Can you imagine being able to mine every bit of value possible from your datasets? If not, it’s because the privacy fears and data leaks risks put decision makers in financial sector in a fake dilemma: Protecting the privacy of your customers and keeping their sensitive data secure or getting agile through privacy innovation? Is there a way to get the best of both sides without sacrificing on privacy and security?

With the adoption and spread of digital technology, software testing helps different organizations stay competitive and shows exponential growth during the last four years. While not every institution will be impacted equally by fast-changing technology, IT undoubtedly plays a significant role in all industries. In this article we emphasize in the eight most important tech trends that banks and financial services organisations will continue to experience within 2020:

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.

SWIFT operates a messaging service for financial messages, such as letters of credit, payments, and securities transactions, between member banks and financial services institutions worldwide. Although such formats have proven to be extremely efficient for electronic data exchange, they are typically difficult for humans to understand, manipulate, and validate.

When testing end-to-end transactions that leverage such message formats, sending a single inbound transaction and validating the result can be both difficult and error-prone, and involves complex test suites that leverage large data sets and perform multilayer validation on the outbound transactions.

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.

One of the key challenges in deploying DevOps is test data management, as in DevOps the need for data grows significantly. With more and more Temenos core banking clients turning to Continuous Delivery and DevOps practices, rigorous and agile testing is essential to releasing quality software. To do this you need quality data, and in most cases project delays occur because testers lack access to the data they need, when they need it.

Test Data Management (TDM) has always been a critical part of the application development and testing life cycle, however its importance or having the right data delivered to the right people, at the right time, for testing purposes is overlooked. Poor test case design and the inefficient provisioning of poor quality data means that test teams find themselves without the data required to fully test a system. Quality is compromised in favour of delivering software on time and within budget.

As Testers, we need trustworthy data! Data is often the root cause of testing issues; we don’t always have the data we need, which causes blocked test cases, and defects get returned as “data issues.”

Testers usually spend up to 50% of their time looking for data, and as much as 20% of the total Software Development lifecycle is spent waiting for it. The need for effective test data management is obvious as data keeps growing and taking more time and money in order to be under control.

An estimated of 1 billion people still use Excel spreadsheets for their reporting. However, in the new age of on-demand business software and the need for a collaborative and always connected business setup, there are areas in which spreadsheets are limiting as your business intelligence tool.