To a large extent, NBFC sales growth is still linear in terms of employee strength. This will not remain sustainable in the face of extreme competition and organisations will have to depend heavily on analytics (in its multiple avatars) to augment / transform business processes. To know more about the revolutionary change in the AI and data analytics industry, Rashi Aditi Ghosh of Elets News Network (ENN) had a conversation with Haryyaksha Ghosh, Chief Data Officer, Aadhar Housing Finance Ltd.
Q. How technically progressive are NBFCs as compared to the other contenders of the BFSI sector?
Ans: The BFSI sector is quite diverse in nature with various types of organisations like commercial banks, insurance companies, non- banking financial companies, cooperatives, pensions funds, mutual funds and other smaller financial entities. NBFCs are a part of the BFSI sector. However, compared to the other players in the BFSI sector, the key difference with an NBFC is in the nature of the asset and liability sides of business.
Q. Any organisation that takes deposits from retail customers, must address the dual requirements of:
Ans: ● Managing retail customers’ deposits
● Acquiring customers by understanding the key customer segments, convincing them to deposit with such organisations (which is more of a “retail push” product), and hence inherently must be more digitally matured. Hence, these organisations must be “customer centric” and hence, must be mandatorily advanced in both technology and AI/ML.
In comparison, NBFCs in the business of retail lending or wholesale lending, do not accept retail customers’ deposits. Loans are a pull product, where the customer needs the loan. Especially in the Indian context, customers will avoid taking loans unless necessary.
The ability to generate sales is dependent more on identifying and offering the right loan products to customers, given the inherent need of the customer. Naturally, such organisations are “product centric”, and need to be necessarily as advanced as other players in the BFSI sector. Hence, NBFCs have traditionally been relatively laggards in technology as well as AI/ML applications, with the exception of a few, who are market leaders.
That said, with customers becoming more digitally savvy, and with increasing competition (and as funding gets increasingly commoditised), NBFCs are also standing up to the technology challenges. A host of NBFCs have transitioned to the latest digital technologies for lead generation customer onboarding, loan management and for other areas of business. They are no more laggards.
However, compared to the nature of business and regulatory requirements, banks will be almost always technologically more advanced than NBFCs. On top of this, the customer centricity and CASA help banks avail a plethora of customer data on an ongoing basis, thereby being able to apply AI/ML better, unlike NBFCs where the bulk of the customer data comes during the time of onboarding and in a limited manner.
Q. How significant is the role of analytics, AI and other emerging technologies in transforming the Indian NBFC sector? What major changes are you witnessing lately?
Ans: A lot of AI/ML related work is happening that is transforming NBFCs. These
are in the areas of digital onboarding (of applicants / customers), credit risk underwriting, technical evaluation of collaterals, collections, operational efficiencies, branch strategies, up-sell, preventing foreclosures (BT-Outs), asset liability management (to name a few), and many other areas.
Q. Will analytics help create value by augmenting business processes?
Ans: As competition heats up, manual processes will make way to digital automated processes backed by data, technologies and AI/ML. Maintaining armies for customer onboarding, credit underwriting, collections and operations, will become extremely cost intensive and challenging, due to rising costs, and employee poaching. To a large extent, NBFC sales growth is still linear in terms of employee strength. This will not remain sustainable in the face of extreme competition and organisations will have to depend heavily on analytics (in its multiple avatars) to augment / transform business processes.
Q. How are you using Data Analytics and AI at Aadhar Housing Finance Ltd.? Are you witnessing any revolutionary change?
Ans: If ONDC (Open Network for Digital Commerce) and OCEN (Open Credit Enablement Network) come to fruition, it will create vast opportunities for borrowers to avail loans by commoditising sources of funds.
Also Read | Analytics Augmenting the Game for BFSI
In parallel, there will be a deluge of data available to lenders to evaluate applicant credit-worthiness, and to understand customer’s payment propensity for collections. Imagine an almost perfect data economy, where lenders are vying for customers, and the best service providers riding on advanced insights leveraging AI/ML & emerging technologies are winning in the market (instead of the cheapest loan providers).
The massive competition to win customers will also create micro segmentation in terms of loan interest rates – risk based pricing will get extensively fine-grained.
Innovations in customer onboarding and customer payment processing will make loan origination and collections processes much more efficient. At the end, it will be customers who will derive maximum benefits, in addition to service providers who will be able to achieve the highest customer centricity.