Artificial Intelligence (AI) adoption is gaining momentum led by private banks, with asset size and CRAR (capital to risk weighted assets ratio) influencing the rate of adoption, according to RBI staffers.

Among different banking indicators, size and financial health of a bank is found to positively influence the bank’s focus on AI, reflecting the impact of economies of scale and the availability of investment on the technological advancement, they said.

Further, text mining of annual reports of Indian banks during 2015-16 to 2022-23 reveals that both private and public-sector banks are increasingly emphasising on AI and related technologies; however, the pace of increase is higher for private banks

“Across all models, we find that AI score is positively related to the asset size of the banks as evident from positive and statistically significant coefficient, suggesting higher adoption by larger banks,” said RBI officials Shobhit Goel, Dirghau K. Raut, Madhuresh Kumar and Manu Sharma in a study “How Indian Banks are Adopting Artificial Intelligence?”, which has been published in RBI’s latest monthly bulletin.

They observed that this finding aligns with resource-based theory, which posits that organisations with greater resources are more inclined to invest in innovation and modern technologies like AI and survey results showing higher AI adoption rate among banks having larger asset size.

Further, larger banks owing to their difficulties in coordination across verticals are likely to achieve higher net gains from adoption of such technologies and data integration, thereby increasing the motivation for adoption of AI.

It may also be indicating that the adoption of technologies such as AI is relatively difficult for smaller banks due to larger fixed cost and absence of economies of scale, the authors said.

CRAR

Capital-to-risk weighted asset ratio (CRAR) which is a proxy for the capital adequacy of the bank and thus a reflection of the financial health of the bank, is positively related to AI score, per the study.

This result resonates with the viewpoint that well-capitalized banks are better positioned to take the investment risks in new technology in terms of adequate capital buffers and confidence to pursue AI solutions, it added.

AI related keywords

The authors assessed that the usage of AI related keywords in the annual reports of private sector banks increased by approximately six-fold in 2022-23 reports as compared to 2015-16 level, going by the quantitative measure of AI adoption in the Indian banking system using a text-mining approach by RBI staffers.

Even in case of public sector banks (PSBs), the emphasis on new age technologies like AI in their annual reports has increased more than 3 times between 2015-16 and 2022-23.

“In a few public-sector banks, the enthusiasm towards AI based technologies is broadly at par with their private sector peers, especially in recent years.

“Further, the word cloud reveals interesting insights with most banks focussing on automation which may be due to a push for efficiency gains and reduce human interventions,” the officials said.

They noted that data analytics is another major thrust area with possible usage in fraud detection and predictive analytics.

While cloud computing and big data continue to be the major technologies which banks are employing, there is an increasing recognition of potential usage of the newer AI and ML technologies like Robotic Process Automation (RPA), Internet of Things (IoT) and Natural Language Processing (NLP).

Automation, data analytics, cloud computing and big data are the major thrust areas, with increasing consideration for RPA, IoT and NLP like technologies by banks especially in recent years.

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