Modelling credit and savings behaviour of chit fund participants

1.1 Why credit scoring?

Banks and other financial institutions face myriad types of risk. The primary form of risk attributed to a banking or financial institution is credit risk—the risk associated with a borrower becoming unable to repay his/her debts to the institution. Therefore, a crucial pillar in the existence of the modern banking and financial industry is its ability to ascertain the risk attributed to it by its borrowers, and making decisions to mitigate the same.

At the onset of the modern banking and financial institutions, human judgement was the prevalent form of assessing the potential risk attributable to a creditor¹. Decisions were based on previous clientele of the lenders and a simple intuition-based system. Credit denied or granted in such a manner proved arbitrary and was solely based on the ‘gut-feel’ of the agent of the institution, who faced the prospective creditors. Over the last century, a systemic change in this process has been observed with the industry moving towards a steady reliance on statistical and mathematical techniques to assess credit risk.

This move has slowly eradicated the arbitrariness of human judgement and ushered in a system of methodological rating of credit through credit bureaus and credit scoring, whereby a prospective borrower is judged on the basis of his/her individual financial and socio-economic characteristics, rather than what the lender perceives to be historically similar behaviour to prior risky borrowers².

However, in developing countries, the absence of efficient credit bureaus makes it costly for financial institutions to collect information on clients’ credit-worthiness. Furthermore, because they rely on the relationship with their customers to assess their risk, they often do not feel the necessity.

The chit fund industry is one such indigenous financial system that mostly relies on personal judgement assessments and contacts to assess the potential clients. 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. The borrower foregoes his/her right to participate in further auctions, but continues paying his/her share of the pool till every individual in the group has collected the pooled amount. The auction process relies on the bidders’ willingness to give up a certain amount of the pool and take the rest as loan. The amount given up at every time period is shared equally among all individuals in the group. In the event that multiple members bid at the cap, a lottery is conducted amongst the highest bidders to decide the winner³.

In comparing the chit fund industry with the modern banking and financial institutions, many similarities can be drawn up. In their functions, like the banking industry, chit funds act as an intermediary to optimally mobilize funds from savers and channelize them to borrowers, and manage the repayment of loans from the borrowers so that savers may receive their dues when they make a claim⁴. Our initial field studies show that the chit fund industry too faces credit risk as their primary source of risk, much like the formal banking and financial institutions. However, over the many years that the chit fund industry has existed in the market, it has been unable to move to a standardized statistical method of assessing risk and still relies primarily on human judgement.

As observed in the banking industry, a move towards standardized statistical methods in ascertaining risk accruable to clients may prove to be crucial in establishing the chit fund industry as a credible formal financial institution and enable it to grow in a more systematic and balanced manner⁵.

1.2 Application of credit scoring to the chit fund industry

In the setting up of a typical chit fund, two primary tasks exist—one, to evaluate the credit worthiness of prospective clients and, two, assess the borrowing and saving requirements of the prospective clients so that liquidity is provided optimally throughout the duration of the scheme.

The role of any statistical process in the chit industry, therefore, would be two-fold: first, to assess the riskiness of a prospective client at the time of entry and, second, to assess whether a person is likely to be a borrower or saver and match him/her to an appropriate group. Thus, more than just a credit scoring process, the problem faced by the chit fund is an optimization problem—minimize the total credit risk of the group, and match prospective savers and borrowers such that demand and supply of liquidity is balanced.

In addressing this problem, good information about client characteristics is a necessity. Currently, chit funds function primarily on social networks⁶. Further, our primary field studies indicate that each client is introduced by existing clients. All the decisions made by the company agents, with regards to assessing credit worthiness or the borrowing or saving nature of the client rely on the relationship the introducer has with the company. Surprisingly, most chit fund companies do not collect in-depth socio-economic information of such clients, and are satisfied with the private information collected over the introduction.

From a financial inclusion point of view, these social networks appear to have great value—they inherently reduce the stringency of requirements for borrowers and savers when compared to the formal banking and financial institutions, thereby allowing access to financial products for those who were deemed unfit by the formal institutions⁷. Initial field studies show that this unique feature, however, has not been maximized upon by the chit fund industry due to the heavy reliance on human judgement for decision-making. The lack of a standardized risk assessment creates a residual risk in the selection of groups, since officers might vary in their ability to assess the credit and savings behaviour of clients, and a departure of an officer from the company would result in the loss of valuable information for the business.

