As the financial services sector increasingly becomes digital, banks and financial institutions are turning to automation to enhance the quality, speed, and security of their software systems, and the tools that test them.
Automation in software testing, once seen as an additional luxury, is rapidly evolving into a strategic necessity for organisations that are working to remain competitive in the fast-paced, tech-driven ecosystem they are operating in.
Particularly since the rise of mobile banking, cloud computing, and AI-powered software systems, the need for rapid and accurate testing solutions has gone through the roof.
Automation in software testing is helping firms to demands by streamlining processes, ensuring possibly the highest levels of quality control they have ever had, while at the same time cutting down on the risks associated with manual testing.
However, the digital transformation that most banks, insurance firms and financial services firms are going through right may indeed create many new opportunities but also brings with it some complex challenges, which can not be ignored, particularly as AI is increasingly taking centre stage within many QA teams.
Speed, efficiency and accuracy
The push for faster software development and quicker rollouts of new features is a key driver of automation adoption.
Financial institutions are facing mounting pressure to release new products and services to stay competitive and meet customer expectations.
Traditional manual testing, while effective, can be too slow to keep up with this rapid pace, especially when multiple updates and iterations are required in short timeframes.
Automation, on the other hand, does allow testing to be completed far more efficiently, enabling banks to execute hundreds of tests across different environments, ensuring quality control without sacrificing speed.
Moreover, automated testing tools can run tests continuously and across a variety of systems, devices, and platforms, such as app, while simulating real-world scenarios that ensure that software behaves as expected and wanted, under a host of different and diverse conditions.
William Dulude, the chief executive officer of HappyPath, said software testing has become an “impediment” in product development as applications grow increasingly complex.
“Traditional testing methods are expensive, time-consuming, and limited in scope — often forcing companies to choose between thorough testing and rapid innovation,” he pointed out.
“Traditional testing methods often force companies to choose between thorough testing and rapid innovation.”
– William Dulude
Moreover, in an industry where accuracy is crucial, automation provides the consistency needed to detect bugs early, reduce human error, and ensure thorough test coverage.
For financial institutions, this means they can release software updates or new features faster, all while maintaining high standards of quality assurance.
Regression testing, in particular, has been revolutionised by automation. Banks and financial institutions often face the challenge of ensuring that updates and new releases don’t break existing functionality.
Automated regression testing helps mitigate this risk by automatically re-testing previously validated features every time new code is introduced. This continuous cycle of validation ensures that software is robust, functional, and ready for deployment with minimal disruptions.
AI and ML
The role of artificial intelligence (AI) and machine learning (ML) in software testing is growing, adding an extra layer of intelligence to the automation process.
With the help of AI, automated testing tools can not only execute predefined tests but also learn and adapt to changes in the software over time. This enables the system to intelligently identify areas that are more prone to defects, ensuring that testing is more targeted and efficient.
AI-powered QA automation tools can certainly boost testing output and performances, but they are not, yet, equipped to respond to unexpected real-world scenarios, argues Victor Ionascu, who is the research & development director at French IT infrastructure firm Axway, headquartered in Paris.
Ionascu, who is based in Bucharest, Romania, sees that QA professionals are increasingly turning to AI to address the growing complexities of software testing.
And for good reason. “AI-driven automation can improve test coverage, reduce test cycle times, and enhance the accuracy of results, leading to faster software releases with higher quality,” he said.
Machine learning algorithms can analyse large datasets of historical test results to predict where future issues are likely to occur.
This predictive capability allows financial institutions to be proactive in addressing potential vulnerabilities before they manifest in the final product. By recommending specific areas to test based on past trends, AI-driven testing can optimize the focus of testing efforts, saving valuable time and resources.
AI and ML are also making it possible for automated testing systems to adjust to changes in code without human intervention. This is especially important for complex systems that undergo frequent updates.
Rather than requiring manual updates to test scripts each time code changes, AI can autonomously adjust test scripts, ensuring continued coverage without the need for constant oversight.
Challenges
While the benefits of automation in software testing are clear, the implementation process does come with challenges. Financial institutions need to invest in the right tools, frameworks, and skilled personnel to properly integrate automated testing into their workflows.
Adam Carmi, a co-founder and the CTO of software testing giant Applitools, recently demonstrated the capabilities of autonomous visual testing with a live demo at the QA Financial Forum London, which took place in the British capital last month.
It showed how Applitools’ technology can be used to detect faults and streamline test maintenance.
“Facing an exponential growth in complexity and scale in the applications they manage, software teams need a new way to test,” Adam Carmi, a co-founder and the CTO of software testing giant Applitools, shared at the 2024 QA Financial Forum London, held in October last year in the British capital.
“Most test automation projects fail to scale,” he said.
Autonomous testing, however, can change the game. “You can attain near complete test coverage. Autonomous testing lowers the skill-set required and it drastically reduces human involvement,” he explained.
“Aside from freeing your team from the complexities of code, these capabilities mean reduced risk, improved delivery velocity, and superior digital experiences that align with consumer expectations and business goals,” Carmi shared.
Many organisations also face the challenge of adapting automation tools to legacy systems, which may require additional customisation.
This is often time-consuming and resource-intensive, especially in firms where legacy applications are deeply embedded into daily operations.
Also, automation testing, while valuable, often struggles with the time needed to develop robust tests and the limited ability to adapt to real-world, dynamic scenarios, Ionascu pointed out.
“The challenge of automated testing is not just about setting up automation frameworks or creating scripts, but also about maintaining and evolving them as the system grows.”
“Despite its value, test automation is not a silver bullet for dynamic and complex systems,” he said.
Looking ahead
Despite these hurdles, the long-term rewards are clear, most in the QA space would agree.
Automation not only enhances the speed and efficiency of software testing but also reduces the cost of manual testing in the long run.
As the technology matures, it’s expected that automation will become an even more integral part of the software development lifecycle, with banks and financial institutions benefiting from even more sophisticated tools and smarter testing techniques.
In fact, as financial institutions continue to innovate, the role of automation in software testing will only grow.
By embracing automation, banks can reduce time to market, improve the quality and security of their applications, and ensure that they remain compliant with the ever-evolving regulatory landscape.
More importantly, it allows financial institutions to focus on the core of their business—delivering innovative products and services that meet the evolving needs of customers.
With automation leading the way, many industry experts agree that the financial sector is on the verge of a new era where the development and delivery of software can keep pace with the rapid changes in technology, customer expectations, and market demands.
Most QA industry insiders largely agree that, for banks and financial institutions, adopting advanced automation practices in software testing is no longer just a strategic advantage, it has become a critical necessity in an increasingly competitive and complex digital landscape.
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