Introduction: The banking technology into the banks in India started with total bank automation in late 1980s followed by introduction of debit and credit cards in early 1990s, Electronic Clearing Services (ECS) in late 1990s, Electronic Fund Transfer (EFT) in early 2000s, RTGS and NEFT in 2004-2007, then came the digital banking products including mobile banking, internet banking, UPI etc. In the recent times, technology based new banking services have emerged in banking landscape such as cloud banking, Chatbots with the help of some innovative applications like Artificial intelligence, Machine learning etc.
Artificial Intelligence (AI) is the technology that enables machines to simulate human intelligence and cognitive capabilities, allowing them to perform tasks that typically require human reasoning, learning and decision making. It encompasses various fields like machine learning, deep learning and natural language processing etc. The use of AI is found in the areas of Language Translation, virtual Assistants, Self-Driving Cars, Medical diagnosis, financial and banking services, supply chain and workflow management, Fraud detection and in many more areas.
Generative Artificial Intelligence (Gen AI) is a type of AI that can create new content and ideas including conversations, stories, images, videos and music. The key difference between AI and Generative AI is that AI analyses existing data to make predictions or decisions, while Generative AI creates new contents based on learned patterns. Traditional AI excels at tasks like pattern recognition and data analysis whereas Generative AI can generate original contents like text, images, music etc through Chatbot, ChatGPT etc. Generative AI Chatbots leverage large language models (LLMs) and natural language processing (NLP) to understand user queries and generate human-like, contextually relevant responses, going beyond simply retrieving information. They can also create new content like, text, images or even music.
GPT (Generative Pre-trained Transformer) models are pre-trained on vast amounts of text data and then fine-tuned for specific applications, making them the foundation for AI- powered chatbots like ChatGPT. ChatGPT was created by Open AI, an AI research company. ChatGPT inter alia can be used to write different types of content like articles, e-mails and code.
Agentic Artificial Intelligence (Agentic AI) is a type of AI that is goal-driven, capable of independently initiating actions and adapting to changes without constant human guidance while Generative AI requires more human input and guidance. Agentic AI is used in customer support, supply chain optimisation and workflow management while Generative AI is generally used for content creation like text, images and music. Generative AI requires large datasets to learn patterns while Agentic AI can operate with smaller datasets. Agentic AI is complex to build and maintain while Generative AI relies on the quality of its input data.
Artificial Intelligence and Human Intelligence: Artificial Intelligence and Human Intelligence differ in fundamental aspects of creativity, emotional intelligence, ethical reasoning etc though both of them may create new contents. Artificial intelligence creates new content from existing data only. That means when there is no existing data, Generative AI cannot create anything new. Moreover, Artificial Intelligence has no ethical reasoning, that is AI cannot say which aspect of any proposition is good or bad. Besides Artificial Intelligence is also not having any emotional intelligence like showing empathy to anybody etc, whereas human intelligence has strong cognitive power and thinking ability and can also create new content with consultation and or without consultation with existing data and information. Generative AI is basically a specialized tool and operates based on pre-defined codes and training data limiting its ability to understand context or find an actual novel solution. But, Generative AI can process large amount of data with much speed which human intelligence cannot do so speedily. Above all, human intelligence is much more superior than artificial intelligence in terms of creativity and artificial intelligence cannot replace completely human intelligence in important areas of creativity, decision making, cultivation of ethics, empathy etc. So, artificial intelligence is not a threat to human intelligence, rather it helps and supplements human intelligence by doing large volume of repetitive and data analysis work with speed and accuracy and creating some contents based on such data.