The need of the hour, therefore, is a systematic collection of financial and socio-economic information from all prospective clients, and a standardized assessment and evaluation of said information to rate the credit worthiness of a client. This process would create an automated and uniform system of assessing prospective clients, supplementing the judgement of company officers, and reducing the risk exposure of the company. A reduction in the risk exposure would result in an increase in the acceptance rate of potential clients and, consequently the customer base of the company.

In order to assess the creditworthiness of clients and their savings and borrowing needs, chit fund companies should ideally shift their current primary reliance on relationship-based client assessment to a system where data-based models supplement existing assessment methods for reasons enumerated earlier. Hence, in this project, we analyse how client information, their retrospective transaction data, and their track records in the bidding process can be optimally used to predict their creditworthiness and financial needs. In doing so, the project aims to be helpful in advising and guiding chit fund companies on how to systematically collect, use, and evaluate client data to efficiently assess a participant’s credit worthiness.

In summary, the main objectives of this pilot were as follows:

The following sections elaborate on the data collection process, description of the data, the credit scoring model and the potential of scalability of the model, as well as the role of technology in reaching these objectives.

2.1. Analysis outline

As stated in the previous section, this pilot aimed to design an algorithm that would analyse the demographic and socio-economic 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.

In order to achieve the above stated goal, three primary tasks were carried out. First, there was an assessment of client information collected by companies; this included demographic and other socio-economic information, transaction records, and bidding information.

Second, the available information was used to design a predictive model that would be instrumental in measuring the risk accruable to the chit fund company and the nature of the client’s repayment characteristics.

And finally, results obtained from above were used for evaluating how the quality of predictions can be improved with improved data collection methods.

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.

2.2. Data collection process

All companies maintain transactional administrative data in two forms: primary transaction data, stored in customized database programs, and secondary client based information, usually stored in physical form.

The administrative data is collected in a four-step procedure by the company:

The above mentioned steps can be understood better with the graphical illustration presented in Figure 1. In this example the scheme duration is 20 months, and the member wins the auction in the sixth month.

Figure 1. An example of the data collection process at a typical chit fund company, where the member participates in a 20-month scheme and wins the auction in the sixth month.

Based on our field studies, we infer that most companies in the chit fund industry in India are, by nature, family-owned and cater to an exclusive set of clients. Their clients belong to closed networks, with a vast majority of new clients being introduced by existing clients of the companies. This results in a personal relationship and mutual trust between the client and the company.

Some companies, however, have deviated from the family run business model and transformed the industry with their large-scale corporate model for chit funds, and have been conducting business based on this philosophy over the last decade. This model relies heavily on agents who form an added layer of bureaucracy and act as intermediates between the client and the company.

Both models have their unique shortcomings as sources of data. For example, while the information collected through guarantor and collateral is of high value in the second model, the formality of the corporate structure forces the companies to return all forms of documentation, which could be an important source of demographic information for this study, back to the clients upon successful termination of a chit scheme. This makes a large quantity of personal demographic information unavailable. Similarly, in the family-run model, there is flexibility in the number and type of personal documents collected at the time of joining and the number of guarantors, lassitude in the recording of late payments, and management of ledger information which display irregularities that needed large-scale manual editing.

Therefore, the data collected for the study had to be extracted from the documentation created at the four steps outlined above. All physical records were manually entered by conducting data entry operations at the location of the records. Several tables thus obtained were collated, cleaned, and combined to form the final dataset. This dataset is in a panel format, where all information is organized by name of chit schemes and by name of members of each scheme.

2.3. Partnering companies and data collected from them

In our research design, we planned the analysis to be carried out on a dataset that is representative of the broad spectrum of formal chit fund users across the country. In line with this mandate, the initial intention was to find two companies that maintain good quality database and represent a large section of the chit fund industry in terms of the spectrum of members.

Therefore, companies in different states across the country—Tamil Nadu, Delhi, Andhra Pradesh, and Maharashtra were approached. Following some initial resistance, a large number of companies were willing to share their data with us. Owing to this response, we collected data from five different companies from the above mentioned states from which, after further scrutiny, focused our analyses of data from two of the companies⁸. The data collected from the remaining companies will be used to assess scalability and to understand the future requirements from the chit fund companies in order to implement a practical credit scoring model. A point of learning for us, and perhaps the chit fund industry as a whole, from this extended data collection process is that there exists an urgent need for companies to have a standardized process for data collection and management such that they will have access to greater information about their members and, consequently, have a higher level of adaptability when exposed to random risks from the members.

