Shivani Siroya
Ashoka Fellow since 2013   |   India

Shivani Siroya

Shivani Siroya is creating a simple yet powerful tool to develop real-time credit scores that helps lenders better estimate micro-borrowers’ creditworthiness and lend at more comfortable terms.…
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This description of Shivani Siroya's work was prepared when Shivani Siroya was elected to the Ashoka Fellowship in 2013.


Shivani Siroya is creating a simple yet powerful tool to develop real-time credit scores that helps lenders better estimate micro-borrowers’ creditworthiness and lend at more comfortable terms. Shivani has taken her tool beyond micro-loans and has started to help urban lower- and middle-class entrepreneurs in the informal sector and their families access other projects, such as affordable housing.

The New Idea

Through Tala (formerly InVenture), Shivani is determining credit scores for existing and potential rural borrowers primarily via an easy-to-use mobile application called InSight. Borrowers are trained to use InSight and are encouraged to log and track their personal and business cash flows (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 the particulars of the borrower and their business, are more comfortable about lending and charge lower interest. Thus, for the first time in any developing economy, informal sector workers are being incentivized to keep records and, in the process, real-time data is being created. Shivani believes financial literacy will allow workers to feel in control of their financial situation and most importantly, create verifiable records for lenders to monitor real-time performance of portfolio post-lending.

InSight currently functions in three states in India through five citizen organization (CO) partners and three paying financial institutions accepting InSight scores. Over 4,000 individuals are using InSight; there is an 80 percent conversion rate in first-time borrowers who begin using InSight pre-loan and a 66 percent daily usage rate among InSight users. On average, InSight users experience a 30 percent increase in revenues and a 6 percent increase in savings. Concurrently, lending institutions are now able to increase their revenue margins, and are able to foresee defaults to take corrective action and reduce costs of audits. InVenture has ambitious plans: initially targeting the 66 million small business owners in India, with intentions to enter other emerging countries in the next three to five years.

The Problem

Micro-entrepreneurs in the urban and peri-urban areas run small, home-based businesses providing services such as tailoring or selling flowers, snacks, fish, and fruits and vegetables. These micro-entrepreneurs don’t own assets that could be used as collateral and thus end up receiving smaller loan amounts ranging from 10,000 INR to 50,000 INR (US$185 to US$920). Though they often have repayment capacity, they don’t have credible records (as most transactions are in cash) and as a result, the lender hesitates to lend larger amounts of money. The lender also charges a higher interest and keeps the repayment tenures shorter to cover their default risk exposure. All of these actions increase the pressure of repayments for these business owners and they frequently end up paying off one loan by taking out another and thus end up in a cycle of debt and poverty.

On the other hand, MFIs, conventional banks’ lending to micro-entrepreneurs, and similar non-banking financial companies are unable to reduce interest rates due to increasing portfolio administration costs and more importantly, risks of default. Between 2005 and 2010, India, which comprises one-third of the global microfinance market, grew 62 percent per annum for unique borrowers and 88 percent for loan portfolios. Lending institutions have experienced decreased profits due to rising defaults, new regulations, and an increase in required reserve minimums. According to a recent report by Microcredit Rating International Limited, the cost for administering microcredit rose by an average of 33 percent in 2011.

Every lending institution has its own proprietary method of verifying the creditworthiness of borrowers. Such methods take longer to turnaround and involve significant working capital and labor outlay. In micro-lending, shorter turnaround times and lower costs are crucial because the business is high-volume and low-margin. Additionally, auditing an existing portfolio is even more expensive and less effective. Although the MFIs want to transition from “trust-network” based lending to larger personal lending, they have been wary due to the lack of an efficient way to estimate a borrower’s creditworthiness.

Lenders need high quality, credible and efficiently collected data about 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 financial records and ratios such as price earnings and price sales with that of companies doing similar business to decide on the rate that the fund should lend money to that company. Poor data quality and the lack of key credit and borrower information are major contributors to the declining quality of portfolio credit.