Applications of Generative AI in Banks: Generative AI (Gen AI) and Agentic AI are the most transformative technologies in today’s rapidly evolving landscape, offering significant opportunities for the banking industry. By leveraging its capabilities such as information retrieval, drafting, summarisation, and translation and understanding its limitations, banks can use the following applications such as (a) Enhancing customer service: Generative AI-powered chatbots and virtual assistants can handle customer enquiries, provide 24/7 support and offer personalised recommendations, improving customer satisfaction and reducing the work load on human beings, (b) Fraud detection and Risk Management: Generative AI models can analyse transaction data and identify patterns indicative of fraud or other financial crimes enabling banks to take pro-active measures, (c) Credit Risk Assessment: AI can analyse customer data and identify potential risks, helping banks make informed decisions, (d) Personalised marketing and sales: Generative AI can generate personalised communications and offers, supporting marketing and sales efforts by tailoring messages/products to individual customer needs, (e) Automation of tasks: Generative AI can automate tasks like data extraction, research and report generation, freeing up human employees to focus on more strategic works, (f) Algorithm Trading: AI can analyse market data and generate trade signals, potentially improving trading performance, (g) Wealth Management and Portfolio Optimisation: Generative AI can help banks provide personalised investment advice and optimize customers’ portfolios based on individual customer needs and risk profiles and (h) Compliance and AML: AI can help banks comply with regulatory requirements and detect suspicious transactions.
Benefits of Generative AI in Banks: The use of path-breaking technology like Generative AI in banks can provide lot of benefits to the bank which inter alia pushes up Topline and Bottomline of the banks such as (a) Increasing Employee Productivity: Automating repetitive tasks such as document summarization, report generation, and translation allows employees to focus on higher-value activities, (b) Improving Operational Efficiency: Automated risk assessments, compliance reporting, and fraud detection streamline back-office processes, reducing cost and errors, (c) ·Enhancing Customer Experience: AI-driven chatbots and personalised financial recommendations provide clients with more responsive and tailored interactions, (d) Innovating Financial Products and Services: AI-generated insights enable banks to create customised investment strategies and credit risk models, (e) Enhancing and optimizing decision making: AI-powered insights can help banks make more informed decisions on loan applications, investment strategies and other business decisions and (f) Competitive advantage: Embracing Generative AI can help banks stay ahead of the curve and gain a competitive advantage in the rapidly evolving financial services industry.
Generative AI adoption challenges in banks: As banks navigate this rapidly evolving landscape, they need to carefully integrate Gen AI applications within a highly regulated environment. Given the unique circumstances of each bank – such as size, market position, strategic objectives, and regulatory environment – there is no one-size-fits-all approach to implementing Gen AI. In general, successful adoption of Gen AI requires banks to act across multiple dimensions. AI initiatives must align with business strategies to be supported by robust IT infrastructure, and adhere to rigorous risk controls. Furthermore, fostering a culture of innovation and providing comprehensive training on Gen AI’s capabilities and limitations are essential to overcoming adoption challenges.
Examples of use of Generative AI in banks in India and abroad: 1) SBI Card, a payment service provider in India, leverages Generative AI and machine learning to enhance their customer experience, 2) Master Card has recently announced the launch of a new Generative AI model to enable banks to better detect suspicious transactions on its network, 3) Airwallex, a global payment company has introduced a Generative AI co-pilot that utilises large language models to accelerate the company’s KYC assessment and onboarding processes, 4) Gen AI has been recently employed by Citigroup to evaluate the effects of new US Capital regulations. They used Generative AI to sift through and summarise 1089 pages of new capital regulation, 5) OCBC Bank has rolled out a Generative AI Chatbot for its 30,000 global employees to automate a wide range of time-consuming tasks such as writing investment research reports and drafting customer responses, 6) SBI Chatbot known as ‘SBI Intelligent Assistant’ is designed to help customers with everyday banking tasks just like bank representative. Another Generative AI driven solution of SBI is ‘Ask SBI Chatbot’ which is designed to assist employees in handling complex business scenarios. It serves as a centralized point of contact for internal staff, improving operational efficiency and reducing reliance on manually curated documents, 7) HDFC Bank launched EVA (Electronic Virtual Assistant), a Chatbot, to offer true power of conversational experience to its customers on all the digital platforms such as the website, Mobile site and the dedicated portal for the bank’s customers, 8) Canara Bank’s Chatbot is named ‘Aura’. It is a 24/7 virtual banking assistant available on the Canara bank website and via the app which help the customers with banking queries and tasks including account details, loan statements etc, 9) The Chatbot of Yes Bank is named as ‘Yes Robot’. It is a customers’ personal banking assistant available anytime, anywhere and is accessible through applications and interfaces such as whatsapp and Yes Bank website, 10) HDFC Bank is leveraging generative AI for structured and unstructured data extraction from documents, developing AI based instant credit decisioning models and significantly improving fraud monitoring through real-time self monitoring ML (Machine learning) models. 11) PSBs in India have introduced AI- driven soft skill training to enhance customer service. PNB Managing Director said, “To enhance training, we are incorporating conversational AI into our programmes. This AI conducts half-hour sessions where it poses challenging and sometimes difficult questions. It analyses body language, etiquette and communication skills, providing an in-depth performance matrix and a ranking system to help employees identify areas of improvement.
