A study led by RBI officials, “How Indian Banks are Adopting Artificial Intelligence?” was published in the RBI’s latest monthly bulletin. It provided empirical reflections on AI adoption for major public and private sector banks in India, using text-mining techniques on the banks’ annual reports from 2015-16 to 2022-23. It also examines the relationship between financial indicators and banks’ AI exploration.  

The use of artificial intelligence is gaining traction, led by private banks. According to RBI officials, asset size and CRAR (capital-to-risk-weighted assets ratio) impact the adoption rate.  

Among several banking variables, they discovered that a bank’s size and financial health have a favourable influence on its concentration on AI, illustrating the impact of economies of scale and the availability of investment in technical innovation.  

 “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 the study. 

They observed that this conclusion is in line with resource-based theory, which claims that organizations with more resources are more likely to invest in innovation and modern technologies such as AI, as well as survey results indicating a higher AI adoption rate among banks with larger asset sizes.  

Furthermore, larger banks, due to problems in cross-vertical coordination, are expected to receive greater net profits from using such technologies and data integration, enhancing motivation for AI adoption.  

It could also indicate that smaller banks have a more difficult time adopting technologies like AI due to higher fixed costs and a lack of economies of scale, the authors stated.  

AI Adoption Linked to CRAR 

According to the study, the capital-to-risk weighted asset ratio (CRAR), which is a proxy for the bank’s capital adequacy and reflects its financial health, has a positive relationship with the AI score.  

This outcome supports the assumption that well-capitalized banks are better positioned to take on investment risks in new technologies due to significant capital buffers and confidence in pursuing AI solutions, it added.  

AI-related keywords 

The authors estimated that the use of AI-related keywords in private sector bank annual reports increased by about sixfold in 2022-23 reports compared to 2015-16 levels, based on a quantitative measure of AI adoption in the Indian banking system using a text-mining approach by RBI staff.  

Even in the case of public sector banks (PSBs), the focus on new-age technologies such as AI in their annual reports increased by more than threefold between 2015-16 and 2022-23.  

In a few public-sector banks, enthusiasm for AI-based technology has been broadly comparable to that of their private-sector peers, particularly 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 reduced human interventions,” officials stated.  

They mentioned that data analytics is another important focus area, with potential fraud detection and predictive analytics applications.  

While cloud computing and big data continue to be the key technologies banks use, there is a growing realization of the potential for newer AI and ML technologies such as Robotic Process Automation (RPA), the Internet of Things (IoT), and Natural Language Processing.  

Automation, data analytics, cloud computing, and big data are the primary focus areas, with banks increasingly considering RPA, IoT, and NLP-related technologies in recent years. 

Full study