This study analyses how existing information on clients of Chit Funds can be used to predict their creditworthiness and savings/borrowing needs.
Chit funds are a form of Rotating Saving and Credit Association (ROSCA) prevalent in India. In this institution, a group of individuals pool in equal amounts of money at a fixed frequency, and at every time period a round of competitive bidding takes place among the individuals to identify a borrower for the collected amount. In order to run successful schemes, Chit Fund companies need to be able to evaluate and predict the repayment behaviour and liquidity requirements of potential clients. Chit Fund companies in India often rely on private information to make this judgement. This project aims to analyse how existing information on clientsof Chit Funds can be used to predict their creditworthiness and savings/borrowing needs.
This pilot aimed to design an algorithm that would analyse the demographic and socioeconomic information, as well as the financial track record of old and prospective clients to predict their credit worthiness
and savings or borrowing needs in order to optimize group formations. The overall analysis is retrospective, that is, all analyses were carried out using historical data. The data was collected from a group of five different companies located in various parts of Tamil Nadu, Andhra Pradesh, and Delhi. The time range for which the data was available ranged from 5 to 13 years across different companies. After sifting the complete data for the best possible collection of information, the payment records of about 5,000 individuals were tracked over a period of 13 years from two different companies.
The study found that very little demographic data is being collected by most of the companies. Moreover, only around 30 per cent of the data is digitized, which means that the rest of the data is difficult to access and is highly unorganized. There is also a pressing need for standardization of collateral and surety requirements. There is a high variation in the number of sureties collected from members, ranging from one to six. We also find that the most used collateral is that of other chits that the members participate in, indicating that the members might be strategically using the chits to make profits.
The findings will be helpful in advising and guiding chit companies on collecting, using and evaluating their client data to efficiently assess a participant’s creditworthiness and optimize the member composition of a chit scheme. However, to scale up the credit scoring model developed in the project, there is a need to examine existing data available with companies and standardise it.