2
Mar

District selection and estimating the potential of the KGFS entity through GDP Mapping

By Surabhi Mall, IFMR Rural Finance

This is the first blog post in the KGFS (Kshetriya Gramin Financial Services) Model Incubation series. The objective of the series is to methodically conceptualize an approach to build the branch network while incubating a new KGFS entity or expanding to contiguous districts. The posts focus on themes that range from district selection to identification of branch locations and optimization of the distribution network.

In this post we start with a brief discussion on the choice of a district and later discuss geography-specific questions that influence the economics of the model. Finally, the post stresses on GDP mapping as a heuristic solution to those questions.

Among the early steps of setting up a KGFS model is identifying a district. District selection is mostly a matter of organisational strategy and choice. The choice may be based on parameters such as district’s rural population, its population density, credit to GDP ratio, forest cover, road density, among others. Reviewing these attributes prior to finalising the district provides an elementary sense of the KGFS’s business potential, composition of the product basket in the branches as well as the degree of customisation that may be required to set up the model. Following this, reconnaissance (Recce) of the district provides insights to questions such as – Does the model need any fundamental changes or customisation in the given geography? What is the degree of competition it is likely to face? What is the current status of customers – i.e. their access to credit, income earning potential, and savings or repayment behaviour. This step serves as a final validation to the district selection strategy.

Recce gives field-level insights on the geography and competition in the district while secondary data helps understand the demography and infrastructure availability. Since geography, demography and the availability of credit in a place have significant influence on how the local economy develops, this should provide reasonable understanding of village-level economics and perhaps its influence on the branch’s business. It may even be leveraged to formulate the entity’s business plan, its competitive strategy and the network of branches. However, is this sufficient to understand the requirements and provide suitable comprehensive financial services? Is this information adequate for each branch to realise and achieve its potential volume of business at every life-stage? In fact, how does one quantify potential of a typical service branch in the chosen district? Finally, can the impact that the branch or the entity would have in the economic well-being of people in the long-run be measured? Estimating the branch service area’s Gross Domestic Product (GDP) provides powerful insights in unpacking such questions.

District_3step_img1
Figure 1: Three steps prior to setting up a new KGFS branch

Why the GDP?

To start with, what do we mean by estimating the GDP of a branch service area? GDP of an area is essentially the summation of different economic activities that thrive and contribute to the economy of that area.

Now, a KGFS stands to ensure the financial well-being of ‘every’ customer and enterprise in the area. This dictates that each customer’s needs be identified, acknowledged and serviced. In this context, the GDP exercise provides valuable insights on the composition of different sectors that thrive in the area. This can then be used to segment customers, identify their respective needs, customise the product basket at the branch and design an apt pricing as well as marketing strategy for them. A deep dive into the village economics through this exercise enables the branch to identify and prioritize customers from occupations with high degree of cash-flow mismatches, i.e. customers who are most likely to benefit from financial services. In effect, all this reinforces the KGFS’ geographic commitment by accounting for all possible households and economic drivers in the area.

The exercise gives insights on the share of existing financial players (from the interest income generated in the area), the median profile of a customer’s household and her debt servicing capacity. At the very outset, these can serve as a filter (post the Recce) to re-assert the choice of the district and the working estimates of the business plan. These estimates can then be built into the annual and monthly business targets for the branch. The activity can also be designed to give a rich sense of actual business that the branch is expected to do, i.e. its market share1. By capturing the share of other formal and informal financial institutions in the area, one can assess the volume of competition. This can then be netted off from the estimated demand for credit, thereby giving the potential market share of the branch.

From an operational point of view, data from GDP can be used to add greater granularities to the branch’s customer management database. It also enables scope for new business development2. For the product development team, it helps estimate occupation-based credit requirement and decisions regarding risk-exposure limits.

More generally, the GDP map helps visualise what kind of financial services are required to increase the size of the pie in the first place. It is a quantifiable measure that may be used as baseline for a ‘village profile’ in order to assess the financial viability and/or impact of a branch.

