Embracing Complexity And Uncertainty: The STAGE Approach For AI-Powered Banking

Originally published in . Link.

North American banks are currently going through two different transformations. The first is a digital transformation with a focus on big data and the customer journey; the second is one that embraces the cloud as a strategic platform for financial innovation and technological transformation.

Sustainable architecture patterns, governance models and engineering capabilities will help accelerate business growth and enhance the customer experience based on the maturity and stage of the financial institution.

The Covid-19 pandemic introduced unprecedented uncertainty and pushed banks over the ascending triangle of digital transformation toward AI-powered banking, whereby changes of regulatory compliance are accelerating and machine learning and robotic automation become core competencies in the accelerated journey.

The STAGE Approach Toward AI-Powered Banking

STAGE stands for strategy, technology, architecture, governance and enablement. It is a framework for business and technology strategy our company developed. There are five key components:

1. Finance fabric

2. Security by design

3. Cloud-native microservices

4. Continuous banking intelligence

5. Self-service analytics

Here is an explanation of each component as well as an overview of how the framework works.

Finance Fabric

For simplicity, “fabric” can be perceived as a sort of time and space that forms the fabric we live in, and in which all objects interact with each other. Finance fabric is where banking data and applications live and interact with each other. The analogy targets the force and context where business and technology align and data and application originate and continuously connect, change and then are aggregated, transformed and archived with cloud engineering.

It starts by truly democratizing the management and governance of data from centralized data lakes (predominantly on-prem) to distributed data lakes (mostly in the cloud). This data should coexist with private clouds in addition to on-prem runtime platforms such as Windows, Linux, Unix, AS400 and mainframe servers.

Security By Design

Systemic risks take strategic change to rectify. Solutions can be trusted by first securing the data to preserve the integrity and ensure safety. Tactical solutions are not ideal. Security by design is a key feature needed to build federated data solutions on hybrid cloud platforms that run distributed cloud-native applications.

In order to achieve security, different design patterns can be deployed. One such pattern is a data plane where security and controls are enforced when data between clouds or between clouds and on-prem runtimes is exchanged.

Another important architecture pattern is the front door pattern to divide and conquer security management by virtualized data domains. These domains are built upon vertical domains (e.g., product data, account data and transactional data).

Cloud-Native Microservices

Microservice architecture, tailored to run in finance fabric, will include native hooks to integrate with the runtime and enable services to become cloud-native. It functions by integrating a long list of design patterns; however, it is designed to channel artificial intelligence that is enabled by machine learning and robotic process automation in order to capitalize on data federated and accessible via the finance fabric.

Continuous Banking Intelligence

Continuous banking intelligence is a feature tailored to leverage the finance fabric to provide real-time intelligence as a piece of data becomes available across the entire landscape of a banking application. This should not overwhelm banks if most of their data is still dispersed in silos or, at best, in data lakes. This can be implemented for banks where the finance fabric is in full swing across hybrid-Clouds, with data federated into respective domains but accessible via data streaming pipelines and safe-guarded via data planes. All of this, of course, is with the expectation that cloud-native microservice architecture becomes mainstream and machine learning is the norm.

In other words, cloud technology has transformed the banking model in terms of product offering, process flow, and customer journey to such a degree that it is at a pivotal point where real-time intelligence is just a step away.

Self-Service Analytics

This is a simpler version of the continuous intelligence provision that targets citizen coders who can capitalize on the power of AI, machine learning and RPA for his/her day-to-day business operations. Akin to self-service reporting tools, this is a tool for intelligence-based analytics. However, it is not simply a tool but rather a toolbox, built to integrate with other features enumerated thus far.

Conclusion

We examined the current risks and challenges banks are facing and how cloud technology can help via the STAGE approach. Keep in mind that this strategy can be used in alignment with the current business and technology landscape. The major features of this framework will be explicated in subsequent articles.