We have witnessed how the consumer loan landscape is experiencing a seismic shift from manual processes and human judgement as Artificial Intelligence takes centre stage. AI is revolutionising the lending landscape through innovation in credit scoring, fraud detection and risk assessments. The benefits and potentials of AI-driven lending include enhancing credit scoring models, high-risk lending, the changing role of loan officers, uncovering fraud and deception, optimising risk assessment, building consumer trust, streamlining loan processing, new applications of AI in lending and helping to prepare for the future.  

Challenges and concerns 

Like every technology introduced into various sectors, AI in this area also projects a few challenges and concerns. The adoption of Artificial Intelligence in the loan industry certainly possesses few challenges. Utilising alternative data sources can lead to privacy and ethical concerns as well as the potential for biased decision-making. Likewise, a higher dependence on automated processes can increase job displacement for human employees. In order to eradicate this, regulatory bodies and lending institutions should work together to address such concerns and develop ethical guidelines to govern AI implementation in the loan industry.  

RBI Governor’s take on AI in consumer loan landscape 

The Reserve Bank of India is advising banks, NBFCs and fintech firms to recalibrate their pre-set algorithms, which apply Artificial Intelligence and increasingly use machine learning tools occasionally. This should be taken care of in response to the evolving dynamics of the financial ecosystem and new information emerging about sectors, consumer segments and global headwinds. Shaktikanta Das, RBI Governor, said, “Banks and NBFCs need to be careful when relying solely on pre-set algorithms as assumptions based on which the models are operated. These models should be robust, tested, and re-tested periodically.” The Governor was speaking at the FIBAC 2023 Conference, which was jointly organised by FICCI and IBA in Mumbai.  

“They may need to be calibrated and recalibrated from time to time based on the changing contours of the financial ecosystem and fresh information. It is necessary to be watchful of any undue risk build-up in the system due to information gaps in these models, which may cause dilution of underwriting standards,” Das added. 

Fintechs were once praised as disruptors and now collaborate extensively with established banks for lead generation and co-lending. This new and rapidly emerging partnership can benefit both parties by providing banks with ready customers without any operating costs and Fintechs with more comprehensive platforms and lending capacity.  

Nevertheless, the partnership has initiated innovative products and services besides the new business models. For example, banks and NBFCs can currently fund ‘new to credit’ customers by analysing their buying patterns and trends with fintechs leveraging hundreds of data points for creditworthiness assessment. Moreover, cash flow lending to small businesses in smaller geographies has risen. 

However, machine learning or AI-powered models have innate limitations as they depend on massive historical data and produce output according to the input fed into the system. It is still unknown how the new data or developments are fed into the system and at what intervals they are done. The RBI Governor emphasised the concerns on model-based lending through analytics, while other issues like exclusions or biases developed by machine decision-making also drew significant concerns.  

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