If you are engaged in development in the field of financial technology, then you probably pay a lot of attention to the security and stability of software products. Comprehensive software testing in this area is an integral element at all stages of the development life cycle, which helps to take care of customers and their confidential data.
According to the World Quality Report (WQR) 2020-2021, about 66% of banks test all the functionality of a digital solution, because QA allows you to take a leading position in the market. But the main thing is to keep them. And since safety and reliability are a key element here, our advice will lie precisely in the plane of financial software testing.
It is impossible to achieve the absolute quality of a software product, but to get as close as possible to it is real. The foundation of an effective QA strategy will be the knowledge of the weighty points and their application in financial software development.
- Do not cut test coverage to speed up QA processes.
In order to get to the market faster, companies often cut development costs, test costs, or completely abandon QA. As practice shows, this approach brings short-term benefits and negatively affects the quality of the code.
To ensure the stable and reliable operation of the software, it is worth checking all the functionality of the fintech product, their interconnections and various integrations with third-party systems. In the opposite scenario, critical defects that block the operation of the application after release remain unnoticed.
In addition to performance testing, security has recently become an important place, because the software used in banks and insurance companies is most vulnerable to attacks by cybercriminals.
The increased number of online transactions due to the pandemic has given hackers more opportunities to illegally obtain confidential data of users and organizations. Some banks have noted a 4.5-fold increase in cyberattacks compared to 2019. And information leakage due to criminal activity has become the second most urgent threat to representatives of the financial sector. This was noted by 71% of respondents according to the results of the 2021 study “Protection against DDoS attacks in banks”, so you should not give up security testing.
- Avoid False Optimization When Automating Testing
Implementing autotests is a proven method of shortening the pre-release period. But without the necessary experience and a precise strategy for the QA team, test automation can become an improvisation.
For example, changes in functionality can slow down QA processes even with the maximum volume of autotests, because the check algorithms need to be constantly revised, like the autotest code. In turn, the lack of automation will also not save time, because the effectiveness of manual testing, while maintaining the size of the team, will remain at the same level or will decrease.
- Pay proper attention to UX
Although portable devices have become an integral part of users’ lives, some companies neglect mobile testing and only evaluate the quality of the web versions. Imagine a CEO of a large chain of stores logs into an app to send a money transfer, but an non-intuitive interface will slow down the transaction. This will lead to a decrease in his loyalty as a customer.
A common mistake of mobile checks is to use only emulators and simulators instead of real devices. Such imitation will not allow identifying all critical software defects.
- Implement Test Automation Smart
The optimal solution is a balanced approach to the implementation of automated testing for functionality that is regularly tested by testers, but does not change dramatically over several weeks.
This speeds up QA processes and helps identify bugs faster than manual testing. To get the most value, companies are embedding artificial intelligence, machine learning, and other innovations in test automation. At the start, this will require additional effort, but this combination looks promising in terms of results.
According to statistics, more than 50% of representatives of the financial sector resort to a data analytics consulting company. This approach turns out very effective in the long run.
