Enhancing the quality of banking technology platforms through a hybrid AI testing approach

In the dynamic landscape of the banking sector, marked by increased operational complexity and regulatory scrutiny, the pursuit of innovation demands a strategic orientation. While offering advantages in cost, quality and speed, this strategic approach to innovation should mandate a cautious consideration of the risks inherent in the integration of emerging technologies. With the continuous advancement of artificial intelligence (AI), particularly generative AI (GenAI), as a method for emerging banking technologies, its wider adoption in the financial industry demands careful consideration of associated risk implications. This paper advocates for a software testing approach that delves into the complexities of banking technology platforms by leveraging generative algorithms to attain extensive test coverage and simultaneously employs rule-based analytics to refine the generated datasets, optimising coverage for faster execution and efficient resource utilisation. Such an approach is in line with a risk-averse innovation strategy, as it balances out the smart creativity of novel GenAI methods with a rule-based discriminative potential of more established symbolic AI methods. This paper serves as a position paper, contributing to the discourse on the integration of innovative testing methodologies in the banking domain.