Name of Innovation: ‘Vaanijya’-A sustainable model for micro-credit

solutions

Team/ Individual Name: Vaanijya

Members of the Team: Akshay Raghuwanshi, Shobhit Gupta, Soumyadeep Majumdar, Vivek Kumar

Name of College and City: IIT-Kharagpur

Course of specialization: Humanities, Electrical, Civil

Year/ Batch: 2014

Name of the Professor/ Placement officer involved in this project: Prof. N.C. Nayak

Stage of Innovation

• Prototype: The mobile application is currently in the Beta phase of its development after successful testing of its model and algorithms.

Idea Prototype Established Scaling

The Innovation

Chosen industry: Finance

Chosen technologies: Mobility and Analytics

Objective – Vaanijya aims at devising a self-sustainable model for providing risk management solutions to the

Micro-Finance Institutes(MFI) by promoting financial inter-dependence among the rural community.

Its three main objectives are:

Risk-minimizing solutions for MFI’s.

Expanding market outreach targeting rural community

Self-help groups formation to ensure effective loan repayment

Approach:

Expanding market outreach for MFI’s via data mining using government-issued AADHAR cards/NPR data.

Utilizing postal and telecom services for marketing micro-credit loans

Algorithm based group formation of borrowers based on geography and profession

Customized credit score for filtering the borrower database

Priority - based reallocation of MFI funds based on loan repayment history

The Innovation(cont..)

Describe the innovation:

Vaanijya is a mobile based application that focuses on end to end microfinance solutions. It integrates information available via AADHAR cards,

National Population Register(NPR) and Credit Bureau in its database. Postal/Telecom services are used to create awareness thereby encouraging

registration of the people of a particular area with the MFI. The registered data is then used by Vaanijya to calculate a customized credit score

based on personal and loan variables such as loan default history, number of outstanding loans, financial condition of the individual etc. to filter the

database for ensuring participation of people above a minimum credit score. This updated database groups the borrowers of a particular profession

together and the MFI funds are allotted to each group. Group repayment is ensured using the Grameen self-help group model which ensures

timely repayment before consideration of a reissue of loans by a particular group. For reissuing loans, the MFI funds are prioritized and re-allotted

based on previous repayment history of borrower groups. Integrating self help and priority based models ensure minimal risk for the MFI funds and

establish a sustainable system of microcredit lending in the long term.

Compelling Need

Market penetration: Huge potential market Resources not utilized efficiently Lack of financial literacy

Operational Efficiency: RBI proposal for interest-rate cap of 26% Existing loan disbursement is too manual and

resource intensive Conservative lending leads to loan labyrinth

Risk Minimization:

Present client default rate of 22% out of which 27% default willingly . Number of loan defaulters are estimated at around 9.2 million with loan overdue amounting to 4000-6000

crores (Mani, 2010). Creating self-help groups helps in alleviating credit risk

Impact

Vaanijya is first of its kind application which will integrate current technological innovations in the MFI

working structure to bridge the existing gap between its market outreach and operational efficiency. The

expected impact of Vaanijya will be:

• Enhancement of the security of high-risk transactions for MFI funds

• Increase in the outreach of microcredit and financial services to the needy

• Improvement in the financial health of individuals

• Promotion of small scale businesses

• Discouragement of conservative lending practices

Metrics:

The success of Vaanijya would be gauged by the following factors:

• Loan repayment growth rate

• Increase in the number of people registering for loans

• Increase in the amount of total outstanding loans

• Cost to Borrower ratio

• Return on Investment

Impact(cont..)

Sustainability:

• The algorithmic framework for Vaanijya is based on dynamic prioritization of industry with respect to macroeconomic conditions. This model

ensures that MFI funds are lent mainly to industries which showcase a healthy rate of return thereby suggesting a long term appreciation in MFI

lending capital

• Vaanijya aims to promote self-help groups as a means for achieving long term stability. Based on the study of building social business models

(Yunus, 2010), it is known that individual borrowers have a higher probability of a loan default as compared to group based borrowing. Making

borrowers inter-dependent by forming self-help groups not only ensures timely repayment for the MFIs but also encourages effective loan

management for the group.

• The lender database is updated after the maturity period of every loan. This database is utilized for client retention by updating them about new

loan schemes thereby ensuring demand side pool for the business.

Implementation

Schematic Approach

Credit Score

GUI

Timeline

Challenges and Solutions

𝑪𝒓𝒆𝒅𝒊𝒕 𝒔𝒄𝒐𝒓𝒆 = 𝜶𝒂𝟏 +𝜷𝒂𝟐 + 𝜸𝒂𝟑 + 𝜹𝒂𝟒 + 𝝐𝒂𝟓 + 𝜽𝒂𝟔

𝒂𝟏, 𝒂𝟐, 𝒂𝟑, 𝒂𝟒, 𝒂𝟓, 𝒂𝟔 are the 6 financial parameters shown on which credit scoring is done,

α, 𝜷, 𝜸, 𝜹, 𝝐, 𝜽 are weights based on experimental data and its empirical study

Challenges and Suggested Solutions

Challenges:

• Collection of data from national records such as Aadhar and NPR from the government machinery may be subject to procedural delays.

• Borrowers may be hesitant to adopt responsibility for covering loan dues for others in the group in case a member default.

• Credit scoring model restricts the ability to reach the extremely poor.

Solutions:

• Alternative sources of data extraction such as municipal corporations may be explored

• Group based lending is promoted by informing the lenders about the risk mitigation and effective loan management solution provided by

Vaanijya. In case of a request for an individual loan only, the lender needs to have a credit score above a set minimum.

• Vaanijya shall provide the filtered list of the individuals who have a poor credit score to the MFIs so that they could be invited to MFI organized

workshops for improving their credit scores.

Competitive Advantage

Competitive Innovation:

Vaanijya will have a first mover advantage as it is trying to create a niche by being a MFIs service provider using data-analytics and mobility as a

tool. New technological adoption by MFIs via Vaanijya will give an edge over existing practices in different ways such as :

• Low-cost marketing for a large area via postal/telecom services

• Fully integrated end to end solution for microcredit loans

• Industry based grouping on borrower's side

• Instant decision for loan disbursement based on credit score

• Systematic way of fund utilization as given for a particular sector

• Financially inter-dependence of similar group-members will influence loan repayment capacity of an individual

Patent:

• Under Indian Penal Act, 1976 an algorithm can not be patented. However, we plan to copyright our mobile application.

Source of guidance:

Vaanijya is constantly guided and mentored by Prof. N. C. Nayak , Humanities and Social Science Department, IIT Kharagpur

References:

[1] http://www.mftransparency.org/

[2] http://www.mixmarket.org/

[3] M-CRIL Microfinance Review 2012: MFIs in a Regulated Environment

[4] Mani Arul Nandhi, Incidence of Loan Default in Group Lending Program, Microfinance Researchers’ Alliance Program, Center for Microfinance,

Chennai

Thank You