Saturday,25 May 2024

Temenos’ Kam Chana: “AI is recreating intelligent customer engagement and driving learning revolution”

5 min read

Interviewed By Neeti Aggarwal

Kam Chana, product innovation director at Temenos, shares her perspective on evolving financial technology trends, how banks need to rethink personalisation and customer engagement, as well as bring efficiency in their business models

  • The pandemic presented challenges to banks in engagement with customers and how to bring greater efficiency in current business models
  • Convergence of cloud, software as a service (SaaS) with AI is making technology more lightweight and scalable for banks 
  • There is increasing focus on  SaaS delivery and explainable AI 

The COVID-19 pandemic has highlighted the gaps in digital journey for banks. It also accelerated the need to rethink technology strategy as well as identify models that can drive customer values. Chana, former digital strategy programme director at Barclays, is a specialist on humanising banking and currently leads the product innovation at Temenos, a global banking software company. Chana shares her views on emerging key financial technology trends. She discussed how banks can innovate and drive efficiency through technology while better utilising data analytics towards hyper personalisation in their customer engagement.

Below is the edited transcript of the interview:


Neeti Aggarwal (NA): How do you see recent developments impacting financial institutions especially the way they have adapted to the trends in technology? What do you see as the key emerging priorities for institutions this year?


The pandemic presented challenges to banks


Kam Chana (KC): The pandemic has been really interesting because it has highlighted what needed to happen for a long time and it really acted as a catalyst to the adoption of technology to enable people to get access to banking. And they should always have been there. But it wasn't because for so many banks for so long, the priority has been gradual transformation, and softly testing the waters, and moving over to digital gently, but really running an old business model. And the business model was people will always walk into branches. That traditional business model has fundamentally been rewritten by the pandemic where people are just no longer comfortable walking into branches. Yet banks have this massive portfolio of real estate that they don't know quite what to do. The pandemic has forced the adoption of technology to become digital, to become mobile, not necessarily mobile first, but to really adopt mobile. 


But at the same time, it has really raised the question of what do we do with branches? And what is the banking model? How do people want to engage with their banks? And how can the banks help? The one question it has raised is the question of technology. How do you become hyper efficient in the way that we deliver a business model that has been radically disrupted by a pandemic? On the other hand, it raised another question, which is there is a situation globally where there is a lot of uncertainty and anxiety around financial well-being. That has resulted in people losing their jobs, people being furloughed, people being unsure whether they're going to have be able to continue to live in their rented accommodation, or in their homes, or with their families. At the same time, you have small businesses that are concerned about how much cash runway they have. So it has created a lot of uncertainty among many different business lines and consumer groups. 

The advantage that it has presented to banks is the advantage to provide better understanding and better help with that anxiety - whether you're an individual or whether you're a business. And where that is drawing the attention of a lot of banks is with data. For us, we are seeing this convergence of the application of digital technology, but also the application of data technology. So everything that comes with data, which includes the infrastructure to harness it, to mine the data, to analyse the data to present insights, and then to use those insights to make recommendations on how individuals whether they are consumers or businesses, can help themselves going forward. Now this is where we're seeing a lot of innovation as well. 

As a banking industry, we see a lot of businesses working and a lot of businesses not working. How can we harness and leverage our collective knowledge to help small businesses actually develop a better business proposition to survive? And that was fascinating. That was a global event that we ran. It was 100% virtual over the space of a week where we brought in real SMEs, brought in the banks, run design thinking and agile under hackathon, and came up with these great ideas on how we can apply our technology to really help in the COVID pandemic.


NA: What do you see as the biggest hurdle for this rapid digitisation and what has been holding them back in the past?


Convergence of cloud, software as a service (SaaS) with AI


KC: I would say potentially not necessarily the maturity of the technology but the convergence of the technology. We're now in a situation where we have many technologies such as cloud and the ability to consume technology through a SaaS model. We have the ability to harmonise and gather data, the ability to run technology in an efficient way to drive value out of that data, that was never there before. And we've got to a point now where technology has made it much more affordable and made it much faster. So one of the biggest challenges we have been seeing is affordability and speed. We're now in a situation where the convergence of these technologies with cloud, SaaS, AI, and with all the digital technologies drive that engagement and learning. Digital distribution and interface have become much more lightweight with the adoption of cloud mostly and the scalability and agility of a SaaS model.

You can now, as a bank of seven or eight people, as a fintech, create a new bank. 


We have an example at the moment with one of our new banks that within a space of five months you can be up and running and onboarding customers. The technology has made it faster and more efficient. And at the same time, you're able to deliver that value very quickly. 


