The surge in mobile transactions and shift to remote operations along with the need to rapidly scale to meet the evolving customer needs have accelerated the digital transformation across financial institutions. The payments landscape has progressed with speed towards real-time contactless payments while cross-border payments adopt the new message standard of ISO 20022. All of these have driven the institutions to rethink their underlying technology infrastructure to meet their future needs.

Pillay said, “We have seen an increased uptake in our software as a service (SaaS) solution based on cloud deployment and managed services. Clients have been trying to expedite how they can monetise the data and achieve better analytics by utilising technologies in artificial intelligence (AI) and machine learning to improve customer service”.

Pillay added banks have increasingly used AI to generate stronger customer insights for a better user experience. It identifies gaps and streamlines processes such as reconciliations and exception management, as well as operations automation. The company’s focus in 2022 is on AI, machine learning, cloud computing and moving clients into a cloud-first platform. It enables a more seamless operations team that can be deployed remotely, while bringing a better user experience.

The following key points were discussed:

• SaaS solution and managed services witnessed an increased uptake during the pandemic
• Utilising AI to monetise the growing volume of data and streamline the transaction life cycle management process
• Companies will shift focus towards utilising AI, cloud and distributed ledger technology

Below is the transcript of the interview:

Neeti Aggarwal (NA): The financial industry is seeing a rapid digital acceleration. What are the most important emerging technology trends in financial services in the last 12 months?

SaaS solution and managed services witnessed increased uptake during the pandemic

Radha Pillay (RP): It’s an interesting time right now in the financial services industry. What we’re seeing is there has been a massive increase in the volume of transactions that are being processed – the number of new players and products that have been pushed into the market to meet the changing and increasing customer demands out there. We’re seeing more prospects and clients who are pushing towards more volume and managed services. It’s essentially having the third-party suppliers to host platforms and use a cloud-first approach to provide the services for their teams.

SmartStream has been doing this for a number of years. We’ve seen an uptake in our SaaS solution, which is based on cloud deployment and managed services where we offload some of the work that were traditionally done by a financial services operations team. These services are seeing a lot more uptake.

With the volume of data, clients have been trying to expedite not just the cloud technologies, but how they can monetise that data. How they can go through the data, have better analytics in the data by utilising technologies in artificial intelligence (AI) or machine learning to improve the services they provide to the customers. The way they do that is through a better understanding of their customers’ behaviour to better predict how customers are going to spend, and what sort of products would be of interest to them going forward. But how can they better automate their operations to get rid of any bottlenecks so they can handle these volumes without having to expand the teams massively? AI and machine learning provide a strong use case in financial services institutions that we are working with. We have been involved with these organisations in implementing some specific use cases to better streamline the operations, or the whole transaction lifecycle management process. We provide a data lake powered by AI and machine learning to give our clients a better understanding of their data, and the capability to potentially monetise their data as well.

NA: You touched upon two key areas – one is cloud and the second is data analytics. You talked about cloud-first, as a technology that banks are adopting. When it comes to challenger banks, yes, they can be cloud-native, and they are digital. But when we talk about traditional banks, they have a whole set of legacy technologies behind them. How can they equip themselves not only to compete with the new players, which are cloud-first or cloud-native, but to bring their technology on par with the legacy systems? How can they address their concerns on data security, privacy and regulatory concerns?

RP: That’s a very good point. You’re right. Banks had legacy solutions and moving those to the cloud has a much more challenging journey. That is one of the key reasons why banks are not trying to be a developer of specialised IT products. They’re pushing that off to the vendors that specialise, enable and provide that transition from the legacy platforms onto more cloud-native technologies such as the ones that we provide. We’ve had a number of use cases where we are moving the platform related to reconciliations or corporations’ processing. These are some of the areas that we cover. We’re moving it away from a bank’s on-premise infrastructure onto our cloud and SaaS. This then offloads a lot of the challenges the banks have. The way that it can be done is to leverage and utilise the expertise of the vendors that have been previewed, come up and create the solutions to be the ones to manage the solutions, rather than try to bring all these disparate technology platforms internally into their own infrastructure.

NA:  What are your views on data security and data privacy? Not all regulators have been equally forthcoming with regard to cloud adoption.

RP: It’s important that as a vendor providing SaaS or cloud-based solutions, we need to be at the forefront of understanding the regulations. We need to be at the forefront of ensuring that we have the right level of certifications such as the Service Organisation Control (SOC) 1, SOC 2, SOC 3, International Organization for Standardization (ISO) certifications, and Payment Card Industry Data Security Standard (PCI DSS) certifications. All these are necessary and we’re doing these on a regular basis. Having biannual audits and providing audit reports are almost like a standard requirement that we need to provide to all the financial services institutions. We are doing it not just for one, but for multiple institutions that bring about the economies of scale. It creates certain utilities in specific operational areas. If the bank were to do the same thing for themselves, it will cost them a lot more, than if they were to leverage a vendor.

