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.

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:

Machine learning (ML) adoption is growing tremendously over the last years, as it is enabling us to leverage the power of machines for a wide variety of applications and use cases.

In terms of technology, ML enables software applications to become more accurate in predicting outcomes, without the need to be explicitly programmed. In addition, it is changing completely the way teams are operating. While in the past monitoring and analytics teams were approached with requests for new dashboards and asked to analyze trending issues and behaviors, they are now able to scan and analyse all of the data an organization collects, and identify any issues or bottlenecks before they really become a crisis.

Climate change is one of the biggest challenges that our world is facing and as the planet continues to warm, its impacts are worsening. The catastrophic events that occur because of the weather are now triple the number that occurred 30 years ago. The predictions for the near future, are not positive: By 2100 half of the species will face extinction and it’s likely that average global temperatures will be 3˚C higher than in pre-industrial times.