Optimizing Network Referrals to Identify and Recruit Credit Worthy Entrepreneurs

Microfinance lenders make limited credit offers due to operational challenges in extensive referral and screening processes. This study aims to explore and optimize a referral protocol to identify good entrepreneurs and increase their access to credit.

Recent studies have shown that while microfinance encourages business creation, the overall impact on business profits is modest at best. It is possible that greater access to credit may have a larger impact if it is targeted towards more entrepreneurial individuals or businesses with high growth potential. Microfinance institutes are offering credit to include larger individual liability loans with more extensive screening. However, it is not an easy task to predict entrepreneurial ability due to difficulty in observing and measuring it. It is also challenging to match them with the volume that the traditional microfinance model caters to. This is due to sales representatives having to trade off a more detailed assessment of potential applicants for increasing their reach to a larger market.
This study aims to develop and optimize a referral protocol to identify good entrepreneurs and increase their access to credit. Furthermore, this study will explore how social constraints may affect the quality of the referrals.
This study was conducted in partnership with an MFI in Uttar Pradesh. This study uses lessons from network theory to restructure the recruitment protocol of a lender to increase: (i) The volume and (ii) The quality of applicants by targeting more central individuals in a market, who are both better connected and have more information about others’ creditworthiness and entrepreneurial capacities.
The research design for this project involved randomly selecting ten businesses from a market and identifying 3 seeds (people) in the market (2 random and 1 central person in market network) to refer businesses from the list for an MFI loan. In half of the villages, the seeds were told that their referrals will be informed of the referee when Approached by the MFI. The other half was told that their information will be kept private. Additionally, in half of the villages, the seeds received monetary incentive for their information while the rest received nothing.

Key Findings

Micro and small business ownership continues to be dominated by men with women owners consisting of only 3.07% of the respondents. 59.63% reported at least one instance of being unable to fulfil their basic consumption in the past three months.

Central seeds know, on average, more individuals on the ‘referral list’ as compared to the random seeds. While the central seed know on average 5.73 individuals from the list, the random seeds know on average 5.29 individuals. From the people they know, the central seeds refer a slightly higher share (71.9%) as compared to the random seeds (71%). The highest share of people referred from the people known is in public-bonus (72.3%) while the lowest share of people referred is in public-no Bonus 68.6%.  The most common reasons sighted for not referring a listed business is either lack of information about that business’s performance or contrarily lack of trust in the community. Access to such information is very useful for a loan officer (representing the lender) so that the quality of application can be judged on this.

The low-cost referring mechanism proposed through this study is ideal for exporting into the current microfinance ecosystem. It is easy to implement and can aid financial institutions in refining their process for identifying creditworthy individuals for SME loans. At the core of this referral protocol is the community and its social network to generate information to make screening and lending for SMEs more profitable. It can also be complemented with people’s perception of debt in neighbouring areas to see if there are still areas with high potential entrepreneurs and credit need. 

Thematic area

Financial Inclusion, MSME and Entrepreneurship

Project Leads

Emily Breza, Arun Chandrasekhar, Francisco Muñoz


Uttar Pradesh


Sonata Microfinance