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.

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.

Essentials for creating a high-quality test data automation strategy

In the midst of the growing digital demands of customers, organizations are feeling more pressure than ever to modernize their practices and automate their business processes in order to keep up with the competition.

Artificial Intelligence augments testing to be zero-touch and autonomous

While DevOps and “continuous everywhere” transformation requires the ability to assess risks with every “sprint”, with speed and quality, testing on the other side has become the bottleneck to taking full advantage of more Agile methods to software development.

Let’s see what are the key differences between predictive and prescriptive analytics:

Traditionally requirements have been managed in flat requirements documents or management software which represent requirements as text. This makes it hard to have a holistic view of the requirements and their associations, and see the size, scale and complexity of a project. In most cases the result delivered often looked very different from what the business had envisioned.

With application environments becoming more complex, integrating a robust messaging platform into your QA and DevOps lifecycle, will accelerate software development, testing, delivery and operations. It offers teams the opportunity to get real-time decisions on every stage, from development to release, based on live comparison and insights. This way collaboration regarding the overall project plan and the overall project efficiency is getting improved and the integration testing phase of your projects is simplified and de-risked with less effort.

With DevOps and Continuous Delivery, releasing with both speed and confidence, requires having immediate feedback on the business risks associated with a software release candidate.