NA: The other theme is around real-time payments and cross-border payments. The banks are looking to monetise their data better. Payments bring them a huge amount of data and now with the new messaging standard ISO 20022, there will be more granular data with the banks. How should banks build their technology around the data that they have, to be able to utilise it better?

Strong use case for utilising AI to monetise growing volume of data and streamline transaction life cycle management process

RP: The payment space has been going through rapid transformation, not just in the region but globally. In real-time payments, open banking had a large impact on all the payment participants. With the latest ISO 20022 standards that came about, the innovations will continue to happen in the issuing and acquiring space where contactless payments have picked up. We’ll see a lot more organisations looking for solutions to manage a large volume of data in a limited time frame. It’s not just a front-office solution that can deliver that, but an end-to-end solution that includes your operations and back-office, which can be as frictionless as straight-through processing and able to scale and provide a seamless experience. We’re doing this by utilising technologies such as AI and machine learning embedded within our solutions to provide a greater level of automation.

NA: What are some of the key areas and use cases wherein banks implement AI and ML technology more proactively? On the use of AI and ML towards cybersecurity and operational resilience, are banks focusing on that, and how can they use it towards that?

RP: We have a client, a large local bank in Singapore, it’s a regional bank. They needed to have automation in the reconciliations and exception management for digital payments. Digital payments are payments coming through various channels such as mobile apps, web portals, or even ATMs, credit cards, and so forth. They need to have a granular understanding of the transactions that are flowing through all these channels to be able to validate those transactions, cross-check them against the records of their various systems internally and do this in a timely fashion.

SmartStream has a digital payments control solution brought about by this automation. We have AI embedded within the solution. A specific use case, for example, is rather than a team of people doing analysis of data to understand how to reconcile or match information together, we are able to leverage AI, by looking at historical data to model how certain transactions should or could match together. This reduces the time required to define rules for people to manually bring transactions together, or to manually define rules to bring transactions together to complete the reconciliation process. That’s one of the use cases where we’ve seen a good uptake in leveraging AI and ML.

Another use case is a provider of one of the super apps in the region. The goal was to assist them in setting up better automation and providing operations automation and control solution that is required by the audit. It was more like a finance transformation project. But this app is being used for booking a taxi, for deliveries, making payments, and in various events that occur in a transaction’s lifecycle. It uses our digital payments control solution and enables them to automate those various events. You could be looking at up to 60 million events that occur in a day. To be able to automate all of that, you need to have the proper controls in place and checks, without having to expand the operations team to manually handle this.

These are the two key use cases that are worth mentioning. We are seeing more organisations looking for similar solutions that can automate the operation’s workflow and that include the entire transaction lifecycle, from inception all the way to its completion. This automation cannot be enabled just by having deterministic rules defined in the workflow. We need to embed some AI and machine learning capabilities because over time, as the type of product changes, the transactions change. Machine learning and AI help us keep the automation levels as high as they were when we initially implement them.

NA: What are your expectations for 2022 especially around technology trends, geopolitical landscape and overall? What are the directions that you see? How are banks building their technology going forward?

Institutions will focus on utilising the cloud, AI and distributed ledger technology

RP: We’ve been working very closely with our research and development teams, our teams on the ground, and with our clients. We’ve completed an exercise, which includes a three-year planning to see what are the types of products and services that the market and the industry were asking for. We’ve seen that the financial services industry is currently going through a lot of changes and transformations and it’s one of the most disruptive industries right now.

We are working with a number of clients on some strategic initiatives that are being undertaken around AI and machine learning and distributed ledger technology. Institutions are looking to get a better understanding of the value of such technologies and what they can provide. Rather than have a sort of broad application, we’re looking at specific use cases which are applicable to the areas where we’ve been providing solutions.

I mentioned the reconciliations and exception management use cases. There is a whole area around exception management workflows. Within the financial services industry, with the volume of transactions, whether they are trading transactions, for example, as breaks occur over time, the operations team or the business users are spending a lot of time to resolve these breaks. But in many cases, they are repeatedly resolving similar sorts of breaks and exceptions. It is another area where we use AI to try and predict why a break, or whether a break can occur at an early stage of the transaction’s lifecycle, and provide a pre-emptive resolution process to it. This reduces the time to resolve breaks as well.

The other area that we are seeing a lot of change is the user experience (UX). We are more used to the kind of simplified and easy to use and access user interfaces (UI) across all the apps that we use in our day-to-day life. That’s expected from solutions such as ours which were previously purely used by a middle office and back office operations team. But they are expecting the UI experience to be simplified, to be more intuitive. There’s a lot of effort done into our solutions to bring it to the standards and expectations that our clients and prospects were asking for. AI, machine learning, cloud computing, moving our clients into a cloud-first platform, and therefore enabling them to have a more seamless operations team that can be deployed remotely, and across various different centres, and bringing about much better UX and UI across all our solutions.

We’ll be focusing on increasing levels of control and automation across business operations, irrespective of the business process involved so that the firms can deploy their staff and better utilise the information they get from their data to monetise the data, and provide value-added services to their end-clients.