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Project Stage:
$1,000 - $10,000
Project Summary
Elevator Pitch

Concise Summary: Help us pitch this solution! Provide an explanation within 3-4 short sentences.

Despite access to micro-credit for years, rural micro-entrepreneurs are not getting out of the cycle of debt or poverty on account of small loan sizes, higher interest rates and shorter repayment tenures for borrowers. To equip financial institutions manage the default and enable them to lend on terms more favorable to micro-entrepreneurs, Shivani is building real-time credit scores for borrowers.

About Project

Problem: What problem is this project trying to address?

Micro-entrepreneurs in the urban and peri-urban areas run small, home-based businesses such as tailoring, selling flowers, snacks, fishes, vegetables, fruits etc. These micro-entrepreneurs don’t own assets that could be used as collateral and hence end up getting smaller amounts ranging from 10,000 INR to 50,000 INR. Though the repayment capacity may be there, they don’t have credible records (as most of the transactions are in cash) and as a result the lender hesitates to lend bigger amounts of money. Besides they also charge a higher interest and keep the repayment tenures shorter to cover their default risk exposure. All of these increase the pressure of repayments for these business owners and many a time they end up going from the first loan to the second to cover for the repayments and thus end up in the cycle of debt and poverty. On the other hand, - micro-finance institutions (MFIs), conventional banks lending to micro-entrepreneurs and similar non-banking financial companies are unable to reduce interest rates on account of increasing portfolio administration costs and more importantly, risks of default. For instance, India, which comprises one-third of the global microfinance market, the growth between 2005 and 2010 was 62% per annum for unique borrowers and 88% for loan portfolio growth, but lending institutions have experienced decreased profits due to rising defaults, new regulations and an increase in required reserve minimums. Every lending institution has its own proprietary method of verifying the creditworthiness of borrowers. Such methods typically longer to turnaround and involve significant working capital and labor outlay. In micro-lending, shorter turnaround times and lower costs are very crucial because the business is a high-volume and low-margin one. And auditing existing portfolio is even more expensive and less fool-proof. According to a recent report on India by Microcredit Rating International Limited (MCRIL), the cost for administering micro-credit rose by an average of 33% in 2011 (MCRIL, “Microfinance Review 2011 Executive Summary - Anatomy of a Crisis, a Financial & Social Analysis “ November 2011). While these statistics may be partly attributed as a growth pang resulting from high growth of loan portfolios, they are primarily due to a significant rise in defaults and cost of portfolio management. The increase in defaults and administrative costs is in large part due to the lack of reliable ground-level data. While MFI’s have depended on groups formed on the basis of trust (SHGs), it is limited to the extent it acts as a security against default. It does not provide lenders adequate diligence or security to lend on terms that are on par with other borrowers. Lenders need high quality, credible and efficiently collected data about the borrowers to mitigate loan risk and approve micro-borrowers. For example, when a Venture Capital Fund lends money to a corporation, the fund compares that company's books and financial ratios such as price/ earnings, price/ sales etc. with that of companies doing similar business to decide on the rate that the fund should lend money to that company. The lack of key credit and borrower information, and poor data quality are major contributing factors in the declining portfolio credit quality. However, collecting such data would only increase administration costs for lenders and there has been no serious effort towards building this data in a systemic manner from other institutions. The micro-entrepreneurs who borrow for their small home-based business (such as tailoring, selling flowers, fishes, snacks, fruits etc.) don’t keep credible records of their cashflows, and profits. This makes it even more difficult for the lenders to assess the creditworthiness of such borrowers. All of these have resulted in increasing default rates and audit costs for these lenders. These ultimately get translated to higher interest rates and unfavorable repayment tenures for the borrowers.

Solution: What is the proposed solution? Please be specific!

Shivani believes, the underlying reason why MFIs are not equipped to create favourable products for the unorganized poor (unlike what they do for the middle class) is the lack of credit scores - information about the borrower's credit history, his business financials and what capital markets call 'comparable ratios' of his business. This lack of credit scores leads directly to 'lending risk' for the lender and thus results in 'higher interest rate' for the borrower. Through InVenture, Shivani aims to create credit scores for such rural borrowers (existing and potential borrowers) primarily via an easy-to-use mobile-application called InSight. Existing borrowers and potential ones are trained to use InSight through which they are encouraged to log and keep track of their personal and business cashflows (in and out) daily and to generate and analyze periodical reports of their financials. InVenture then passes this information to institutional lenders - who now armed with more data about the borrower, his business and its comparables - feel more comfortable about lending and charge lesser interest. Thus for the first time in any developing economy, unorganized workers are being incentivized to keep records and such a real time data is being created. Shivani believes this will go a long way in empowering them with financial literacy and most importantly create verifiable records for lenders to verify before and after lending. InSight is currently in use in three states in India through five NGO partners and three paying financial institutions accepting InSight scores. Over 4000 individuals are using InSight, with a 80% conversion rate (first-time borrowers who start using InSight pre-loan) and 66% daily usage among InSight users. On average, InSight users experience 30% increase in revenues and 6% increase in savings. Besides, lending institutions are now able to increase their revenue margins, forsee defaults to take corrective action and reduce costs of audits. InVenture is initially targeting the 66 million small business owners in India, with plans to enter other emerging countries in the next 3-5 years.