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  1. Analysis
July 5, 2019

Artificial Intelligence: is ramping it up a smart move?

By Patrick Brusnahan

Temenos is a true believer in artificial intelligence (AI), and while the concept is hardly new to anyone in the industry, its evolution is accelerating on a daily basis. Briony Richter speaks to chief architect and AI head Prema Varadhan and Temenos Technology VP Sriraman Ganesan about the firm’s efforts

Software and technology firm Temenos has launched its AI Centre for Excellence.

Announced at the end of 2018, the Centre of Excellence will foster a collaborative ecosystem with Temenos’s banking clients and partners. It aims to focus on the following:

  • Front office: Temenos will help banks to embed AI to deliver improved customer experiences, greater personalisation and a seamless onboarding process;
  • Payments exemption handling: Temenos will use AI to identify payments going into exception and monitor users’ manual corrections. It will learn how to automate these payments, improving straight-through processing rates and lowering costs, and
  • Real-time fraud detection: Detecting fraud in real time is critical for banks. With AI, banks can use a vast amount of customer data and transactions to flag activities outside behavioural patterns that could potentially be fraudulent.


CI sat down with two leading figures in the initiative to see how the project is coming along and what it hopes to achieve.

CI: can you briefly describe your roles at Temenos?

Sriraman Ganesan: In my current role I work with Prema at the Centre of Excellence. As part of the Centre of Excellence we have a suite of operations that we have built. I receive the requirements, I have a team of people who ‘solutionise’ it, and at the end of day we make it ready to release to clients.

Prema Varadhan: I head the AI strategy at Temenos, and I also have another role which is chief product architect. In that role we make sure, with a group of architects, that all the products are joined up. Apart from the AI role, I interact with customers and work with our research and strategy teams to ensure that we are staying ahead of emerging trends.

One of those areas is what AI is part of, and a few years ago we started looking deeper into AI. We realised that AI is something we wanted to embed into our banking products.

CI: What have been the main developments at the Centre of Excellence?

PV: The first thing is around thought leadership. Now, this is thought leadership in terms of understanding where the applications of AI are in the banking industry. We talk to a number of leading customers who spend a lot on innovation and validate the use cases. The other thing that we are involved in is helping our banks understand how to set up their shop, particularly around AI. We share our experiences and give them the knowledge to use AI solutions themselves.

The Centre for Excellence is not just about building products and use cases; it’s about understanding trends like AI.

CI: What are the key challenges that banks are facing with AI?

PV: The first one is around the data aspect. If AI is the rocket, data is the fuel. If they don’t have the right data to feed through the AI algorithm, then the AI will simply not be accurate enough.

They need to solve their data architecture first, and define how to use that data. Data is so fragmented now, so banks have to think about collecting it and blending it to make it clean enough and that it is reliable. That’s one challenge.

The second is an industry-wide challenge around lack of clarity about how the regulators are viewing AI. If we look at loans being completed by AI, if a machine is taking a decision to reject or accept a loan, you need to know, as a regulator, that this is how you would audit that machine. That includes explaining the decision the machine has made.

Why is that difficult? What happens over a period of time is that the machines will start learning on their own as they get exposed to more data. They become smarter, and if you don’t know what data is exposed to the machine, and you can’t explain it, then a problem arises.

Banks need to be able to give the regulator reasons why the machines have rejected or accepted certain applications. This has been a massive challenge, but there has been a lot of research done by us to look for solutions and platforms to solve this problem.

SG: We are constantly working on integrating the AI solutions within the banking software products. We are not trying to build standalone AI solutions, but trying to make them work for our customers. That is the sub-challenge of those two main challenges that Prema has explained.

CI: What use cases are being worked on right now?

PV: There is particular use case within the financial crime area. Financial crime is so vast and so complicated, and all banks have a serious need to fix their problems in real time.

The real-time element is a major challenge for banks. What has happened in the last five years is this area has been dominated by fintechs, which apply AI very successfully in this problem solving.

The way fraud happens, the patterns keep changing every single time. There are new patterns going undetected, and the only way to solve this problem is to not look for the patterns that hackers use: it is to put it the other way around and try to find your customer behaviour. Anything that falls out of normal behaviour, class that as a fault.

With the behaviour information, the AI can track those patterns. You can then start classifying the behaviour of customers.

We are taking it one step further and doing a real-time anomaly detection. As and when the transaction comes, we do the patterns and can tell if it needs to be rejected.

CI: Is there still a need for the human touch when working with AI?

PV: Yes, there are some use cases that can be fully automated, and there are a few that a bank may want to have humans involved.

An example of this is credit scoring. Banks need to make sure that they are accepting the right loans, and not just turning them away because the machine has maybe rejected some. AI can do so much more than other technologies.

We are able to do all the hard lifting of getting the AI algorithm ready, and that gives our customers the confidence that they don’t need to do everything from scratch. We don’t give them toolkits: we give them solutions that are reliable and ready.

SG: We have the capability to integrate AI into this domain that differentiates us from the rest. The integration is what we bring to the table, as well as the banking software.

CI: What’s next?

PV: We have a very big pipeline, and a very strong one. We have our Temenos Infinity line that we launched last year. That basically takes care of all the distribution services for a bank, including marketing, sales and servicing. That is a really exciting space that AI sits in.

You need hyper-personalisation in distribution services. As a customer, you want to be treated properly by a bank. This kind of personalisation is not possible with traditional software engineering methods.

The machine can understand the behaviour of each customer, and then we can start recommending tailored services. This is something that we are actively working on.

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