While institutions for financial services are increasing their investment in generative AI, they face challenges in realizing their benefits. It is not -contact to promote the proposition that Genai offers the potential to stimulate innovation, to improve data accuracy, to streamline compliance processes and offer predictive analyzes.
These benefits will enable financial institutions to make better decisions, improve operational efficiency and offer more personalized 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 depends on one critical factor: quality technology.
He continues.
“Because of the entire company, you have to place quality technology at the start of the strategy to implement AI, not just at the end of it.
He tells RBI that financial institutions that make quality engineering (QE) that are central to their AI strategies will be ahead in the race to enjoy a return on their investment on Genai.
“Quality Engineering is entirely about building trust in a technology from the beginning. It is a mentality and a discipline that ensures each part of the digital experience and the life cycle of software development works smoothly, safely and responsibly.
Taking banking: We want to ensure that every ATM, App and Chatbot works exactly as it should be before a customer even touches it.
“If you need to test the data, you must test the decision -making logic. You must ensure that it behaves correctly, especially for cases in the real world. In principle, without quality engineering, without testing, you make decisions that may be biased, especially when dealing with regulatory issues.
AMDOCS, a world leader in digital transformation that cooperates with financial institutions to engineer and implement essential elements for financial services of the next generation, has done its amounts and reveals estimates about the potential return. In particular, it reports that financial institutions that use the power of automated QE-Workflows see victories in the short term. These include an improvement of 50% in the optimization of the test coverage, a 60% decrease in the test certification time and a 33% decrease in the test time.
The adjustment to AI is still in the early stages. Adrillingi emphasizes that it is crucial for financial institutions to start their Genai roadmap with an effective quality strategy.
It means that QE is integrated into genai processes from the start, with continuous tests that Genai use. In the first phase, Machine helps to automate testing testing at a speed that corresponds to AI-driven development. It also improves the precision in test selection, which increases overall efficiency.
FSIs who now include Real-World AI value in QE-Workflows will now be better positioned to implement market-oriented use cases in the future. It starts with taking on a maturity model for Genai QE and to focus on baseline maturity as quickly as possible.
“AI pushes everyone one level higher. Developers not alone. Not only. The line between a developer and a tester is actually blurred and the responsibility remains with both.
Many organizations nowadays treat AI as a scientific experiment because they want to explore. They want to explore AI and what it can offer. First of all, our best advice treats it as a product. Do not treat it as a scientific experiment. “
He says that banks want to integrate AI into their ecosystem, must ensure that it has the right level of trust from all stakeholders.
To give practical examples, Genai has the potential to deliver the high -quality, personalized experience that is typical of private banking to every customer of retail banking. AI assistants help select applicant applicants and request loans faster. Mass market segment customers can use natural language to find out more about the plus points and minuses of various pension -saving strategies. Amdocs traditionally excelled in the telecommunications sector. It now uses his experience in highly regulated industries, especially in financial services.
“We help banks to modernize all aspects of their business, from data to cloud to quality engineering to customer experience.
If you think about it, the banking sector has been stopped by Legacy Systems. We help those institutions to overcome those limitations and to modernize the technologies, to modernize the databases. The data -driven approach of AMDocs integrates insights back into the core systems of Banks, creating a cloud trip with a assigned cloud trip that optimizes the costs and maximizes returns.
Banks want to accelerate product development. They want to improve customer involvement. We have done this in telecommunications and we are now doing this in financial services. “
“Amdocs” Dror Avrilingi about connecting AI with Quality Engineering “was originally made and published by Retail Banker International, a Global Data brand.
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