3.1. Overall summary

As stated in the previous section, the final dataset was extracted from two companies. This data covers an operational period of 15 years from 1996 to 2011. It consists of the transaction records of almost 5,000 individuals who have participated in a total of 274 chit schemes, each ranging from duration of 20 months to 50 months. The final format of the dataset is set in a panel form, thereby allowing the observer to follow all transactions made by each individual over the duration of the schemes that they were participating in. Over the next few pages, with the aid of descriptive statistic tables, we will draw an outline of the data collected from the companies and the basis formed by these statistics for further in-depth multivariate regression analyses.

Table 1 outlines the distribution of the schemes which constitute the dataset. This gives an overview of the characteristics of the dataset being used for the analysis.

From Table 2, it can be seen that over the last 15 years: (1) the proportion of high value schemes has been increasing; (2) the proportion of schemes with high monthly contribution has been rising; and (3) the proportion of shorter duration schemes has been rising. This implies that the focus segment of population for the companies has been transforming over the years, the chit industry is moving away from lower and middle income groups, and seeking to primarily target higher income groups. From interviews in the field, the principal reason for the above trends is that the transaction costs for running schemes of all values remains the same, thus higher value schemes allow the companies to make greater returns on their investments.

As discussed in the earlier sections, the chit fund model is highly applicable in the context of targeting financial inclusion for lower income households due to their social/relationship network lending and saving characteristics. Unfortunately, it is evident from the above mentioned trends, in order to target the financial inclusion goal, a certain amount of intervention is needed.

Both companies maintained a closed network of members. Though they maintained, what at first appears to be a near complete demographic record of their clients, a lack of regular updating of the databases restricted the information of clients available to that of what was available at their time of joining. Given the fact that a vast majority of clients stay on with the companies for extended lengths of time, the demographic information aspect of the dataset needs to be improved upon in the future.

3.2. Demographics

The following tables give us an indication of the characteristics of the demographic and socio-economic parameters of the members, in terms of gender, age, and stated income levels, as collected by the companies at the time of the members’ joining.

As observable in Table 3, the gender ratio of members of the companies maintains an overall approximate average of 60 males for every 40 females. This figure might not be truly indicative of the level of female participation in the companies, since, as stated by the company representatives, in the case of many female participants the tickets were dummy tickets—bought by a male member of the family in the name of female members. This usually occurs when the male member of the family is the only earning member and saves in the name of other members of the family.

The average age of the representative population also remains steadily above 40 years. This is indicative of how the target population of the chit industry continues to be middle-aged individuals. Combining this trend with the trends observed in Table 2, the industry seems to be targeting middle-aged individuals who fall in the higher-income groups. As per the trends observed in the Indian population growth, a majority of the population lies in the age group 15–30; therefore, future targeting of younger populations might prove to be the much needed shot-in-the-arm for the chit fund industry⁹.

The reported income levels of the members appear to be grossly understated. As per Table 2, if a large proportion of schemes have monthly contributions of greater than Rs.3,000, then, assuming contributions towards chit funds to be a form of savings and an average national savings rate of around 30 per cent, it is highly unlikely that average income levels can remain around Rs.12,000. For the future, it might be useful to collect correct income levels, or update this field regularly, since the disposable income of an individual may determine whether an individual can make the monthly contributions.

The observable trend of greater participation in higher value schemes, and grossly understated income levels, might imply that users are channelling unaccounted earnings into the chit fund industry.

Table 4 shows the type of occupations reported by the members. Private sector employees figure as the largest group, forming roughly 45 per cent of the chit fund users. Self-employed professionals constitute the smallest proportion of members at less than 2 per cent.

3.3. Member participation

Table 5 outlines the participation of members across the two selected companies. This is an interesting statistic to take note of, since the chit fund industry relies on social networks as a means of expanding business. Both companies show a significant number of members who have returned to participate in schemes after participating in one scheme.

Table 6 describes the nature of the repeat customers observed in Table 5. On average, members wait for approximately 20 months before joining a new scheme, and those customers who participate only in one scheme at a time, wait for nearly a year after completion of one scheme before joining the next. A particular point of interest is how a large proportion of repeat customers participate in multiple schemes at the same time: almost 70 per cent of the members, who participate in multiple schemes during their tenure at the companies, participate in more than one scheme at the same time.

3.4. Irregular repayment behaviour

Table 7 outlines the rates of irregularity in payments in the dataset. By definition of the chit industry, defaulters are those individuals who have not paid their dues for more than 3 months. Default behaviour can be observed both before and after a member takes the loan. The industry focuses primarily on the group of individuals who default after taking the loan, but both type of defaulters cause the company to lose money, since the company is forced to pool in working capital to ensure smooth functioning of the schemes for the long duration of time that the member defaults for.

