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The 7 transformational technology trends to revolutionize the financial sector in the post-pandemic era

The 7 transformational technology trends to revolutionize the financial sector in the post-pandemic era 3

The 7 transformational technology trends to revolutionize the financial sector in the post-pandemic era 4By Sam Barton, Group CTO, Smart

Sam Barton is Group CTO at Smart, the global retirement technology provider backed by J.P. Morgan, LGIM, and others. Sam describes the 7 transformational trends with the power to boost the financial sector forward to meet the new needs of Americans.

Just three short months into the new decade, the world as we knew it changed so significantly that we all had to reinvent our daily lives as working and learning from home became an international responsibility. Throughout this unprecedented period, rank-and-file technology workers became the unsung heroes who kept the lights on for the financial services industry.

For years, the industry was happy to maintain on-premises hardware and the systems to support them, but a sudden loss of staff in offices created a point of inflection in how we interact with the systems that support our work. 2021 kicked off these initiatives in earnest, but it’s clear there is more work to be done. So, what is in store for 2022 and beyond?

1 – Process automation vs people processes

The industry has long been moving to paperless processes or screen-scraping programs that pull data from discordant systems. It’s also not uncommon to employ third party workflow tools that add automation as a veneer over the systems that can’t be automated. In any case, the end result is still people-powered. To be more resilient and truly leverage the cloud-based solution, these journeys need to be entirely automated, including when it comes to leveraging customer self-service opportunities to minimize business processes.

Automation brings huge cost savings as computers work 24/7, are less prone to error, and don’t need paid holidays. Still, process automation is often overlooked in the transformation project, as the target of achieving a new cloud-based solution is more visible as a deliverable. However, in order to leverage new real-time data and scalable solutions, automated processes need to replace overnight batch jobs, retired on the journey into the cloud.

Similarly, as an organization builds a cloud-based infrastructure, new deployment pipelines are needed. Instead of a team provisioning a new server and promoting a pre-production environment to production, the same process is now written as code (Infrastructure as Code) using templates that define the environment variables, the build, and even operating system of the instance the software will be installed on. Instances are created and destroyed in minutes all via automated deployment processes (CI/CD) that have built-in tests to report on their performance.

As the business embarks on this transformation, it must shine a light down every tunnel and ensure each department meets the new target architecture.

2 – Business intelligence vs artificial intelligence

All businesses rely on data to monitor various performance metrics. These business intelligence reports have always existed in one form or another, and became “Big Data” as information moved away from paper and became more digital. What’s new is how this data is mined and managed.

Artificially generating those reports will be a big focus as the transformation projects near completion. As more data is captured,  BI reports also need to accelerate and the old ways of inspecting data need to be reinvented. For example, it is understood that fraud is best caught by machine learning tools that flag values that fall out of a defined tolerance. Moving that defined tolerance to one that is re-evaluated in real time by a more reactive and ML based decision tree (AI) is a necessary milestone.

3 – Security by design

Each step on the transformation journey offers a potential point of ingress and egress for customer data or important financial information. Just like the BI point, this data is now moving at a faster rate, meaning automation is needed to scan for vulnerabilities and detect issues in real time, which includes building in automated, real-time threat detection. We’re not going to do away with those annual penetration tests, but we are going to add more defenses, meaning that the next penetration test has less to report on.

This is as much a software problem as it is a cultural one, as the mindset of software engineering needs to adapt to suit the new architecture. The test suite in the Continuous Integration server needs to include security tests and the Continuous Deployment pipeline should also be augmented to scan for vulnerabilities. At the same time, those writing the code need new development guides that spell out best practices, and the quality engineers testing the feature need to inspect for security weaknesses while ensuring the feature meets its intended business requirements.

Security by design is not a single policy, but a cultural shift that sees a business unit taking ownership of the security of its products and services.

4 – Hybrid-cloud vs cloud native

To stay relevant to a customer base demanding a more engaging digital experience, businesses have invested heavily in cloud-powered technologies. But as these budgets were being approved, no one thought to also transform the business processes and culture of the people who support the legacy systems, creating a pair of competing policy interests pulling in opposite directions. The inevitable compromise was to agree on a hybrid-cloud model. The legacy administration products stayed on-premise and a new cloud-based digital experience layer was introduced. This made logical sense in a pre-pandemic era. But just as working from home became mandatory for the employee, the idea of a fully cloud-native solution soon became mandatory for the business.

2021 was the year to accept this revelation, and 2022 will see the death knell of the on-premise solution. Businesses will go fully cloud-native not only to protect themselves from customer migration, but also reduce internal productivity losses and build better disaster recovery solutions.

5 – Serverless vs microservices

Going fully cloud-native is not a turnkey activity. The initial attempt to simply move the on-premise software into a cloud-based equivalent machine will soon reveal incompatibilities. The legacy software was not designed to leverage cloud-native hardware.

Lambda Functions, a product offered by AWS, enable the quick introduction of features that don’t require complex infrastructure to support them, making them much more agile in both delivery and in the procurement and approval processes. There is simply less to review by the business and technology staff. It’s an efficient means for the business to achieve the first foothold into a cloud-native solution and one expected to see greater adoption once the precedent has been set.

The concept of a Service Oriented Architecture (SOA) is not new and in fact Microservices are considered contemporary in the financial industry today. But if the Service is written in a way that depends on an on premise solution, its performance is locked to the limitations of that machine. By moving the same feature to a Serverless solution, one where the infrastructure scales seamlessly behind it, the small (micro) Service evolves to become Serverless and the business can justifiably say it has achieved its first milestone on its digital transformation roadmap.

6 – Cloud data vs legacy policy documents

By their nature, startups disrupt established markets. Project leads overseeing Digital Transformation for established businesses share this aspiration. While it is often easy to agree on  technical strategy, the hardest part of a transformation project is less visible at project kick-off. Just like legacy software, business operations are hardcoded into policy documents and have shaped the very DNA of the business. Long-held practices can shape everything from business processes to workplace culture. The overnight lockdown of 2020 led to a strategic assessment of many of these policies, as operations teams had to suddenly access customer data while working from home. But as we accept this new norm, the policy bandage has yet to be taken off.

So as we introduce new technology, it is equally important to challenge documents that don’t suit cloud-based solutions.

The end goal is to move customer data away from on-premise hardware and into a scalable cloud-based solution. Software and data can finally be reunited in a real-time solution and those overnight batch jobs finally disappear, signaling another major milestone on the transformation roadmap.

7 – Responsibility and sustainability

The financial services industry is no stranger to sustainability. Customers want to ensure their money is invested responsibly, and ESG funds are the mainstay of any wealth product today. But while that external accreditation has been met, the new focus is on the footprint the businesses themselves leave behind.

Original on-premise solutions were stood up in data centers designed to meet the demands of the busiest day of the year. But today’s cloud-based architecture can intelligently scale up on demand as customers rush to use the product online, and scale down again in quieter hours. using less electricity. This simple example saves money while also contributing towards the sustainability report for the business. The same logic applies across the business. Offices are aware of when someone is working from home and turn off their workstation and associated office lights.


The aforementioned topics collectively add up to a necessary to-do list for any business that has withstood the challenges of lockdown and seeks a better set of defenses for the future. Achieving these goals brings business benefits while meeting the demands of today’s customers. This serves as a vital evolutionary change as the world adapts to a post-pandemic era.

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