Shubh Loans is a vernacular app that is helping a growing number of salaried employees in getting a loan of up to Rs. 5 Lacs with a long term (up to 4 years) EMI tenure. The smart credit model deviates from the traditional model (where the individual’s repayment capacity is the sole criteria), and replaces it with the Shubh Loans credit model (which analyzes repayment capacity of and intention-to-pay by the individual).
Rahul Sekar is the Chief Technology Officer and Co-Founder of Shubh Loans. He is an IIT Madras alumni, previously heading portfolio analytics and modeling for Goldman Sachs Asset Management in Bangalore. He has an expertise in the use of data modeling to assess risk and evaluate credit worthiness.
In an interaction with IncubateIND, Rahul Sekar talks about the role of new technology in improving the loan process. Read On!
How Big Data and Artificial Intelligence are changing Online Lending?
It is still early days for Big Data & Artificial Intelligence in lending. There are parts of the lending process that these emerging technologies are helping. Identities of potential borrowers are verified digitally using video analysis & face/voice recognition. Financial documents like bank statements, payslips, address proofs etc. are digitized using image recognition & OCR technology powered by AI. Customer support has been automated using voice recognition & chatbots. All these help reduce TAT and provide a truly real-time experience to borrowers.
One area that is yet to mature is the lending decision (underwriting). In the not-so-distant future, all of the borrowers details like identity, financials, spending patterns, tax records, criminal records, social data etc. will be analysed by intelligent machines that have learnt the rules of lending from historical data. As of today, firstly, the custodians of these data (Banks, utility providers, social media etc.) are trying to figure out the privacy framework to share data with lenders with users consent. Secondly, the machines that underwrite are executing rules written down by smart people who have analysed whatever data is available. True AI would mean that the rules are learnt directly by the machines without human intervention.
How tech-enabled alternative lending is bridging the gaps in credit systems?
Gaps in credit systems exist for two primary reasons. First is the non-existence of traditional data that lending institutions have gotten used to for underwriting. Second is the high cost of lending operations make certain type of loans and target segments unviable.
Technology is solving both of these problems. Data science & AI powered by alternative data is able to build lending decision engines that no longer rely exclusively on traditional data. End-to-end paperless processes are bringing costs down that open up previously credit excluded segments.
How will new technology improve the loan process?
The lending process is broadly divided into 4 parts; acquisition, underwriting, documentation & collections. Technology is improving all of these parts.
People are spending a lot of time online means that acquisition of customers is friction less. Submitting digital documents with a few clicks/swipes/taps is very convenient. Data science & AI are transforming the traditional art of underwriting into an objective model that is cost effective. Digital signatures and eKYC are making documentation instant. Gamification ideas are transforming collections by motivating the borrowers to maintain a healthy credit record and reduce NPAs.
Can blockchain and related technologies solve India’s huge lending gaps?
Blockchain is a transformational technology. Much like the internet & GPS were in their early days. We tend to overestimate their usefulness in the short term and underestimate them in the long term. The first use case for blockchain will be digital currencies. It will take at least a decade for this to mature and for people to transact freely. Lending will be easier once currency transactions go digital with blockchain. However we must not expect anything on this in the near future.
One other blockchain use case in lending is the data in the credit-bureaus. These bureaus are a central repository of all loan repayment data in the country and worldwide. We could create a public ledger of this data based on blockchain. This will reduce bureau related costs for lenders. Here is a startup idea!
How blockchain technology will transform P2P lending?
Blockchain can help decentralize the lending & repayment data, removing the need for an intermediary. However the risk assessment of a potential borrower is a difficult one that needs an agency. The other major problem faced by the P2P lending industry is regulating the source of funds from an anti-money-laundering standpoint, which blockchain will solve indirectly when currencies go digital.