When – Ex-ante or ex-post?

While the above arguments advocate executing this prior to branch opening, this may not be a binding proposition. One can customize the scope of the exercise based on the objective sought at different life stages of a branch. For example, for an existing, low-performing branch where the enrolled database isn’t representative of the area’s population, the study’s objective can be to capture the economic drivers, re-estimate the branch’s potential and identify untapped business avenues. It may also aid to gauge the share of competition of other players’ vis-à-vis that of the KGFS branch.

However, the maximum utility of the exercise would lie in leveraging it as a diagnostic tool to acclimatize with the new geography of business. Such information, when captured at the very inception stage of branch set-up can greatly aid in understanding the branch’s gross potential and scale of operations, relevant needs of potential customer and perhaps even insights into strategies to thwart current or budding competition. If the GDP study in a branch is conducted at the proposed stage of branch, re-estimation and scoping for new business development through primary studies may then become redundant. Another added advantage of doing this prior is that this can be a part of the branch staff’s training track aimed at familiarizing them to the landscape of the area.

In the long run, a time-series GDP exercise process– prior to branch opening and ‘x years’ post branch opening is perhaps going to be the only real indicator of branch performance. Branch performance in this case will not mean the business at the branch but perhaps the change in (composition of) GDP of the service area and the impact of the KGFS branch on the lives of the people3.

KGFS Impact Long Run = f (actual business and type of customers served by the branch, increase in GDP share contributed by those served in the area that can be exclusively attributed to KGFS’ operations between KGFS0 & KGFS1).
[KGFS0 – is the GDP estimate from the study in time period 0 (prior to branch opening) & KGFS1 – Is the GDP estimate from the study in time period 1 (post x years of operation)]

The next blog in the series is on the Activity-based Costing of the GDP exercise. Through the lens of GDP studies done in the past, it will attempt to provide indicative answers to questions such as time and resource costs of the exercise.


Notes:

  1. Market share of a branch estimates the actual served market/sales of a branch. This addresses concerns such as market cannibalization.
  2. Based on forward and backward linkage of value chains in the area, loan purpose and occupation type, unexplored segments, etc,.
  3. This estimation needs to account for fixed effects.


We recently hosted a series of knowledge management sessions (Spark Spring Edition 2015), as part of which Surabhi presented on this topic. In her session titled “Gross Domestic Product Mapping”, she spoke on how GDP Mapping can provide an intensive diagnosis of a defined location and how it can be used in a research process aimed at providing richer, robust and relevant information about markets, variables and potential.

View the presentation from her session below:

4
Feb

Estimating the Diversity Score of a Portfolio across Multiple Correlated Sectors: Generalized Herfindahl-Hirschman Index

By Vaibhav Anand and Ramasubramanian S V, IFMR Capital

Diversification is an effective risk mitigation strategy for portfolio risk management. It helps to mitigate risk arising from various factors, including extreme events, except factors which are systemic in nature. Often diversification across counterparties, sectors or geographies is the only risk mitigation tool available to a credit portfolio manager. It is important to measure and monitor the degree of diversification periodically to ensure that concentration risk remains low. However, quantifying the diversification may not be straightforward. Herfindahl-Hirschman Index (HHI) is one such measure but has limitations as it does not take into account correlation among underlying assets. In this post, we discuss a more general and effective measure of diversity score, Generalized-HHI (GHHI), to quantify diversification of a portfolio across multiple correlated sectors and sub-sectors.

In this post we first give a brief overview of how diversification helps to mitigate risk. We also discuss briefly the effect of correlation on portfolio risk. Next, we give a quick introduction to the classical HHI measure. In the last sections we present the GHHI formulation and illustrate its usage to identify a best diversified portfolio. In this blog post, we do not discuss in detail the derivation of GHHI and request interested readers to refer to the Working Paper for a detailed discussion.

