A majority of Indian small merchants come under immense financial challenges as they don’t have access to established financial institutions for lack of bureau record,” says Manish Khera. Which is why he founded Happy Loans in 2016. The organisation, started by Khera and Gautam Ivatury, aims to empower the 600 million+ underbanked population in India by offering products that can be directly operated digitally, minimising the documentation and KYC verification requirements.
With over 25 years of experience in the banking and financial sector, Khera has been an investor and advisor to various banking, technology, and impact related ventures. Here’s chatting him up.
How does Happy Loans work?
Happy Loans is a 100 per cent digital lending platform based on AI build with more than 1000 variables about retailers in real-time. We have partnered with merchant aggregators who enable digital payments for the merchants and offer credit lines to micro enterprises. These are largely retail stores selling grocery and apparel, and restaurants, chemists and so on. The vision is to create a socio-economic impact on the lives of millions who cannot borrow from mainstream lending authorities. We are one of the champions of change by Niti Aayog.
What are some of the challenges that you initially faced?
Thanks to the technology, we haven’t faced much challenges. The repayment is automated from the lendee’s wallet/POS sales. We served 8,000 loans with a repeat rate of 56 per cent merchants borrowing again from us proves that the product is well received by the market. Our instant disbursals have impressed them so much that they have stopped seeking credit lines from unstructured sources.
What kind of background check do you do before loan disbursal?
Happy Loans invented a statistical modeling-based analytics engine which takes the relevant customer data from merchant aggregators like POS machine vendors or BC network, apply the rich insight of the partner from over 1000 variables and deliver instantaneous results facilitating and expediting the disbursal process. This programme can best judge both, the ability and willingness of credit-seekers to repay. The artificial intelligence accounts for all the 1000 variables of the merchants’ business starting from the sales, nature of business, region, climate, lean and peak season, repayment behaviour and so on.
What role does technology play in such a business model?
Technology is the backbone of this model. With R&D efforts we have created a real-time appraisal of potential borrowers with its proprietary statistical modeling and machine learning-based analytics engine. The engine blends a wide range of data sets from multiple sources to reduce them into comprehensibly fewer numbers of meta-variables. It helps us a great deal to analyse the profile along with the business model of a lender and gives us a projected result on the said individual.
What has been some of your key observations about the market when it comes to borrowing money?
According to our analysis, these are our observations about the market when it comes to borrowing money:
Business Expansions: The amount might be very small, but due to lack of credit score and sometimes proper documents, these small time merchants can’t avail the loan from banks, thus a majority of them come to us for loans.
Infrastructure of their establishments: When the business is growing, they need bigger space to stock more products, thus these small loans help them sustain the business.
To increase working capital: To manage their day-to-day operations small businesses sometimes need loans to meet their daily operations’ needs until their earning assets are sufficient to cover their working capital needs.
Cash Flow: Cash flow is always a challenge for a small business as they have unsold inventory that needs to be moved to bring in new products. To maintain the ratio, cash flow needs to be in place to manage these operations.
To improve term for bigger loans: Since a majority of these businessmen don’t have credit score, the short term loan helps them in building a decent credit score which gives them the chance to strike a better deal when they need a bigger loan in the future.
What are some of the areas you are going to focus on in the coming years?
We are focusing on increasing the partnerships to scale our offering to the mass market in India. We are talking to some of the large players in the industry and are hopeful to revolutionise the lending ecosystem with our ‘happy offerings’. Our machines will be more efficient to service a larger audience.
