How Quality Engineering Can Help Financial Institutions Get the Most Out of GenAI
If you work in financial services, you’re probably wondering about all the ways generative AI (GenAI) can transform not only your firm but the whole sector itself.
Imagine agentic agents creating customized retirement plans that fully understand each customer’s needs and financial constraints. AI producing diverse, anonymized data sets that don’t breach privacy regulations. Or interpreting regulatory changes on the fly, then validating the compliance of your company’s processes automatically.
This AI-infused dream is fast becoming a reality. AI is already creating a lot of the code banks are using to connect with customers in ways they’ve never been able to before. And applications powered by AI are starting to drive more banking-related activities and open up more markets for new, creative services. In many ways, FS organizations’ futures are dependent on how effectively they can employ GenAI to sharpen their competitive edge.
For GenAI to work, quality engineering (QE) has to play a major role – in every industry, and especially in a heavily regulated, customer centric field like financial services. GenAI simply can’t fail. There’s too much riding on it.
Financial services leaders are facing some critical issues as they embark on their GenAI journeys. Stakeholders in business and IT – including CIOs, Chief Digital Officers and Chief AI Officers – are investing time, energy and resources in developing aggressive GenAI strategies, so they want to make sure those strategies are delivering to their full potential. These same stakeholders also need to ensure that customers trust that the organization is using GenAI correctly and appropriately, and that the outputs are accurate. This all starts with a commitment to QEFS leaders are excited about the potential GenAI has in store. To fully unlock this potential, they need to develop well thought out quality strategies and commit to the necessary investments. This approach is essential to avoid the pitfalls seen in early cloud and DevOps transformations, where many enterprises found that the lack of a comprehensive quality strategy limited the ROI these programs had achieved.
At the same time, leaders of QE functions inside financial organizations are facing questions of their own. With so much depending on the success of GenAI, organizations need to take their quality practices to a higher level. If QE fails, GenAI fails, and the organization itself will suffer the impacts. Are QE leaders ready for what’s ahead?
As the QE function assumes more responsibility, quality engineers’ roles and their mandates will change. While the transition will require some work, the effort will be well worth it.
In a new eBook, “Driving GenAI success in financial institutions through quality engineering,” we offer a roadmap for organizations looking to get the most out of their AI strategies. We discuss how GenAI is accelerating the evolution of developers’ and quality engineers’ roles, and what that means for organizations’ futures. We look at GenAI’s impact on QE, and vice versa. And we highlight how FS organizations can successfully inject GenAI into QE.
To start, organizations can follow a maturity model we’ve developed to guide their increased use of GenAI in QE. Organizations can expect to move from a baseline level, where GenAI functions more as a knowledge base to higher levels where GenAI eventually becomes a fully autonomous partner in the testing process.
By implementing strategies such as LLM optimization, organizations can use QE to actually expand their business possibilities. Putting QE on the case to continuously evaluate, tune and optimize LLM models improves business offerings and makes them more robust. Constant training refines an application so expertly that a bank should soon be able to create a customized agentic agent for each customer that the customer can relate to and trust while also being aligned to the bank’s brand.
Then, to scale and accelerate these transformation efforts effectively, organizations must bring order to the process. This could be facilitated through bringing into the organization an emerging role that oversees quality at the C-level. By overseeing GenAI’s integration into QE, a Chief Quality Officer could ensure operational excellence while aligning innovation with broader business objectives. This role becomes the architect of trust, ensuring systems are reliable, secure, and prepared for future challenges.
GenAI has changed the business landscape in a short time, but its impacts will evolve over time. Organizations have an opportunity to get ahead of the AI trend – to ensure that quality engineering provides the foundation GenAI processes and applications need to deliver their full value in years ahead.