In this case, it is evident that more than 35 per cent of the members have defaulted at least once at any point of the scheme; but, of interest to us is the nearly 24 per cent of members who have defaulted after taking the loan. Though the overall rate of default transactions remains at 4 per cent, and 2 per cent for the period after taking a loan, being able to understand what drives this payment behaviour and tackle the same is of utmost concern in mitigating risks faced by the companies.

Other than the default behaviour, we also observe a large proportion of members who make irregular payments. Late payments are those payments which are made after the scheduled time, but less than 3 months from the due date. Early payments are those payments which are made before the due date. Part payments are those payments which are partial payment of dues and lump-sum payments are those which are bulk payments for dues of multiple months.

Late payments, part payments, and lump-sum payments also add to the credit risk faced by the company. Although they do not attribute as much risk as defaults after a loan, their extremely high incidence is a cause of worry. Almost 90 per cent of members have made at least one late payment, and close to 85 per cent of the members have made at least one part payment. Early and lump-sum payments are made by almost the entire population. Summarily, irregularity in receipts from the members is of high concern.

Table 7, in combination with Table 8, describes the severity of defaults when they occur.

This table demonstrates that when a default occurs, that is, when the member has been unable to pay dues for more than 90 days they are unable to clear dues for another 2 months on average. This obviously results in the company having to provide excess working capital to the schemes where such defaults occur, thereby increasing the cost of risk mitigating.

In the following tables, we observe how default rates differ among different classes of chit funds. In this initial analysis, we consider four classes based on chit scheme value, chit scheme duration, monthly contribution level, and a combination of monthly contribution and chit scheme duration:

It appears that there is not much difference in the default rates of high and low value schemes, as evident from Table 9. In both cases, percentage of members who have defaulted at least once, overall, remains approximately 34 per cent and the percentage of total transactions that were default transactions are around 4 per cent (Table 10).

Longer duration schemes indicate higher rates of default, both in terms of percentage of members (around 38 per cent vs. 28 per cent) and number of transactions (around 5 per cent vs. 3 per cent). This is an expected result since longer schemes will allow more people to default. Therefore, assuming long duration schemes to be of higher risk based on this statistic alone might be inconclusive.

Table 11 exhibits similar trends as the defaults in the chit value case. It appears that there is not much difference between high monthly contribution and low monthly contribution schemes in terms of percentage of members defaulting. In both cases, roughly 30–35 per cent of the members default at least once. However, it seems that the rate of default transactions in high value monthly contribution schemes are more than one percentage point higher than the low value monthly contribution schemes. Therefore, monthly contribution levels might have a greater role in defining default categories.

An interesting class of schemes run by the companies are true high value schemes. These are schemes that have monthly contribution greater than Rs.3,000 and are lesser than 30 months in duration. If we study Table 12, it appears that true high value schemes differ significantly from the overall high value schemes, as seen in Table 8. There is a significant drop in the percentage of members defaulting at least once and the percentage of total default transactions also exhibits a similar trend. This might imply that individuals seeking short duration high value loans are less risky than those seeking high value long duration loans.

One observation recorded while interviewing branch managers and company owners during the course of the field study was that late payments and defaults increase in frequency around festivals. Table 13 shows no such seasonality in late payments, but defaults, especially defaults after taking prized amount, show a mild spike around the months of October and November, that is, around the time of the Indian festival of Diwali.

3.5. Collateral and surety information

The next table (Table 14) outlines the summary statistics of the collateral and surety information collected by the companies. As stated in the section on the functioning and data collection process of the companies, collaterals and sureties are collected from individuals when they borrow from the companies (that is, when they win the prized amount and lay claim to the same). However, as stated earlier, a standard benchmark does not exist for the types of collateral and surety collected and all decisions on this matter are based on human judgement, thereby bringing in an inherent bias into the system.

In the dataset being analysed, we have information pertaining to a total of 9,318 chit tickets from 274 chit schemes. This figure excludes the tickets held by the companies themselves, and all these tickets are held by the 4,936 members being assessed. As is evident above, the companies have collected collateral or surety from approximately 82 per cent of the chit tickets. The remaining 18 per cent of the members are those who claim the prized amount at the end of the scheme and, therefore, are not required to provide any collateral or surety. These are the individuals who are the pure savers in the schemes.

What is of interest, however, is the wide range of sureties that are collected—from one to six, with a mean of 3.2 sureties. This further indicates the need for a standardized procedure in assessing risk, because such a wide range of sureties implies the existence of biases towards certain types of members.

