, and support growth,” Morgan commented. “Financial institutions need modern architectures
built to support any deployment environment, be they on-premise, cloud, or hybrid. This gives organisations full control over their tech strategy.”
Morgan believes that “together, AI-powered, cloud-delivered technology means banks can attract more customers, retain them, cross-sell, and ultimately, become the central point of end-users’ financial lives.”
The state of play
Already, banks are beginning to deploy GenAI in very specific areas across the front, middle and back-office. For example,
Commonwealth Bank is now using GenAI chatbots to emulate customer behavior and test products and services with specific personas;
Morgan Stanley has built a GenAI tool to assist relationship managers during client conversations; and
Wells Fargo is using GenAI advice for goal setting and planning within the wealth space.
“The journey of the industry to SaaS has been long. We have been in this process from the beginning, driving and enabling our clients to be able to adopt cloud, as and when it made sense to them. Not all banking players are ready to go to SaaS and cloud
today, but our clients want choice, and that’s what we have been aiming to provide,” noted Morgan. “At a time when new technologies have become so transformative and mission-critical to banking, it’s imperative financial institutions embrace them as they continuously
evolve and deliver value for customers.”
In summary, GenAI and cloud innovations enable banks to stay laser-focused on their business and customers – leaving the security, resilience and technology operations to the specialists.
Where next?
As technologies like Gen AI continue to be developed and rolled out, how rapidly will the banking value chain change?
Agentic AI is poised to be the next frontier, leveraging autonomous decision-making, collaboration, and learning, to revolutionise financial services. Agentic AI could evolve client interactions, play a key role in fraud detection, and supercharge productivity.
No matter what the next big AI innovation will be, the bottom line for Morgan is “building a culture where customer-centricity and innovation are part of the financial services DNA.”
Clearly, the shockwaves of this technology will continue being felt for some time, so financial institutions must surf them.