Over the last decade, non-performing assets in the Indian banking system have remained a matter of concern and immense scrutiny. With the coronavirus pandemic exacerbating asset quality, non-performing loans and restructured assets are expected to further rise to 11.5% in FY22 from 8.7% in the previous fiscal. On the other hand, loan processing and disbursals have become much quicker, thanks to digital channels.
Amid these elevated levels of stressed assets, it is imperative to look into the causes of NPAs with which banks and NBFCs continue to struggle.
However, before we delve into the reasons, it is essential to acknowledge that NPAs cannot be completely eradicated given that business risk is fundamental to lending. This implies that some NPAs are purely circumstantial and arise when things do not go as planned in business. Although risk as a metric is broadly expected to be taken care of during loan processing, there are a few specific factors in today’s lending ecosystem that may be contributing to higher NPAs.
With the quick adoption of digital channels by customers and digitization by lenders, enhancing customer acquisition and experience has largely been the key focus for banks and NBFCs. A variety of products such as personal loans are being offered as quick as a wink, which forces faster decision-making by lenders based on little information from the customer. For instance, while telemarketers aggressively and desperately offer loans, customers are hesitant to provide comprehensive information readily. Thus, while a seamless customer experience may be achieved, perhaps, a crucial aspect of lending—risk—is not being effectively taken care of, particularly, in the times of aggressive sales. A much-needed corrective action here would be maintain a balance between business targets and credit quality. By no means can risk management be an option. What can come to the rescue of traditional lenders is replacing or supplementing manual credit appraisals/decisions with automated solutions to keep pace with digital sales channels.
Over the years, credit has become increasingly sachetised. While earlier, loans had a connotation of a large sum, now, like commodities, loans come in small sizes, right from a few thousand rupees to a few lakhs. These micro loans undergo underwriting checks to a much lower extent compared to larger loans, because the higher the degree of diligence, the costlier it gets for lenders. Now, given the size of these loans, it makes little sense for lenders to spend the same amount of money that goes into undertaking diligence for a big-ticket loan. This has resulted in lenders compromising on diligence for smaller loans. Think of it as a customer using ‘Pay later’ options for small-ticket purchases on food delivery apps. There is hardly any diligence involved when the customer gets credit. Nonetheless, the cost of compromising on diligence is much higher in terms of the percentage of NPAs. A solution to this challenge could be affordable, automated due diligence options using alternate data points for small loans.
Lenders are investing heavily in acquiring customers via robust front-end solutions; however, this is not being matched with digitisation at the backend. Risk and fraud analysis are not being automated, which results in the inability to verify 100% of the loan applications coming from digital channels. Basically, the lenders’ fraud checks and underwritings have a limited bandwidth in comparison with the ever-growing lending targets. When lenders keep reducing the percentage of applications being reviewed, the risk essentially keeps on rising leading to higher NPAs.
Banks and NBFCs have been employing automation in many of their processes. However, the entire value chain is yet not being fully automated, leaving loopholes in the lending process. This is true particularly in the areas of pre-emptive monitoring and early warning signal (EWS) detection. EWS systems can help identify risks associated with fraudulent accounts at nascent stages and allow timely preventive action by lenders. Only to give you a perspective, the average lag in detecting loan frauds in India was 2 years in 2019-20 and even higher at 63 months for loans of Rs 100 crore and above. EWS systems can certainly make a large difference in this scenario.
Sophisticated EWS systems that detect red flags on time by scanning not just conventional data but also alternate data points for credit assessment have become a necessity for lenders who wish to have cleaner books. All they need to do is integrate these technologies into their traditional frameworks, which will not just manage risk but also enhance their capacity to acquire more business.
(The writer is CEO & Co-Founder, Karza Technologies. Views expressed are personal)
Download Money9 App for the latest updates on Personal Finance.