Another point of interest in this table is the type of collaterals provided by the members. A staggering 60 per cent of collaterals provided were other chits that the members were participating in. If we compare this figure, with the figures from Table 5 and Table 6, it implies that a large proportion of repeating members might be indulging in strategic financial planning using chit schemes, that is, participating in multiple schemes at the same time, using one scheme as a collateral for the other, and timing the borrowings and savings such that overall interest rates faced by the members would be minimized.

3.6. Auction information

The final tables attempt to describe the trends observed in the monthly auction process. As outlined in the earlier section, every month a competitive bidding process is carried out to determine which of the members of a given scheme collects the prized amount as a loan. The minutes of the said auctions are recorded by the companies, and this provides information on how often a member participates in auctions before winning (Table 15).

As can be seen in Table 15, the average member participates in about four auctions before being able to claim the prized amount as loan. The maximum number of attempts before a member can claim the prized amount appears to be 25. This, therefore, implies that there is a great unmet demand for liquidity. In this regard, Table 16 outlines this fact in the example of the most frequently used chit scheme type, those of 30 months duration¹¹.

As can be observed, leaving aside the first auction, in the consequent first quarter of the scheme, the first 10 auctions, all face unmet demand for liquidity, or the percentage of unsuccessful bids, at rates greater than 50 per cent. A deeper concern is that in this period of the chit scheme, all participants bid at the maximum permissible cap, and winners of the prized amount are selected by a lottery mechanism, thereby increasing the randomness of meeting the demand for liquidity.

The rates then maintain an average of approximately 35 per cent till the last quarter of the scheme, and thereafter these rates drop. The drop in the last quarter corresponds to the fact that most of the members at that period are savers and therefore have no demand for liquidity. What is of interest to our study is the high demand in the first half of the scheme.

In summary, it appears that though default rates are overall low, there are some trends that need further analysis, for example, the effect of demographics on irregular payment behaviour, the effect of various trends in savings behaviour on the irregular payments, or the effect of high rates of unsuccessful bidding on the trends observed in irregular payments. However, it is obvious that a systemic overhaul is required to address the nature of risks faced by the chit fund industry. In the next sections we shall take a more in-depth look at these trends and formulate a methodology for analysing, assessing, and quantifying the risk faced by the companies.

4.1. Where does risk need to be assessed?

The role of an average chit fund company is centred around performing seven primary tasks: (1) locating potential members; (2) collecting and processing their information at the time of joining; (3) identifying a scheme where said member would fit in; (4) collecting monthly dues; (5) conducting auctions and identifying winner; (6) assessing level of surety and/or collateral applicable for winner; and (7) disbursing loan amount.

As we have already seen in Section II, the existing procedure of identifying risky clients followed by the chit fund industry rallies around the collection of, what usually proves to be incomplete information at Point (2). Further on in the scheme, at Point (6), the assessment of risk and therefore the levels of surety or collateral required are again based on the information provided at Point (2), consequently adding to the risks already accrued to the companies at Point (2).

Figure 2. Tasks of a typical chit fund company.

Therefore, standardized statistical methods need to enter the typical chit scheme at two points—at the point of collecting and processing demographic information, and at the point where assessment of the level of surety/collateral takes place.

At the first juncture, having a better knowledge about the new member is crucial. Demographic information, like age, income, gender, and type of occupation is collected now by the companies. This information, coupled with historical information about the member, like whether they were members prior to the current scheme that they are joining, whether they had defaulted in their earlier schemes, how many years have they been part of the company, etc., can improve the quality of assessing risk of the member.

Similarly, at the second juncture, when the member has been participating in a given chit scheme for a certain period of time, and takes part in an auction, wins the auction, and is about to claim the prized amount as a loan, another assessment needs to be carried out. At this juncture, not only will demographic information be available, but also the transaction history for the months that he/she has been participating for. Therefore, an assessment methodology that supplements decision-making processes by including the transaction history in the loop is needed.

4.2. Risk assessment at the point of entry

Due to the relationship-based screening of prospective clients, overall, the chit fund industry lacks a systematic collection of data at the point of entry. Clients are asked for basic demographic data only, and in many cases, based on the social network by which the client has been introduced to the company, even these parameters are skipped. In general, for new members, only name, age, gender, income, and occupation information is collected. In the case of old members, the companies gloss over the updating of basic demographic parameters and only state the earlier scheme(s) the member had participated in. In both cases, characteristic biases are built into the data collection process, resulting in asymmetric information for the companies: (1) lack of prior financial history from prospective new clients does not allow the companies to judge the client’s retrospective financial repayment behaviour; and (2) lack of updated demographic characteristics in the case of returning clients does not allow the companies to update existing risk profiles of the clients. Furthermore, in the case of Point (1) above, there is no information sharing mechanism that exists across chit fund companies, resulting in clients being able to hide irregularities within the industry.

