By Ramanujam Komanduri
Banks have been the early adopters of technology across the world to ensure exceptional customer experience. From a customer experience point of view, ATMs were the first real foray for most people into digital banking. They sped up the service and introduced the cash-on-the-go concept, thereby improving the customer experience. For the banks themselves, mobile applications and ATMs greatly reduced the cost of doing business. We have come a long way since then!
However, there is a lot of legacy attached to the Indian banking industry. As banks evolve their businesses, they are focusing on creating separate analytical practices such as credit risk analytics, operations analytics and compliance analytics. This evolution led to the need for these siloed practices to communicate with each other. Often, these silos study similar data structures to extricate revenue opportunities and risks. With the set-up of integrated banking analytics – addressing of institution wide challenges, facilitation of actionable insights and implementation of corresponding technological solutions will be more effective and efficient.
Data-centric Banking is the Future
As banks transform themselves to keep up with dynamic customer requests, Data-centric banking is the future. The financial sector is moving quickly to incorporate a data-driven approach into their business and using data to mitigate risk, manage fraud, process mortgages and ensure customer delight.
For this to happen, banks and financial institutes are working to ensure that the deluge of data is quickly and efficiently routed to the right applications. High-frequency traders, for example, value speed when making financial decisions. Risk managers, on the other hand, need to slice and dice data in multiple ways to build the sophisticated models needed to create financial instruments to determine risk and return.
Banks and financial institutions are and can effectively utilize data to offer customized products and services to consumers in real-time. In the new data-driven world, success of an organization depends on its ability to fuel data-driven innovation by modernizing the IT and most importantly by bringing the power of the cloud to every part of the business and help them have a secure hybrid multi-cloud experience.
A hybrid multi-cloud environment helps the organization to continue to use their robust technology frameworks while leveraging the latest applications in the areas of Machine Learning, Artificial Intelligence (AI) and other modern fintech applications which are born in the cloud.
While AI fuels innovation, data fuels AI. NetApp is helping banks deploy AI in an environment where it can interact seamlessly to fuel business innovations. Data is dynamic and rests in multiple places (edge, core, and cloud), NetApp Data Fabric unifies data management across the data pipeline.
Organizations can realize the promise of AI and deep learning by simplifying, accelerating, and integrating their data pipeline with the NetApp’s AI proven architecture and cloud-connected all-flash storage. But the hybrid multi-cloud must deliver choices, not complexity. The right experience is key.
Data – Global Currency of Financial Services
For financial marketers as well as consumers, data and technology have improved mobility, access and convenience. By leveraging the vast quantity and diversity of data, the financial sector can uncover patterns and pursue breakthrough ideas, to win in an increasingly competitive business landscape.
The challenge for the financial sector is to customize and broaden customer offerings with a constant focus on improving IT efficiency. Today bankers are becoming data thrivers for multiple reasons; their IT investment vary from modernization of infrastructure to leveraging public and private cloud services. New data roles and technologies are being used to manage challenges in the data security and compliance. With unstructured data exploding rapidly, data is increasingly distributed across on/off-premises.
From an Indian point of view, the finance sector has outlined a blueprint for technology adoption with a proactive strategy to use AI to leverage the power of data. AI techniques are being applied to unstructured data sources to derive critical investment and risk indicators in shorter timeline than traditional methods.