However, collecting such data would only increase administration costs for lenders and there has been no serious effort toward building this data in a systemic manner from other institutions. The micro-entrepreneurs who borrow for their small home-based businesses 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.

The Strategy

To influence MFIs to lend on more flexible terms and provide cheaper access to capital, Shivani realized three critical aspects to borrowing and lending. First, borrowers need cash flow records and credit scores. Second, micro-entrepreneurs need to be equipped with financial literacy, and finally, record keeping behavior need to be instilled and practiced in these communities.

InVenture partners with lending institutions like Vistaar Finance and Muthoot to understand the communities they serve and identify the people best suited to use InSight. Typically those are community members who currently don’t have credit scores or previous credit history, or those seeking to move from smaller loans to bigger loans and therefore need the most financial assistance. Once these potential users are identified, an InVenture team partners with the local CO or SHG to provide understanding on what InSight can offer, training on how to use it, and its advantages as part of a general financial literacy training. The curriculum includes basic financial vocabulary, how to distinguish between personal and business expenses, how to categorize different kinds of expenses (i.e. transport or labor) and how to calculate revenue, loss and profit in addition to training on how to use the InSight SMS application.

Maitris, meaning “trusted friend” in Hindi, are local female entrepreneurs at the center of InVenture’s delivery model. These women serve as the eyes and ears of InVenture on the ground and they are responsible for ensuring existing users keep accurate records as well as recruiting new users. Additionally, their payments are linked to the number of new user signups (one-time payment of 5 INR per new user) and to the continual use of InSight among existing users (these are users who send at least 20 SMS a month equaling about 20 INR per existing user). Because they are dependent on familiar and trusted networks of Maitris, InSight has the potential for greater traction in a community.

InSight is best understood as a suite of three different pieces working in concert together: the mobile application, a database and related algorithm, and web portals and dashboards both for data entry on the ground and for lenders to monitor borrowers’ progress in real-time. To use InSight, the users need to send single daily SMSs to a local number. Using a basic combination of numbers, this message records detailed information about expenses and revenue for that day. For example, a message of “2000 1000F 50T” would indicate 2,000 INR (US$32) of income and 1,000 INR (US$16) of food-related expenses and 50 INR of travel-related expenses. Users can also view their aggregated income, expense and profit information at any point in time.

A separate application connects with the database and applies their proprietary algorithm to the data to calculate a score (with 30 days’ worth of data from the user) that predicts each user’s ability to repay a loan. Shivani’s team uses independent regression analysis to judge variables that best predict default rates and is constantly rebuilding the algorithm that uses machine-learning artificial intelligence to continually refine the scores based on new data.

The final complementary components of the InSight product suite are a series of web portals and dashboards that help with data entry on a grassroots level, Maitris’s tracking of the activities of users they are responsible for and clients’ direct monitoring of their users. Currently, the team is developing a “Maitri dashboard” that connects to the database and allows the Maitris to see how often and accurately their users are reporting data. An “InSight dashboard” that allows lending institutions to access their clients’ data in real-time is also being developed.

InSight’s back-end design allows for alarms to be set if suspicious data or inconsistent entries are logged. In such an instance, InVenture sends a field auditor to work with the Maitri. Once the audit confirms fraudulent behavior, the entire group led by the Maitri will be put under a probationary period of two weeks and their credit scores will go down. To validate the data collected, InVenture performs random audits of 5 percent of the sample size on a monthly basis, where they verify household assets, equipment, raw materials and inventory. Additionally, they have been able to create baseline metrics for various households and businesses based on geography and other demographic indicators that allow them to pinpoint outliers very quickly. Lastly, InVenture also pulls in verifiable data points such as SIM card top offs, absenteeism, chronic diseases, household physical assets, rent, and school fees.