Therefore, all risk assessment at this juncture needs to be based on the following parameters: (1) stated demographic indicators, that is, age, gender, occupation, and income; (2) whether the client participated in schemes earlier with the same company; and (3) whether the client displayed any irregular payment behaviour in the schemes he/she participated in earlier.

Keeping the data availability in mind, we propose a model which allows us to analyse the above stated parameters in order to identify specific determinants of increasing total defaults. Thereafter, using the identified determinants, develop a rule by which prospective clients can be rated on a scale of Low Risk to High Risk.

The dataset used for this analysis is cross-sectional—each member’s demographic characteristics and prior repayment records are aggregated member-wise.

4.3. Risk assessment at the point of claiming prized amount as loan

At the point where a client wins the auction process and lays claim to the prized amount as a loan, he/she officially accrues credit risk to the company by formally becoming a borrower. This point can also be called an inflection point in the characteristics of the client—he/she changes from being an apparent saver to being a borrower. Therefore, an assessment of risk accrued to the company by the client at this juncture is logical.

Having said that, an analysis of available data at this juncture is crucial; it can be safely assumed that the data available to the company has been greatly supplemented since the time the client joined the scheme. At the time of joining, the company has basic demographic information about the client, that is, age, gender, occupation, and income level. In addition to this, the company also maintains record of whether the client has been an earlier member of the company or not. Adding to the available data is: (1) transaction records of the individuals over the months that they have been participating in the scheme; and (2) retrospective non-prized bidding data, that is, information pertaining to the individual’s demand for loans.

The conglomerated data available for each member at the point of their taking a loan allowed us to consult existing models of credit scoring available in the formal banking industry. For example, the Fair Isaac Corporation (FICO) methodology of consumer credit scoring that is accepted as the industry standard across the world.

FICO scores are widely-used by banks around the world to assess the credit worthiness of clients. In many countries, banks are not allowed to deny loans to applicants, but are allowed to raise interest rates for clients who are assessed to expose the bank to higher risk. In the Indian context, banks and formal non-banking financial organizations use these scores to determine credit eligibility by assessing the risk level of a prospective borrower.

Since the exact scoring methodology is the intellectual property of FICO, and the exact formula is unavailable in the public domain, a break-up of the weights given to different categories of a client’s financial history is available and is listed in Table 17.

In comparing the above category heads with the type of information available in the dataset at the point of an individual laying claim to the prized amount as loan, we were able to identify variables that could be used to build a credit scoring model loosely based on the FICO methodology. This choice of said variables that are applicable to the chit fund industry, are listed in the final column.

The above variables, in addition to the demographic and prior records of participation in chit schemes of the members, were arranged in an unbalanced panel format. That is, for each member, we have data for each month he/she participated in the scheme up to the point of him/her taking a loan.

For example, in the case where risk assessment was done at the time of a member joining a scheme, the identification and analysis of determinants of risky behaviour takes place again, followed by the development of a relative score that rates members from being Low Risk to High Risk.

5.1. Regression analyses at the time of joining

The dataset used for this set of regression analyses was cross-sectional in nature, where each member’s details were aggregated to form one line of information. The analysis conducted was retrospective in nature. For each member, his/her age, gender, occupation, income level, and prior participation in terms of number of schemes he/she has completed in the given company were considered.

In this analysis, we wanted to study the determinants of defaults at the member level. Therefore, we intended to use the total number of defaults as the dependent variable, and explain what drives defaults in terms of demographic variables. That is,

                                                                                  Total Defaults = F(Demographic variables)

Thus, we aggregated the total number of defaults each member had committed in all the schemes they participated in. As we can see from the descriptive statistics of irregular payments, approximately 35 per cent of members have defaulted at least once in their participatory history¹². Therefore, in our sample space of 4,936 members, a large number of members had zero values for total number of defaults, and among the 35 per cent who had non-zero defaults there appeared to be a large skewness. To counter this, we decided to use a logarithmic transform of the total defaults variable, and consequently the equation for estimation became as follows:

                                                                           Log (1+total defaults) = F(Demographic variables)

Similarly, logarithmic transforms of total defaults before winning prized amount and total defaults after winning prized amount were also created.

