Glitches in a bank's software system can pose significant challenges, disrupting the daily lives of customers. In the banking sector, the real-time testing of complex software is crucial but challenging, given the rapidly changing requirements that must align with customer expectations. Despite allocating substantial resources to enhance security against external threats, there is often an underestimation of the importance of thoroughly assessing the quality of the code itself. In 2023, software malfunctions garnered considerable attention, repeatedly putting banks in the headlines. Below are some notable software failures from the year:

Have you ever been left worrying if your payment will go through without any issues?

Software testing is currently undergoing a big transformation. We are seeing a major move towards using Artificial Intelligence (AI) to automate the design and validation of tests while significantly reducing reliance on human effort. With AI in the mix, banks and financial organizations can generate and execute tests autonomously, refining its capabilities through human guidance and input. As a result, we are on the brink of having access to virtual testing workforces for every development team.

The quest for fast, dependable test data poses numerous hurdles for application development teams, ranging from the speed and quality of data movement to concerns about security and costs throughout the software development lifecycle (SDLC). Below, we outline the most prevalent challenges organizations encounter in managing test data.

In the ever-evolving world of automation, a groundbreaking concept has emerged: composable automation. Despite its recent emergence, composable automation holds tremendous potential to revolutionize the testing process, enabling testers to enhance efficiency and maximize reusability. In this article, we will delve into the essence of composable automation, with a specific focus on its application in test automation. Additionally, we will explore the multitude of benefits it offers, paving the way for a more streamlined and agile testing approach.

In the realm of AI testing, the concept of explainability holds immense importance. While the capability of AI in making decisions and reshaping how organizations operate by revolutionizing processes free from the errors and prejudices of human workers, the main prerequisite for its success is being able to understand and trust those decisions. That means there is a need in all industries for transparency, interpretability and explainability AI in order to avoid a future built on flawed and exclusive insights.

The banking industry has seen significant growth in the demand for test automation in 2022, driven by the need to support digital transformation. Going into 2023, banks continue to seek new ways to improve their efficiency and resilience. In this context, automating software testing will become increasingly important to achieving these goals.

Software glitches in a bank’s software system can be one of the most troublesome issues that turn customers’ everyday life into a challenge. In banking, testing complex software in real-time is vital yet tricky as requirements change too rapidly to meet customer expectations. While organizations do spend a lot of resources to accelerate security from external threats, checking the actual quality of the code is being underestimated. Software malfunctions put banks in the headlines many times during 2022. Here are some of this year’s software failures:

According to Gartner by the end of 2024, 75% of organizations will shift from piloting to operationalizing AI. It may sounds like a risky plan for companies to adopt but in reality they translate technology advantage into competitive advantage within their industry. A plan that goes beyond post-COVID-19 world and can overcome unpredictable market shifts requires an ever-increasing velocity and scale of analysis in terms of processing and access. Embracing technological innovation is no longer just an option. Customer expectations and demand for new digital services are already increasing at a rapid pace.

Banks and Financial institutions need to deliver better quality software to the business faster and at less cost. No bank can afford the time, the cost or the risk of employing an army of manual testers. They need to be able to respond to changing requirements by provisioning fit for purpose test data to the right place at the right time to accelerate and improve test cycles.