Use of AI in banking poses challenges around bias and data ethics: RBI bulletin
Mumbai: While artificial intelligence is expected to have a big impact on risk assessment and fraud detection in banking and finance, it will also pose challenges such as the possibility of bias and issues related to the ethical use of data, said an article in the Reserve Bank of India’s (RBI’s) October bulletin, published on Monday.
Written by RBI officials from the Department of Economic and Policy Research, the article had the usual disclaimer that the views were those of the authors and not of the central bank. It said AI has the potential to reduce inefficiencies through automation, by minimising errors in human decision-making and providing cost-effective solutions.
“It is also expected to make banking services accessible to the population at the bottom of the pyramid,” it said, adding that RBI has recognised the potential of artificial intelligence and machine learning (AI/ML) and related technologies, and has encouraged banks to use them for due diligence and monitoring for know-your-customer (KYC) and anti-money laundering (AML) norms.
According to the article, annual reports of Indian banks from FY16 to FY23 showed that both private and public banks were increasingly emphasising AI and related technologies. That said, the pace of adoption has been quicker among private sector banks, it added.
“The greater adoption of AI in private-sector banks could be due to a larger proportion of their clientele being better equipped to access digital services and more comfortable with modern technology-based solutions,” it said.
Private banks lead the way
According to the article, private banks often cater to more financially aware and affluent customers and therefore could see higher potential for AI-based solutions such as customer segmentation, robo-advisory, and robo wealth management tools to cross-sell or provide other financial services. It added that private banks, especially those with relatively fewer branches, were much more likely to adopt AI-based solutions to gain new customers or cross-sell different products as it would be more cost-effective.
The authors found that initially (FY16), public-sector banks were proactively considering AI/ML and related technologies, with a similar number of AI-related keywords (AI score) as their private counterparts. However, from FY17 to FY23 the number of AI-related words in the annual reports of private banks picked up.
“The usage of AI-related keywords in the annual reports of private-sector banks increased approximately six-fold in FY23 reports as compared to FY16 level. This may be due to a combination of recognition of additional use cases of existing AI-based technologies along with more agility in adopting newer and more advanced AI techniques and models,” the article said.
