The use cases of AI and Machine Learning (ML) applications by banks in India range across areas such as credit underwriting, regulatory capital planning, liquidity management, fraud detection and prevention, risk assessment and management, portfolio optimisation, pricing models, and chatbots
FinTech BizNews Service
Mumbai, January 31: Union Minister of Finance and Corporate Affairs, Smt Nirmala Sitharaman presented the Economic Survey 2024-25 in the Parliament today.
The highlights of the use of Artificial Intelligence by banks in the survey are as follows:
Over the past several decades, banks have consistently adapted the latest technological innovations to redefine customer interactions. Globally, banks introduced ATMs in the 1960s and electronic card-based payments in the 1970s. The 2000s saw the widespread adoption of 24/7 online banking, followed by the rise of mobile banking in the 2010s. Now the world is in the artificial intelligence (AI)-powered digital age, driven by decreasing data storage and processing costs, greater accessibility, and connectivity. These innovations can lead to higher automation and often enhance human decisionmaking speed and accuracy when correctly managed to mitigate risks.
The use cases of AI and Machine Learning (ML) applications by banks in India range across areas such as credit underwriting, regulatory capital planning, liquidity management, fraud detection and prevention, risk assessment and management, portfolio optimisation, pricing models, and chatbots. The rapid pace of technological evolution in India, particularly in areas like AI, blockchain, and data analytics, has created new opportunities to reimagine traditional financial services and processes.
AI and large language models (LLMs) have enhanced customer service through interactive chatbots and personalised experiences, while blockchain offers secure, transparent, and efficient transactions. Moreover, evolving consumer behaviour and expectations, driven by the rise of digital natives and increasing demand for personalised, seamless, and convenient financial solutions, encourage established companies and newcomers to innovate to remain competitive.
Along with the benefits, using AI in the banking system entails a few risks. The black-box nature of AI systems can make it difficult to assess the system's reliability or contest its decisions. This lack of transparency can lead to trust concerns and challenges in validating the fairness and accuracy of AI decisions, making it challenging to audit or interpret the algorithms that drive the decisions. Accountability risks include difficulty in tracing decisions to their source and establishing liability. Other risks include those related to (i) human resources, such as inadequate human oversight, over-reliance on AI, and loss of human expertise; (ii) cyber risks; (iii) malicious usages like synthetic identity frauds, rogue trading, and market manipulation; (iv) system related risks such as inability to intervene and market correlations; and (v) third-party dependencies and service provider concentration.
International bodies such as the Organisation for Economic Cooperation and Development (OECD) have outlined core principles governing the use of AI, which include inclusive growth, respect for the rule of law, transparency and explainability, robustness and safety, and accountability. The Hiroshima AI Process Comprehensive Policy Framework established in December 2023, includes a set of guiding principles and a code of conduct, marking a significant step towards a coordinated global approach for the responsible development of AI. Different techniques are being adopted by regulators across the globe, most of which are focused on principles-based guidance.
Establishing robust AI governance is the first and crucial step in addressing the challenges that come with the implementation of AI systems. Without an appropriate governance framework, AI systems may operate without clear guidelines or oversight, leading to potential abuse or misuse of technology. As vulnerabilities could evolve with the pace of innovation and degree of AI integration in financial services, regulatory and supervisory effectiveness may take a backseat if financial regulators’ AI-related skills and knowledge do not keep pace with developments in this space. Accordingly, the RBI has proactively engaged with regulated entities and experts to assess the ongoing developments while effectively communicating its expectations through multiple engagement forums. It has also created a regulatory sandbox focusing on innovative technology products/services.
Further, the RBI announced the establishment of a committee to create a Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) in the financial sector.