The future of AI in banking: The future of AI in banking is positioned to be transformative with advancements that promise to reshape the industry in many ways. As technology continues to evolve, banks are expected to leverage AI to deliver even more personalised and efficient services. Some trends that are likely to define the future of AI in banking are (a) Advanced personalization: AI will enable banks to offer hyper-personalised services tailored to individual customers’ needs and preferences. By analyzing vast amount of data available in banks, Generative AI can provide customised financial advice, different banks’ products recommendations and real-time support enhancing the overall customer experience, (b) Enhanced security measures: with the increasing sophistication of cyber threats, AI will play a critical role in bolstering security measures in banks. Advanced AI algorithms will be able to detect and respond to fraudulent activities in real-time, ensuring the protection of customers’ assets and sensitive information, (c) Automated compliance: As regulatory requirements become more complex, AI will automate banks’ compliance processes. Machine learning models can continuously monitor transactions and flag potential violations reducing the risk of non-compliance and streamlining regulatory reporting, (d) Expansion into new services: AI will open up new avenues for banks to offer innovative services such as AI driven investment platforms, robo-advisors and smart contracts. These services will not only attract new customers but will also create additional revenue streams for customers, (e) Ethical AI development: There will be a growing emphasis on ethical AI development ensuring that AI systems are fair, transparent and free from biases. Banks will invest in framework and guidelines to govern the responsible use of AI fostering trust among customers and stakeholders.
Challenges of Generative AI in banking:
Using generative AI in banking presents several challenges and limitations. (a) One major issue is data privacy and security. Generative AI can handle vast amounts of financial data but must be used cautiously to ensure compliance with various regulations, (b) Integrating data-driven AI systems increases the risk of data breaches, requiring continuous monitoring and updates to protect sensitive customer information. Furthermore, AI models rely on accurate and up-to-date data to produce reliable results. Poor or incomplete data-sets can lead to incorrect outputs, negatively impacting financial decision-making and customer trust. (c) Another significant challenge is the integration of AI technologies within existing banking systems. Many banks operate with legacy systems that might not be compatible with new AI frameworks, which can create costly and time-consuming issues. (d) Also, while AI can automate and streamline many processes, it should not have the final say in critical decisions such as loan approvals. Instead, AI should handle data analysis and initial assessments, leaving the ultimate decision to human financial professionals. This approach ensures that AI serves as a powerful tool to enhance banking operations without neglecting its limitations, (e) In India, there is no dedicated AI law at present, but it is actively developing a regulatory framework through various initiatives such as “The Digital Personal Data Protection Act and the Information Technology Intermediary Guidelines and Digital Media Ethics Code” which addresses specific aspects of AI development and deployment such as data privacy and ethical guidelines.
Conclusion: As Generative Artificial Intelligence (Gen AI) will continue to proliferate and integrate more deeply into future banking operations, the banking industry will be more active, robust, customer-centric and secure. The future of AI in banking will be not just about technological progress, but it will be about creating a more intelligent, inclusive and profitable banking and financial ecosystem that wil benefit all such as individuals, companies, organisations and the economy of the nation as a whole, though, after all, the manifested work of artificial intelligence will be utilised under the guidance and supervision of human intelligence to avoid any kind of misadventure.
P.P.Sen
Managing Director
Citizens’ Urban Cooperative Bank, Gangtok.