Vishal Bhatia, Chief Digital Officer, Canara Bank, in an exclusive interview with CIOTechOutlook, shares his views on how organizations leverage smart technologies while ensuring robust data security, overcoming legacy system limitations, defining clear KPIs and use feedback loops for enhance personalized customer journeys and more. He has over 24 years of experience in Digital, Technology and Fintechs roles.

Maintaining consistency across digital and physical channels is difficult due to data silos and varied interfaces. How can companies integrate data and streamline processes to ensure seamless, cohesive experiences across all customer touchpoints?

The entire core system is available on the central part, and a data warehouse where all the data is designed, the same data is available to the branches, call centers, and chatbots. Hence, the bank is leveraging the CRM solution to deliver a view of the customers there. The bank can also prioritize customers from various channels such as devices, mobile applications, and net banking, or the customer physically goes to the branch or ATM. There are several other offers the bank can provide, which are also propagated from the AI engine, and departments such as sales, service and marketing are centralized with data being propagated to all channels so that the bank can ensure the central repository of data is available and utilized consistently, across all the omnichannel way to the customers.

Personalization at scale involves heavy data use, raising concerns about privacy and security. How can organizations leverage smart technologies while ensuring robust data security and compliance with privacy regulations?

Being a regulatory body, the bank ensures that the data is safe and secure, privacy is taken care of, and the consent framework is built before the customer data is utilized. The other point is that banks don't share any data outside the data center, which resides at the bank premises. From a bank's perspective, it is also essential to ensure that anonymous data or encrypted data is being used to get insights into customer behavior and personalization through AI technology and implement it using the anonymized data based on the available data patterns. Besides, it has a robust governance framework where multiple levels of approval occur for the models being built, how the data is utilized, and proper audit mechanisms.

Real-time data integration is complex, particularly when dealing with legacy systems. How can companies overcome legacy system limitations and integrate real-time data for dynamic, personalized customer experiences?

Over the last five years, the bank has invested a lot in technology and ensured the core data analytic hub set up for which many engineers are working. The approach is helping the bank to ensure all the legacy data is available in real-time in the data warehouse, and this is being utilized in all the channels, branches, touchpoints and from the data lake, which is helping to ensure the same consistency and personalization effort is being taken. Hence, there is no need to rely on legacy systems. Real-time integration is happening from the legacy to the core data lake and data warehouse. From there, the rules and multiple models were implemented. Then, it goes to the channels, devices, and branches, which have been used as a wrapper, or overlaying layer, to give a personalized offer to the customers.

Measuring personalization success requires tracking complex KPIs and continuous data analysis. How can businesses define clear KPIs and use feedback loops to continuously enhance personalized customer journeys?

The focal point is on how the business revenue has increased. With the investments made by the bank, there is an incremental growth of 8-11% on asset and liability, and many new customers have been acquired and can be measured. Based on the personalization, what journey has the customer taken, whether it reached the final stage, and it is helping the bank to validate the accuracy of the model, then perform the process engineering, where the gaps are.

The clear mandate helps increase revenue, reduce costs, and detect fraud. Hence, these are how numbers can be quantified, which helps achieve the targets, and KPIs can be measured.  Furthermore, it is crucial to take the customer feedback to measure the NPS score and ensure that the NPS score is available on the particular survey or the transaction between the customer and the customer service department. And the feedback from the customer helps the bank to deal with them more effectively. Hence, this approach can measure business targets based on customers' views of the bank's products and services, further improving the processes.

Cross-functional collaboration between departments with differing goals can impede seamless customer experiences. How can companies foster cross-functional collaboration to align teams in delivering smart, seamless customer journeys?

The bank has taken from each user wing what sort of use cases they want to look into, predominantly on the AI part. These have been governed by senior management, which reviews them, puts the use cases on the pilot, and measures them. Once the pilot starts working, the bank witnesses how the performance is improving. Hence, the AI part and the business collaboration help the bank ensure that success is granted in the implementing models. Besides, wherever the gaps that have not been picked up in the market are available, various processes should be improved. However, there is no guarantee that it will succeed with a cent percent; the critical point is the feedback and experimentation being taken care of.