Traditional banks have a wealth of data from decades of operations, but their outdated data systems keep this information in silos, limiting AI’s impact – especially on hyper-personalized customer experience.
Businesses in South-east Asia (SEA) have already embraced AI, automation, and data-driven insights to enhance customer experience (CX).
According to SoftServe’s latest research, 80% of APAC leaders agree that their company allocates funds towards the latest GenAI trends at the expense of more valuable data and analytics initiatives.
As major banks in the region ramp up AI integration, it becomes more crucial than ever that traditional banks re-examine their priorities and set the right data foundation to ensure success in leveraging the power of AI to improve performance and productivity at the backend, while improving customer experience at the frontend.
We find out how this may be achieved in a Q&A with Wells Vaughan, APAC Chief Technology Officer, SoftServe.
What are some specific developments in the digital transformation in banking that may create blind spots that spell danger to banks in their digital transformation journey?
Wells Vaughan (WV): In the past decade, APAC’s banking sector has undergone a digital transformation, driven by mobile banking, AI, and blockchain technologies. While these innovations enhance customer experiences and streamline operations, they can also create blind spots by sidelining the importance of solid data management. For instance, mobile banking apps often store data in isolation, leading to fragmented customer information that disrupts seamless experiences.
This pattern is repeating with the rise of AI in banking. Despite 78% of APAC leaders acknowledging the need for a complete overhaul of their data strategies, many banks push ahead with AI initiatives without ensuring their data infrastructures are capable of supporting them.
Additionally, 68% also agree that leaders in their organizations do not fully understand how to generate value from data, which negatively impacts their investment priorities. This results in crucial data projects being overlooked in favor of Gen AI initiatives that depend on those very infrastructures to succeed.
According to SoftServe’s latest research, 80% of APAC leaders agree that their company allocates funds towards the latest GenAI trends at the expense of more valuable data and analytics initiatives. What’s your perspective on this?
WV: The fact that 80% of APAC leaders report prioritizing the latest GenAI trends over critical data and analytics initiatives is indeed troubling. While GenAI offers significant potential, overlooking foundational data management undermines its effectiveness.
Consider AI-driven customer service chatbots: while these tools can automate processes and personalize experiences at scale, they’re only as good as the data they’re trained on. Fragmented or outdated data can lead to skewed results, damaging the customer experience and eroding trust. Data maturity—the ability to manage, utilize, and extract value from data—is essential for successful AI implementation. A balanced approach is critical; leaders must first modernize and integrate their data ecosystems
