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The move to cloud-native banking and the role for continuous intelligence

According to research by Finastra and Efma, banks saw digital transformation as their most pressing issue for the future. 81 percent listed the move to digital as their biggest priority for the next one to three years, while 66 percent listed adopting new technologies as their next big project. Around 56 percent listed innovation as where they needed to put the most effort.

To achieve these goals, you may have been looking at the role that the cloud can play. From being of limited interest a decade ago, cloud is now a default way to deploy applications and services. However, this approach has replicated banks’ previous applications and deployments from their traditional deployments to the cloud. Today, the opportunity is how you can harness “cloud-native” applications to achieve your goals around decision making in real time.

Cloud-native describes how applications, infrastructure and microservices can take full advantage of the cloud. Using this approach, you can build, test and deploy new products that can personalise services to customers in real-time and in ways that would not otherwise be possible with those more traditional or legacy infrastructures. These new services can lead to agility, efficiency and performance improvements over time - according to Capgemini, going to cloud-native led to improved agility for 88 percent of banks surveyed, while 84 percent stated this move led to reduced operating costs and increases in revenue.

However, the move to cloud-native is not as simple as just replicating or shifting existing systems into the cloud. Cloud-native involves understanding the applications and underlying platforms, and using these new approaches to get insights faster, more economically and at scale. If you are at a challenger banks, implementing cloud-native systems is easier as you can start from scratch. If you are at a more traditional bank, the move to cloud-native is harder as you have plenty of existing IT resources and process- both hardware and applications - that were not built in the cloud. Integrating all these services together can be a costly, complex and painful process.

The continuous tsunami of real-time data leads to continuous intelligence

Part of the problem here is that these deployments both create more data and work in different ways. New applications tend to be developed using containers, microservices and shared responsibility models, where each component is highly separated from each other and only connects through Application Programming Interfaces (API).

These microservice architectures enable more agility and efficiency in your approach to building modern, reliable, scalable applications. This allows your components to be managed and updated more easily, which means the overall business service can continue running rather than needing downtime for the complete system. Alongside this, today’s modern applications emit more and more signals and distributed telemetry such as logs, metrics and traces. This data needs to be unified and  continuously observed in real-time, at scale. 

This data exists across multiple application components and boundaries. Collecting, ingesting and unifying all this data is a complex and costly task, but it is now essential to secure this data while also making it available and actionable for teams throughout your software delivery optimization process 

With all this data generated continuously from your applications and platforms, you have a constant stream of information coming through. This data can be used for different purposes across banks - for example, software development teams can use this data to see where their applications can be improved over time, while the bank security department can check that all the security rules are being followed. Interestingly, this data can also be used for business purposes - because the vast majority of your customer interactions now take place digitally, this data can show how customers react to different scenarios and updates.

This process is called continuous intelligence. Gartner defines this as how companies can use real-time analytics around their data and integrate these results into business operations, prescribing actions for individuals in response to what is taking place. Without continuous intelligence, the move to cloud native will take longer and require more support from developers and IT operations, who work in silos. 

In the banking sector, the move to cloud-native will carry on as banks modernise their systems and harness their data more efficiently. To keep this process continuous, you will have to consider how all this data is gathered in real time, but also how this data can support decision processes and recommendations in real time too.


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This post is from a series of posts in the group:

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