I think the other aspect of this is the arrival of the fintechs. And we can see globally, that regulation, in many ways, has helped, but so has open banking. The barriers for fintechs and banks to enter the banking industry are lower than they have ever been. And the adoption of a hyper efficient technology model has helped a lot of fintechs to come in. And not only challenge from a cost management perspective, but to challenge from a value addition perspective. 


A great example of that is one of our new clients, Flowe bank. They have changed the banking model entirely. They are almost playing like an aggregator, but also a traditional bank. And what actually enabled that was the adoption of technology. I think that is key without the adoption of cloud and SaaS, and all the accompanying technologies, they would never have got to market in the five months that it took them to get to market. So that speed is now possible. That hyper efficient business model is now possible. That hyper personalisation that is really driving the humanity in banking is now possible. 


NA: Traditionally the banks were kind of cautious when they were shifting their mission critical systems like core banking onto clouds, probably because of data or security concerns. What trend do you see in terms of bigger banks and their adoption of clouds in technology?


KC: The sort of security aspects of it. And the lack of maturity of cloud for some years was a key concern of many banks particularly in tier one. I think we have passed that point now, globally. A lot of banks have adopted cloud, many different cloud models. It has become normal for banks to adopt cloud technology to enable their efficient business models and their low cost to serve. I think what we're seeing more interested in now is adopting as quickly as possible, and adopting it with a SaaS model as well, so that they cannot only create a lightweight technology, infrastructure and service model, but also scale it as quickly as they grow their customer base. So that acquisition model is now aligned directly to their technology growth model


NA: What are you seeing happening in the financial institution where they are applying AI and ML, and how you are responding to that need?


Investing in SaaS delivery and explainable AI to support financial institutions


KC: There are a number of areas where the technology is being applied, particularly around that whole gamut of data from harnessing it, to reporting on it, using it to drive insight and therefore recommendations. That whole piece, in our mind, has kicked off a new revolution of what digital was about. For a long time digital was about moving your cost basis to a low-cost digital channel. It was all about channel shift. And in many ways it was about lower value of service than a high value of services. It was like we will bring the high value people into the branch, and we will roll out the red carpet. And we will move all of our low cost people and low value people to mobile and digital channels. 


I have been working in digital a long time. I remember sitting in those meetings where it's like, “Let's take this low value segment and move them online because we don't need to worry about them”. 

What that resulted in was partly success and partly failure because with the success of moving everyone online, you actually lose that engagement with them. Now they have all gone online, and you can no longer have that conversation. They are all self-serving, and they are all doing what they need to do. We heard this story again and again that now how do we engage them?


With the arrival of open banking and the ability for other organisations to harness your data, they can very easily take the conversation with your client to their bank without actually taking the client. 

So how do you then use technology to create or recreate that engagement model through a digital channel? And that is where data analytics and AI have come in along with xAI, to create what we call intelligent customer engagement.

And that means through the digital channels you are moving away from just a digital model, where you are just distributing things through an interface but you are now creating value using your datasets, your analytics, and your machine learning models. And then you're potentially even exposing the drivers of those machine learning models to users and your customers so that they can better understand what it is they are trying to do with their money, whether they're a consumer or a business. We are calling that the learning revolution. And that learning revolution is kicking off a trend in digital banking which we see as one that is driven by algorithms and is potentially autonomous. Now this is almost moving into the kind of the self-driving banking world because with the arrival of machine learning and using machine learning to learn from what consumers are trying to do, what they are actually doing so to almost describe what they are doing, diagnose why that's happening, to then predict what could happen, and then to ultimately prescribe what should happen. This gives a lot of control back to the user.

It educates them and it empowers them. And this is where we see the real advantage.


We have been talking about personalisation for many years but personalisation used to be this person has just come into some cash, let's promote them, let's market a mortgage to them. But personalisation has moved on from that.  

We are now in a situation where the kind of hyper-personalisation is possible but also hyper-understanding and hyper-empathy, where you can almost run a model of one where you understand what one individually is trying to do and then you can start to serve content and start to serve a model that serves that individual in what they're trying to do with their goals.

And that takes that relationship with data from a reflective point of view with looking backwards at analytics. What did happen but also predict what could happen. And key to that is providing control back to the user. 

One of the key fears around the use of artificial intelligence is that devolvement of control, losing the control. We have all watched the social dilemma and we are all concerned about how big tech have taken our data and essentially we become the product. 

What we hope for technology and for banks is that they use the technology to provide control back and empowerment back to their customers using their data rather than the other way around. That is where we see a really big opportunity for digital to become now the catalyst that drives the learning revolution, the empathy revolution, and ultimately delivering a model where control goes back to the end user. 



NA: Thank you so much. 

Keywords: Sibos 2020
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