Validata Blog: Talk AI-powered Testing

Without a fast and trusted way to detect change impacts, upgrading your Temenos application or optimizing processes can expose the business to significant risks. There are still a lot of banks relying on manual testing or traditional testing frameworks to verify and validate changes in their critical business processes.

Traditional test automation tools have achieved little in terms of automation, keeping automation levels below 25% even after years of software testing. These tools were built more than a decade ago – some even more- to cover a limited set of technologies and the Waterfall Methodology, and are ineffective to meet the business demands of faster application delivery and faster time-to-market. In addition, businesses, are increasingly adopting Agile and Continuous Delivery, which introduces a new set of challenges.

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.

On May 2019, Deutsche Bank found out that its software, which is meant to Detect Money Laundering activity, had a bug that prevents a program from retroactively scanning and analyzing corporate payments to flag potentially suspicious transactions and identify patterns that can be notified to regulators. The AML software program “was configured erroneously with two out of 121 parameters defined incorrectly,” the financial institution said in a statement. This was not the first time Deutsche Bank has been repeatedly fined for failing to fight money laundering as well as to comply with sanctions against foreign countries and entities.