How Diversification Helps

We discuss the benefit of diversification using a hypothetical credit portfolio of INR 1000 which can be lent to a single or multiple borrowers. Let us assume that all borrowers have an annual default probability (PD) of 5%. The portfolio manager has three options to lend the entire amount to:

(a) Scenario 1- A single borrower – No diversification
(b) Scenario 2- Equally to ten borrowers with no default correlation among them
(c) Scenario 3- Equally to ten borrowers with a pairwise default correlation of 0.33 among them

Assume no recovery.

Assuming 5% PD for each borrower, the expected loss of the portfolio in all scenarios is same and is equal to INR 50. However, the unexpected risk, measured as the standard deviation of loss here, for the three portfolios will be different.

The loss distributions of Scenario 1 and Scenario 2 are shown in the Figure 1. Scenario 1 has higher standard deviation because it has probabilities bunched only towards the ‘INR 0 loss’ and ‘INR 1000 Loss’ events which is intuitive for a one borrower portfolio- either all good or all bad, whereas a diversified portfolio, under scenario 2, has a better distribution with a very thin tail. For example, under Scenario 2 the probability of INR 1000 loss is nearly 1 in 10,000,000,000,000 as opposed to 1 in 20 in Scenario 1. Remember that Scenario 2 assumes no correlation among borrowers.

GGHI_Img1
Figure 1: Loss Distribution: Scenario 1 and Scenario 2

Let’s see how correlation impacts the loss distribution. The loss distribution of Scenario 2 and Scenario 3 are compared in Figure 2. It can be seen that the latter’s loss distribution has fatter tails. The probability under Scenario 3 of INR 1000 loss is nearly 1 in 240, much higher than that under Scenario 2 but lower than that under no diversification Scenario 1. This tells us that greater than zero default correlation among borrowers increase risk in the portfolio and should be taken into account while quantifying the degree of diversification.

GGHI_Img5
Figure 2: Loss Distribution: Scenario 2 and Scenario 3

Herfindahl-Hirschman Index (HHI)

One commonly used method of measuring the degree of diversification is HHI. HHI is defined as sum of the squares of the portfolio proportions. Consider a loan portfolio P with exposure across 3 counterparties, C_i, with corresponding proportions, c_i, where i = 1 to 3. Then the degree of diversification for P across counterparties can be measured using HHI, where HHI is:

HHI =\sum\limits_{i=1}^3 c_i^2

However, the HHI assumes the sectors are independent and does not take into account the correlation among them. We propose a more general metric, the Generalized Herfindahl-Hirschman Index (GHHI), which incorporates correlation among underlying assets.

Generalized Herfindahl-Hirschman Index (GHHI)

The GHHI formulation for a portfolio P with across n assets having pair wise correlation of \rho_{ij} can be written as:

GHHI= {\sum\limits_{i=1}^n} c_i^2 + \sum\limits_{i}^n \sum\limits_{j\neq i}^n 2c_ic_j\rho_{ij}

The derivation of the above formulation is not discussed here for the sake of brevity. We request the reader to refer to the Working Paper for a detailed account.

GHHI Vs HHI – Illustration

As an illustration the HHI and GHHI are estimated for four hypothetical credit portfolios, A, B, C, and D, with exposure in 12 counterparties across three sectors S1, S2 and S3. The counterparties are pairwise correlated within a sector with correlations of 0.05, 0.25 and 0.5 respectively. It is assumed that sectors are pairwise uncorrelated. Column 4 to 6 of Table 1 shows the exposure of the four portfolios in different counterparties.

GGHI_Img4

Portfolio A is a seemingly perfectly diversified portfolio with equal exposure to all the available counterparties. In fact, a comparison based on the classic HHI yields a similar conclusion. However, it is shown using GHHI that a more diversified portfolio can be created taking into account the correlation among the counterparties in a sector. The last two rows of Table 1 show the calculated value of diversity scores of each portfolio as well as the effective number of counterparties in each portfolio. Based on HHI, the degree of diversification of the portfolios follows the order: A > B = C = D. Whereas GHHI takes into account the correlation and provides a more accurate order: D > A > C > B, i.e. a portfolio with exposures skewed towards a highly correlated sector, such as S3, will have lower risk mitigation.

