As financial services institutions ramp up their investment in Generative AI, they face challenges in realising its benefits. It is uncontroversial to advance the proposition that GenAI offers the potential to drive innovation, enhance data accuracy, streamline compliance processes and provide predictive analytics.
Douglas Blakey speaks with Dror Avrilingi, VP and Head of the QE and Data & GenAI Studios at Amdocs
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These benefits will empower financial institutions to make better decisions, improve operational efficiency, and offer more personalised customer experiences. In short, transform banking as we know it.
But according to Dror Avrilingi, VP and Head of the QE and Data & GenAI Studios at Amdocs, the success of AI is dependent on one critical factor: quality engineering.
He goes further.
“For the sake of the entire enterprise, you have to put quality engineering at the beginning of the strategy of implementing AI, not only at the end of it.
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By GlobalDataHe tells RBI that financial institutions that make quality engineering (QE) central to their AI strategies will surge ahead in the race to enjoy a return on their investment on GenAI.
“Quality engineering is all about building trust into a technology from the start. It’s a mindset and a discipline that ensures every part of the digital experience and the software development life cycle works smoothly, securely and responsibly.
Taking banking: we want to make sure that every ATM, app and chat bot works exactly as it should before any customer even touches it.
“When you need to test the data, you need to test the decision-making logic. You need to make sure that it behaves correctly, especially for real world cases. Basically without quality engineering, without testing, you are making decisions that might be biased especially when dealing with regulatory issues.
Near-term wins
Amdocs, a global leader in digital transformation working with financial institutions to engineer and deploy essential elements for next-generation financial services, has done its sums and reveals estimates as to the potential returns. Specifically, it reports that financial institutions leveraging the power of automated QE workflows are seeing near-term wins. These include a 50% improvement in test coverage optimisation, a 60% decrease in testing certification time, and 33% decrease in testing design time.
The adaptation to AI is still in its early stages. Avrilingi stresses that it is critical for financial institutions to start on their GenAI roadmap with an effective quality strategy.
It means integrating QE into GenAI processes from the start, with continuous testing that leverages GenAI. In the first phase, machine learning helps automate testing at a speed that matches AI-powered development. It also improves precision in test selection, boosting overall efficiency.
FSIs that incorporate real-world AI value into QE workflows now will be better positioned to deploy market-leading, customer-facing use cases in the future. It starts by adopting a maturity model for GenAI QE and targeting baseline maturity as soon as possible.
“AI is pushing everyone one level up. Developers are not only just coding. The line between a developer and a tester, is actually blurring and the responsibility remains with both of them.
A lot of organisations today are treating AI as a science experiment, because they want to explore. They want to explore AI and what it can offer. First of all, our top advice, treat it as a product. Don’t treat it as a science experiment.”
He says that banks looking to integrate AI into their ecosystem must ensure that it has the appropriate level of trust from all stakeholders.
Amdocs USP
To give practical examples, GenAI has the potential to deliver the high-touch, personalised experience typical of private banking to every retail banking customer. AI assistants will help loan applicants select and apply for loans faster. Mass market segment customers will be able to use natural language to learn more about the pluses and minuses of different retirement-saving strategies. Amdocs traditionally excelled in the telecommunications sector. It is now leveraging its experience in highly regulated industries, especially in financial services.
“We are helping banks to modernise all the aspects of their business, from the data to cloud to quality engineering to customer experience.
If you think about it, the banking sector has been held back by legacy systems. We are helping those institutions to overcome those constraints and modernise the technologies, modernise the databases. Amdocs’ data-driven approach integrates insights back into banks’ core systems, creating a value-led cloud journey that optimises costs and maximises returns.
Banks want to accelerate product development. They want to enhance customer engagement. We’ve done this in telecommunications and we’re doing this now in financial services.”
