| | MAY 20228CIOReviewIN MY OPINIONBANKING ON THE FUTURE OF ARTIFICIAL INTELLIGENCE FOR MAXIMI$ING DATA Artificial Intelligence (AI) is at the heart of a paradigm shift in the financial industry. We are witnessing a revolution as AI is having a powerful impact on banking. Most of AI-led innovation can be grouped under one of the four buckets: Customer Experience, Operational Efficiency, Revenue Generation and Risk Mitigation. Although these buckets seem different, there is some overlap.Customer Experience: Customer centricity should be the starting point of digitally-led innovation , with an objective of building a wholistic 360-degree view of the customer. This sounds simple, yet achieving this is easier said than done. Complexities associated with fragmentation of customer data across different business groups prevail. Providing an enhanced customer experience covers multiple aspects: personalized and contextualized experiences, seamless client onboarding, unified experience across service lines, seamless self-service capabilities, Virtual Chat Bots, Robo-advisors or a recommendation engine. Also notable, recommendation models created for banking clients are much more complicated than those used in e-commerce applications. That's because the datasets are disparate and include past banking or trading history, social events, lifecycle events, spending patterns, etc. Given the complexity dealing with individualized fragmented data, and the underlying organizational silos, optimization of the client experience is a roadmap. Banks that strategically invest into this earlier on will be the clear winners.Operational Efficiency: Banks are always seeking to derive greater efficiencies. The advent of Robotic Process Automation (RPA), Optimal Character Recognition (OCR) and Natural Language Processing (NLP) tools has made this much easier. Shifting much of these tedious, manual tasks from human to machine, banks have significantly reduced the need for human involvement. That's had a direct impact on everything from performance and efficiency levels to staffing and expenses. RPA solutions are generally less obtrusive within the existing infrastructure, so it's easier for banks to see this as the path of least resistance. RPA is used to extract structured and unstructured content from emails, pdfs By Ravnit Singh Kohli, Managing Director (Technology) and Head of the New York/Canada Business Unit, SynechronRavnit Singh Kohli: As the Managing Director-Technology and Head of NY/Canada Business Unit at Synechron, Ravnit is currently responsible for leading multiple global client relationships, driving business development and sales, IT Strategy and consulting, execution and delivery, P&L and program management for strategic financial clients. In addition, a vital part of his role is providing thought leadership around the impact of emerging technologies (particularly AI/Machine Learning/RPA) on the financial industry. He is a key contributor to Synechron's Accelerator programs, leveraging emerging technologies to address key business challenges in the BFSI space. Ravnit has significant techno-functional hands-on experience as well as a proven track record for execution and delivery of IT Solutions in capital markets and wealth management functions across asset classes, lines of businesses and front-/mid-/back-office functions. Prior to Synechron, Ravnit was a Senior Developer at Polaris Consulting & Services Limited. He received his engineering degree from the prestigious Indian Institute of Technology, Bombay.
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