To counter the effects of skewness in the demographic variables, we created a logarithmic transform of age, and categorized income into high, medium, and low levels, with the reference group being all members with missing income levels. The quadratic form of age was also considered. In a similar manner, dummy variables were created to represent male members, whereby all male members took the value 1, and female members were the reference group; and, all company tickets took the value 1, and female members were again the reference group. In case of occupation categories, each occupation group was represented by a dummy variable, with ‘other occupations’ being the reference group in each case¹³.

Following this process, we created categories based on occupation and income levels, as outlined in Table 18.

The final regression models that were estimated were as follows:

Log(1+Total Defaults) = F[Log(age), (Log(age))², Number of schemes,Dummy(male), Dummy(company), var 1…,var21]

Log(1+Total Defaults, before winning) = F[Log(age), (Log(age))², Number of schemes, Dummy(male), Dummy(company), var 1…,var21]

Log(1+Total Defaults, after winning) = F[Log(age), (Log(age))², Number of schemes, Dummy(male), Dummy(company), var 1…,var21]

The results estimated from the above models are shown in Table 19.

As seen in the above table, when attempting to identify the determinants of increasing total defaults at the time of joining, the following trends were noticed.

If a member participates in an additional scheme, his/her tendency to default at least once during the duration of a scheme increases by 12 per cent. This implies that when a person has been a member of a company for a longer duration of time, the greater the chance of him/her defaulting, indicating that there exists a significant relaxation of stringency of rules being applied to the member by the companies—a direct off-shoot of the relationship maintained by companies and their clients.

Demographic parameters—age and gender appear to have no significant effect on total defaults. However, it appears that those individuals who mention their income and occupation tend to default lesser than those who do not, as evident from the negative deviations from mean.

When looking at categories of income and occupation, the following groups appear to deviate, in a negative manner, significantly from the total defaults:

The only parameter that affects a member’s defaults before winning a prized amount is greater participation in schemes; each additional scheme tends to increase total defaults before winning by 10 per cent.

When looking at the parameters affecting total defaults after winning a prized amount, the following trends were observed.

If a member participates in an additional scheme, there is an increase in total defaults after winning of 5 per cent. As mentioned earlier, this correlates with the possible relaxation of rules with older members.

Interestingly, demographic effects have a greater significance here: an increase in the age of the member, results in an increase in the total defaults after winning at a decreasing rate; male members tend to have a 6% higher rate of defaults after winning than women; and again, it appears that those individuals who mention their income and occupation tend to have a lower tendency to default, as seen in the negative deviations from the reference group (those individuals who did not mention their income and occupation).

When looking at categories of income and occupation, the following groups appear to deviate, in a negative manner, significantly in the total defaults:

5.2. Derivation of credit score at the time of joining

Using the results from above, a few deductions about riskiness of clients can be made directly: (1) the length of time spent with a company should not be used as a determinant of stringency of rules applicable to the member, since it is apparent that these type of members display a statistically significant increase in total number of defaults; (2) youngest and oldest members are the least risky, and middle-aged members show a large tendency to default; and (3) male members are riskier than women members.

In order to quantify the riskiness observed in the above regressions, we decided to categorize individuals into various risk types based on their income and occupation categories. For achieving this, we considered the statistically significant deviation of the corresponding category dummy from the mean, that is, assuming the reference group to be all individuals who had not stated their income and occupation at the time of joining; we consider the deviation of those who did mention income and occupation. In doing so, we observe that all groups have a negative deviation from the mean, and we proceed to rank all income and occupation categories by these deviations, as seen in Table 20. Maximum deviation implies least risk, and no deviation implies maximum risk. The switchover between each risk type and score is based on whether the difference between each consecutive estimate is significantly greater than zero or not.

From the above ranking, we see that Self-employed Professionals with high income are the least risky type of members, therefore given a risk type of ‘A’ and a score of 1. Similarly, moving down the table, the riskiness increases till Rank 17, where a cluster of income and occupation categories display zero deviation from mean, and are given the risk type ‘E’ and a score of 5.

The same methodology is applied to the regression results obtained from the third model—to identify the determinants of total regressions after winning the prized amount. The risk types and scores thus obtained are outlined in Table 21.

From the above ranking it appears that those members with government sector jobs and those in the lowest income category are the least risky, with a risk type ‘A’ and a score of 1. Business owners, retired individuals, and housewives with medium income level are the most risky, with a risk type of ‘E’ and score of 5.

On combining the above two tables, we are able to derive a score for total riskiness of clients at the time of joining and this is shown in Table 22.

As per the above table, the final scores derived at the time of joining indicate that the least risky groups are government servants with low income and self-employed professionals with high income. The groups with maximum risk are business owners and retired individuals with medium and high income.

