By Rajashekara V. Maiya
The leap from AI to generative AI
The introduction of ChatGPT, a language model developed by OpenAI, revolutionized the evolving landscape of artificial intelligence (AI), profoundly impacting how we communicate, access information, and interact with technology.
While not an entirely new concept, generative AI marks a significant advancement from traditional AI systems, enabling machines to not only recognise patterns but also generate new content that closely resembles human creations.
Generative AI or Gen AI utilises large language models (LLMs) to automate tasks and produce human-like content, advancing from rule-based systems to creative data-driven models.
Generative AI in banking
Generative AI can potentially supercharge banks’ operations, enhancing efficiency, improving customer experiences, and driving growth.
According to a McKinsey report on generative AI, banking is one of the top industries that could experience an annual revenue impact of $200 – $340 billion by implementing various use cases. Leading banking and financial institutions like ABN Amro, BBVA, and Goldman Sachs are already experimenting with several use cases for generative AI, such as marketing, loan processing, credit analysis, and debt collection.
A look at some impactful Gen AI use cases in banking
Customer support: Generative AI models like ChatGPT are a promising replacement for traditional chatbots in customer support. Banks have deployed chatbots for several years but with limited capabilities in queries and responses. With its ability to comprehend context, generate human-like responses, and adapt to various conversational scenarios, Gen AI significantly improves chat-based interactions, paving the way for more engaging and effective communication between humans and AI-powered systems. Plus, a model like ChatGPT, with more than 200 billion communication parameters, makes it more powerful, given the sheer number of permutations and combinations.
Personalised financial advice: By analysing vast amounts of customer data, including transaction history, spending patterns, and investment preferences, generative AI algorithms can generate customised and richer recommendations for products and services that align with individual customer needs.
Compliance and regulatory reporting: Being heavily regulated, banks must adhere to stringent compliance requirements and reporting standards. Banks can automate compliance checks and streamline regulatory reporting processes by employing AI-powered systems. Since generative AI models adapt and evolve rapidly, this reduces the chances of errors and inaccuracies and ensures that banks remain compliant with changing regulations.
Language support across geographies: Generative AI models, such as ChatGPT, offer language translation services for banks with a global reach or presence. They can accurately translate documents for marketing, sales, training, and compliance purposes, ensuring that the intended meaning and ethos of the content are preserved even for customers or employees who are not fluent in the bank’s primary language.
Other use cases include customer onboarding, risk management, and fraud detection, where generative AI can deliver considerable value.
Is the future smooth sailing for generative AI?
As generative AI becomes more prevalent in business and consumer applications, ensuring sufficient computing capacity to support them will be a significant challenge. LLMs such as GPT-3 also demand substantial computational power, leading to high energy consumption. The massive data centres and computing resources required to train and operate these models can contribute to increased carbon emissions and environmental impact, posing a problem for ESG-conscious banks.
The potential impact of gen AI on employment and job displacement is another concern. GPT-4, OpenAI’s newest version, is reportedly the most aligned to human values, skills, and creativity. Banks need to work toward augmenting the skills of their workforce with rebadging and reskilling to thrive in the era of AI.
With high regulatory scrutiny for banks, ensuring that AI-generated content adheres to ethical guidelines and is not used maliciously is essential for building trust and maintaining responsible AI practices.
Using Gen AI, the responsible way
Despite the challenges, the future of generative AI is brimming with possibilities, potentially redefining how we live and work. From creating realistic virtual worlds to generating personalised content, the capabilities of generative AI are vast and ever-expanding. The technology is rapidly gaining traction in the financial services sector. It is a valuable tool to generate new data, obtain meaningful insights for making informed financial decisions, and much more.
Striking the right balance between innovation, ethical considerations, and responsible deployment will be essential to harness the full potential of generative AI for the benefit of society. As research and development in AI continue to progress, we can expect generative AI to have a profound and transformative impact on various aspects of our lives in the years to come.
(The author is Rajashekara V. Maiya, VP and Global Head of Business Consulting, Infosys Finacle, and the views expressed in this article are his own)