InVenture currently targets end users who are transitioning from group to individual loans. Typically seeking loans in the range of 35,000 INR (US$570) to 300,000 INR (US$4,882), these persons make most transactions in cash and currently have low levels of financial literacy, some or no previous credit history and have been rejected before by lending institutions. InVenture has also started talking to some of the bigger lending institutions such as ICICI Bank to explore ways to standardize and simplify credit scoring mechanisms at a country level. In addition, InVenture is already generating interest from micro-lending institutions in other developing economies.

InVenture is structured as a hybrid of a US non-profit, InVenture Foundation (IVF) and a US B-Corp, InVenture Capital Corp (ICC). While IVF manages the financial literacy and community building aspects of the work, ICC owns InSight and transacts with lending institutions. ICC’s primary revenue streams are via generating potential leads and auditing existing loan portfolios for lenders.

The Person

Shivani grew up in India before moving to the US for high school. It was in high school when public health issues first appeared on her radar. One of her friends had lost her mother to HIV and Shivani saw that as her friend was trying to cope with the loss, she also had to deal with mistreatment from her fellow students in class. Stunned by how in an advanced economy like the US, HIV was still taboo, she rallied around her friend and helped raise school awareness by making the students active participants in the process. Completing her undergraduate degree from Wesleyan University in 2004, she worked on equity research with UBS, tracking stocks of companies in the healthcare consumer space. Although she thoroughly enjoyed understanding the metrics behind why businesses are successful, she felt something was amiss.

Shivani left UBS in 2007 to work with a small CO focused on sex workers in the Devadasi community in south India. She realized that women, once seen as religious figures pre-independence, were suddenly seen as prostitutes and marginalized. Shivani realized how public perception impacts the livelihood and economic opportunities of communities.

To better understand both the importance of self-sufficiency in terms of livelihoods and the need for public access to healthcare, she pursued her master’s degree in health economics and finance at Columbia University. Shivani wanted to understand how these two issues worked in conjunction to impact the healthcare sector. For example, one needed to be healthy in order to be productive and one needed to be productive in order to access healthcare. After her degree, she worked with the UN. Here, Shivani worked alongside an economist focused on South Asia and sub-Saharan Africa who was building costing models for reproductive health and microfinance programs in different countries. On one project, Shivani analyzed how micro-entrepreneurs were running their business and how the extra cash inflow affected their business and household spending patterns. She recognized that once these micro-entrepreneurs took out a small loan to start a business, they continued to take out more loans to keep the business running and to take care of the interest payments. Shivani knew that there were two reasons for this: first, unfavorable loan terms (smaller loans, higher interest rates and shorter repayment tenures) and second, that these micro-entrepreneurs lacked financial literacy to effectively manage cashflows and expenses.

After the UN, Shivani returned to the US and joined an investment bank as part of their mergers and acquisitions team. While assisting the team with financial matters such as bookkeeping and creating valuations, it occurred to her that while lenders were willing to finance a mergers and acquisitions transaction to figures as high as $650 million, they turned away from lending a meager $1,000 to a rural entrepreneur primarily due to lacking a credit score. She began to connect the dots and founded the InVenture Foundation and worked in parallel part-time.

Shivani sought to test her hypothesis and pilot an initiative. She screened and conducted experiments on her design with 12 micro-entrepreneurs to understand potential risk. Using her savings, she lent to borrowers and her loans differed from the existing models in three ways: she provided longer tenures of the loan, offered lower interest rates and tracked their progress after receiving the loan by working with the entrepreneur daily on his/her business. After 18 months, her venture had 100 percent repayment rates and higher cashflow generation from all the micro-entrepreneurs involved. This success proved that there was a need to lend on more flexible terms and empower communities with financial literacy by providing them with business skills. Rather than launch another MFI, Shivani felt it would be more effective for her to equip existing MFIs with the critical data they need to challenge their traditional forms of lending.

Shivani determined the need for such data and services within the scope of 85 lending institutions of all sizes and their willingness to pay for InSight. She then transitioned full-time to InVenture.

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