Global regulatory requirements and constantly increasing transaction volumes demand greater operational efficiency and risk management. Financial institutions need efficient Reconciliation and Investigation capabilities to reduce financial risk. These capabilities help identify payment errors, discrepancies, or irregularities early in the payment process.

In the changing world of finance, it's crucial to keep updating and innovating to stay competitive and meet customer needs, increasing compliance costs and intensified competition are driving institutions to explore innovative solutions.

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.

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.

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: