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.

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.

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.

In today’s digital era, banks and financial institutions need to implement continuous intelligent testing solutions to succeed in their digital transformation initiatives, mobile technology integration and modernisation of their core systems.

With Validata Sense.ai we have extended our Analytics functionality so clients are now able to centrally analyse and monitor their testing efforts and progress leveraging AI and machine learning.

by Vaios Vaitsis, Founder & CEO at Validata Group   To win, serve and retain customers, testing activities are becoming more pivotal as business process quality is deemed more important than time to market. Beginning with the business-driven change request, through to project scoping and on to DevOps adoption, the infrastructure for technical and regression testing must be present and aligned to every phase.

Getting Quality Performance right is key to the success of every area of the QA business. Organisations need to see results across projects, departments and geographies  in order to make faster and more informed decisions.