By Vinod Ganesan, Country Manager – India, Cloudera
India’s banking sector is witnessing significant competition from new-age fintech companies as well as large technology companies like Facebook that are entering the fray with payment solutions. Facebook’s USD 5.7 billion investment in Reliance Jio Platforms can help the social media giant monetize WhatsApp and increase its revenue from India. With over 2 billion users globally, of which more than 400 million of them are in India, this partnership with Jio in the short-term plans to enable millions of mom-and-pop stores to sell products and transact via WhatsApp .
This presents a significant opportunity to digitize the presently unorganized retail segment comprising of traditional kiranas and mom-and-pop stores accounting for about 88 percent of India’s retail industry . Not being able to actively cater to this segment is where traditional banks lack the institutional agility to act upon evolving customer demands.
By adopting artificial intelligence (AI) or machine learning (ML) models across the enterprise, banks can optimize several processes that are operational in nature and gain a comprehensive single view of their customers derived from many different data sources. These models can help banks identify key behavioral characteristics so that they can offer better and more personalized customer service through various touchpoints. Additionally, AI and ML can assist banks in automating fraud detection and significantly reducing false positives of suspected money laundering transactions. By enabling near real-time automated credit risk decisions and risk management alerts, AI/ML solutions help banks reduce risk while expanding revenue opportunities.
Yes Bank, for instance, has been exploring ways in which it can leverage data as a key driver to improve customer acquisition and customer experience. With the help of a unified data management platform, the bank is now able to synchronize and process structured and unstructured data generated across many systems with speed and agility to run real-time analytics whilst maintaining heightened data security .
With technology revolutionizing the banking sector, traditional banks cannot stay put – they must recalibrate how they operate to align with the future.
Enterprise-wide data strategy for intelligent automation
For AI or ML to deliver significant and transformative value to the business, these technologies need to be implemented across enterprise functions to reduce the time spent on mundane chores and automate decision-making. However, businesses will also need a reliable IT backbone that can support the use of big data.
Most banks in India currently operate on legacy systems, which makes it challenging to implement AI/ML strategies as these systems aren’t adept at supporting big data effectively. Without a conscious effort to embed data and machine learning intelligence across the business at large, banks risk not being able to identify the next best move for growth, or new revenue streams.
Eliminate silos, access it all
AI and ML also need a constant inflow of information to function effectively. Banks must focus on eliminating data silos to employ a holistic data strategy. An enterprise data cloud, which is a data platform that can manage the entire data lifecycle from edge to AI, is key to integrating data across the enterprise. With an enterprise data cloud, information can be collected at the edge – through touchpoints like ATMs, mobile phones, and bank branch offices – centralized and stored in a reliable, accessible manner. This information should then be fed into AI/ML tools regardless of where it resides without compromising the security or governance of data. By gaining control of their data from the data collection process all the way to prediction, banks will be well-poised to use the power of their data to serve customers better, operate with greater efficiency, and strengthen security to protect the business.
Crossing the chasm
It is understandably not easy or possible to replace entire IT systems or rewire how banks function overnight. There needs to be a plan and a conscious effort to digitally transform, such as having an enterprise AI strategy charting out a roadmap specific to the needs of the organization. In order to become AI-first, Indian banks will need to leverage an enterprise data cloud that can help automate, augment, or completely reinvent their operations. Only when AI is adopted enterprise-wide can employees as well as C-suite decision makers adopt data-driven decision-making to arrive at better answers than humans or machines could arrive at, on their own.
Realizing the return on investments made towards developing and implementing AI/ML pilot projects is also not a fast turnaround. Indian banks need to cast a wider net, implementing several pilot projects simultaneously and reviewing these results regularly to gauge if the project is adding value or needs to be halted. They may not see success with every single project — however, the ones that do succeed will significantly increase the efficiency of the organization. The ways in which AI can be used to better decision-making will continue to expand; and while newer implications can disrupt workflows, banks that succeed at implementing AI throughout the organization will find themselves at a greater advantage in this constantly evolving, competitive business landscape.