5.3. Regression analysis at the time of taking loan

The dataset used for this set of regression analyses was an unbalanced panel, where each member’s monthly transaction details were tracked till the month the member claimed the prized amount as loan. This allowed us to assess the savings behaviour of the individual until he/she becomes a borrower, and use observable transactional behaviour characteristics in identifying the determinants of defaulting as a borrower. As outlined in the previous section, apart from the demographic characteristics, each member’s transaction history will be used to assess the member’s records of delinquency, severity of delinquency, demand for loans, outstanding dues, etc., and all these, in turn, will be used to determine the probability of a member defaulting as a borrower.

For identifying the determinants of defaults in this analysis, probit models of the following form were defined and estimated:

    Pr(Default after winning = 1) = F[transaction history as a saver, demographic characteristics]

As in the earlier regression models, to counter the skewness in demographic variables, a logarithmic transformation of age was considered, dummy variables for male members and company members were included, and, finally, categories for income and occupation were created¹⁴.

The results estimated from the above models are listed in Table 23.

From the above table, some clear trends related to transaction history were observed. First, with respect to the choice of schemes, participation in longer duration and high monthly contribution schemes increases the probability of defaulting as a borrower. However, higher loan amounts (prized amounts) indicate a lower probability of defaulting.

As a member remains active in a company for a longer duration, he/she exhibits a lower probability of defaulting as a borrower. Comparing this result with what is obtained in the first set of regressions, we can infer that though an older member faces laxer rules, he/she exhibits risky behaviour only as a saver (time period prior to claiming prized amount)¹⁵.

If a member participates in a scheme for a longer duration before claiming a prized amount, he/she has a lower probability to default. This implies that if a member exhibits saving behaviour for a longer duration before taking a loan, he/she will have a lower probability of default.

In terms of payment behaviour, if a member increases the number of part (incomplete) payments, then he/she has a lower probability of default. Since part payments imply a member’s willingness to pay his/her dues, this result implies that though a person might not be able to make complete payment of dues, his/her willingness to do the same shows a reduction in the probability of default.

If a person makes late payments during his/her period as a saver, he/she has a greater probability of defaulting as a borrower. This also gives credence to the fact that bad savings behaviour gives rise to bad borrowing behaviour.

If a member defaults as a saver, he/she tends to have a lower probability of defaulting as a borrower. Conversely, if a member’s default period increases, his/her probability of defaulting as borrower increases.

Members making more lump-sum payments tend to have a greater probability of defaulting as a borrower.

Combining the trends observed in payment behaviour with the fact that being a saver for a longer duration implies lower probability of default, it can be deduced that good payment behaviour during the period a member is a saver, in other words good savings behaviour results in good payment behaviour during the period as a borrower.

Two other interesting observations from the transaction history related results are: (1) a member showing a greater desperation for a loan, that is, if a member participates unsuccessfully in a greater number of auctions, he/she will have a greater probability of defaulting; and (2) at any given point of time, if a member makes payments for additional schemes, then he/she has a greater probability of defaulting as a borrower.

With respect to demographics, trends similar to the regression results from the time of joining were observed. That is, as age increases, probability of defaulting as a borrower increases at a decreasing rate; male members display a greater probability of defaulting as a borrower; and, those members reporting income and occupation tend to default lesser than those who do not.

When considering income and occupation categories, the following groups appear to have significantly higher probability of defaults as a borrower:

5.4. Derivation of credit score at the time of borrowing

Using the results from above, a few immediate deductions can be made with regards to the riskiness of members at the time of borrowing. In other words, if a member displays, or has displayed these trends during the period of him/her being a saver, then he/she should be labelled as a risky client: (1) borrowing at the early stages of a scheme; (2) making a large number of late payments or lump-sum payments; (3) participating in auctions frequently; (4) participating in more than one scheme at a time; (5) being middle-aged; and (6) male.

Following this, we used the same methodology as that used at the time of joining, to categorize members into various risk types. Consequently, we were able to generate scores for the different categories of income and occupation as listed in Table 24.

From the above ranking, we see that Self-employed professionals with high income continue to remain the least risky category in terms of income and occupation. Small business owners with high income, self-employed professionals with medium income, business owners with medium income, housewives with high and medium income, and retired individuals with low income are the riskiest type of members.

In combining the rankings obtained above, with the rankings obtained in Table 22, we created a comprehensive score with which to rank clients. These scores, and the rankings thereof, are shown in Table 25.

As reflected in the Table 25, the least risky group appears to be Self-employed professionals with high income, and the riskiest group appears to be Business owners with medium and high income.