The financial services industry has been in a constant state of disruption for the last decade or so. There has been a rapid evolution of technologies, business models and customer expectations. However, if I were to call out a watershed disrupter with the potential to revolutionize the industry, it is without doubt, Artificial Intelligence (AI).
AI can drive significant benefits to both the top and bottom lines. AI based capabilities such as adaptive and predictive analytics, autonomous machine learning, and intelligent automation can deliver next generation customer value across value chains.
However, to successfully leverage AI, banks must cut through the hyperbole of technological breakthroughs, which dominate today’s headlines. They must consider AI simply a means to an end; an approach to radically improve decision making across the enterprise and address business issues at scope and scale. This requires banks and technology firms to focus on real world use cases that solve specific issues and drive quantifiable business value. Read on...
Increased research in the area of machine learning, has improved its capabilities, enabling better predictability and consistency of outcomes. However, banks and technology firms must ensure that the machine learning use cases they explore are defined by business issues. The availability and variability of data are also important factors for successful identification and implementation of use cases.
Understandably, it is still early days for the banking industry in the AI journey. The industry and its technology partners continue to invest and experiment with the technology. It is however time to go further and focus on business outcomes and value. Only then can banks surmount barriers to AI adoption. Determining how to measure and document the business value of a use case, keeping it simple, leveraging existing infrastructure and establishing a clear deployment strategy are aspects banks should consider as they begin their AI journey.
In this newsletter, we explore real world use cases where AI and Machine Learning offer quantifiable benefits to banks. We look at how banks can leverage a modern core banking platform equipped with AI and Machine Learning capabilities to drive business value. We also showcase the latest research that can help banks determine the scope, state, value, and risks in AI plans.
We thank you for your time and hope you find the insights valuable.
Managing Director & CEO
Oracle Financial Services Software Limited
Learn how Oracle FLEXCUBE’s Machine Learning Framework can help banks drive positive business outcomes by empowering better decision making [...]
Learn how a realistic use case strategy can help a bank jumpstart the AI journey and rapidly achieve business benefits[...]
Learn how digital experience powered by AI and ML can offer banking customers hyper personalized services [...]
24 July 2018
AI is almost a definition of hype. Yet, it is still early: New ideas will surface and some current ideas will not live up to expectations. This Hype Cycle will help CIOs and IT leaders trace essential trends and innovations to determine scope, state, value and risk in their AI plans.
Now is the deciding time for the future of AI. Only 4% of CIOs worldwide report they have AI projects in production. Every decision about AI influences AI's long-term direction. AI gives hope and fulfills sci-fi fantasies; it is both utopian and dystopian. Anxiety about implementing AI is increasing. The term "artificial intelligence" is on gartner.com's top 10 lists for emerging searches, high-growth searches and most popular searches. Data and analytics leaders across many industries are seeking a breakthrough, which they should target in the long run. However, the immediate impact of AI is within practical applications. [...]