Finally, we minimize the GHHI to identify the optimum proportions across assets for the best diversification. The optimal proportions across different sector are indicated in the last column of Table 1.

For a more detailed perspective on this subject please do refer our working paper which you can access here.

18
Jan

Selection of Research Proposals for the NSE-IFF Financial Deepening and Household Finance Research Initiative

By Dr Santadarshan Sadhu, Coordinator, NSE-IFF Financial Deepening and Household Finance Research Initiative

The NSE-IFF Financial Deepening and Household Finance Research Initiative is very pleased to announce its final set of proposals selected for funding in this round. We expect, through this initiative, to catalyse a body of high quality research pertaining to critical questions of household finance, financial inclusion and financial deepening. We would like to thank all the researchers who applied to this initiative, making it extremely competitive and setting a high benchmark for future rounds. We would like to congratulate all the researchers whose proposals have been selected for funding and wish them the very best. Here are the selected proposals with a short description of each:

1. Getting our priorities right: Targeted agricultural credit and technology adoption in India by Andre Butler and Camille Boudot, Centre for Micro Finance

Access to agricultural credit directly influences production through two main channels: Primarily, credit can raise investment in input use and hence productivity; Secondly, credit provides farmers with the opportunity to smooth consumption and thereby increase the willingness to take risks and engage in productive agricultural investments. However, existing evidence on agricultural lending through formal institutions has questioned the effectiveness of priority sector lending as a pathway from poverty. India’s progressive financial inclusion agenda, including the promotion of a wide network of rural bank branches as well as encouraging priority sector lending, provides a unique setting in which to examine the constraints of these hypotheses. We will build on existing research and methods to determine if and how credit influences production decisions and investment, and ultimately productivity. The specific questions we consider are:

i. Does reducing liquidity constraints through formal credit flows encourage farmers to adopt widely available technologies? Does adoption of these improved technologies increase aggregate productivity of major crops?
ii. What is the impact of credit on farmers’ production decisions? Specifically, does credit enable farmers to cultivate more high-risk-high-return cash crops?
iii. Does formal credit allow farmers to make more long-term investments?

2. Quality of investment advice in retail banking in India: An assessment by Renuka Sane, ISI Delhi and Monika Halan, Mint Money

Understanding that opening of the bank account is but the first step in a much longer journey to financial inclusion, this paper seeks to analyse the efficacy of banks as vendors of retail financial products. The study aims to evaluate if bank based relationship managers sell financial products that are high fee generating regardless of the products that may be more suitable for the customer. The theoretical literature on financial distribution makes a distinction between sophisticated and naive customers and considers naive customers to be particularly susceptible to mis-selling. In this study, we evaluate if even informed customers, i.e. those that understand their financial requirements, and the gaps in their portfolio, are vulnerable to mis-selling in the absence of a regulatory framework that recognises specific customer rights, or requires basic suitability checks in advice and sale of financial products. The study proposes to evaluate the extent and pervasiveness of mis-selling in the banking channel in India. This allows us to measure advisers response when we exogenously vary the types of clients, especially in their financial sophistication in the form of knowledge of and demand for specific products.

3. Examining the adequacy of MFI multiple lending directive by RBI: A study of slum dwellers’ loan choices by Kanish Debnath and Priyanka Roy, IIM Ahmedabad

While a significant number of households in India do not have access to credit, parts of the country have already experienced crises of over borrowing. Similarly though credit is used for many purposes, it is still not taken as a signal to launch new financial products or product bundles. Though Indian financial inclusion experts in their eagerness to push credit into low-income households have received multiple setbacks, their pursuit has remained relentless. With multiple credit agencies often competing in the same geographical area, over-borrowing and even ghost borrowing has become rampant. In order to put a plug on the rising Non-Performing Assets, the Reserve Bank of India issued new directives for all Non Banking Financial Companies in December 2011 with further modifications in August 2012, restricting the borrower’s freedom in a bid to control over indebtedness. However, we reason that restraining borrowers to borrow only from two MFIs or less will create further problems for both borrowers and MFIs. In a market of illiteracy and informational asymmetries, people with a tendency to cheat can still defect, households may be denied loans at the time of need and rogue MFIs may pre-empt members from other MFIs, thus increasing their retention costs. We therefore aim to study the borrowing behaviour of slum-dwelling households in the city of Pune to gauge the adequacy of the RBI directives in containing over-borrowing and understand whether other financial products can contain their need for multiple loans other than income generation.

4. The Technology of lending: Contract design of informal credit products by Amy Jensen Mowl, IFMR Finance Foundation

The stubborn persistence of high levels of informal credit penetration, and comfortable coexistence of formal and informal credits markets, have re-emerged as important policy and research questions. Potential answers to these research questions have come from scholars in multiple fields, many of whom have focused on South Asia and India. Within economics, a vast literature has explored the causes and consequences of credit market failures, the unintended impacts of policy change, and the interaction of formal and informal credit markets. Within the field of anthropology and economic anthropology, scholars have made major advances in understanding household financial behavior, as well as the social norms embedding informal, semi-formal, and formal credit practices. Despite much excellent work, the research examining credit markets and informal practices have not yet fully explored the role of contract design in informal credit products as a cause and consequence of financial exclusion. Without such understanding, we are left with an incomplete analysis that both inhibits the opportunity for formal sector providers to learn from informal product innovations, and inhibits the clear framing of financial inclusion strategies and policy analysis. This study hopes to remedy the gap in the literature by analyzing the contract features of a clearly defined set of informal credit products in rural and urban Tamil Nadu. Through a careful analysis of the specific contract features of currently available informal credit products, I will show that in contrast to the assumption that access to formal, cheaper credit will result in households shifting away from informal credit options, the notions of access and price should be re-examined, as informal credit products rapidly adapt to changing conditions in the formal sector with a surprising set of contract innovations

5. A Framework for Financial Behaviour Modelling in a Rural, Low-Income Environment by Nandan Sudarsanam, IIT Chennai

The objective of this action research study is to create an empirically driven normative framework for business decision-making. The research design associated with this objective would be conclusive and not exploratory in nature. Additionally, given that we intend to use observational data as opposed to experimental data, this study will fit under the broad umbrella of a descriptive research design. Our research intends to use both longitudinal and cross-sectional data. Specifically, we intend to use data analysis methods to infer behaviour (defaults on loans, late-payment, cessation of service, etc.) from various attributes demographic details and past financial transactions. We intend to use a series of methods termed as ‘supervised learning’ to achieve this goal. While building credit scores from past transactional and demographic history is the bedrock of modern retail banking, a preliminary literature investigation by the researchers found little academic research in the space of developing such scores in a low-income, sparse transactional history environment. There are also problems in applying the traditional credit scoring models like FICO, CIBIL etc. to score low-income households. The major concerns are that existing prescriptive models were not built for this demographic, and existing methodologies lack the appropriate data to build a robust model. This motivates us to develop novel changes to the existing supervised learning methods, to make them more useful in this domain. The study also intends to validate the created scores with field case studies of mock implementations, and actual pilots.

The final research output upon completion of these projects will be published as working papers by March 2016.

28
Dec

Top 5 Game changers for the Indian Financial System in 2014

Our picks (in reverse chronological order):

Suitability becomes a Customer Right:

The RBI published the final Charter of Customer Rights for banking customers in December 2014. This Charter has introduced the Right to Suitability for all banking customers, in addition to the rights to fair treatment, transparency, privacy and grievance redressal. While all other rights have been captured to varying degrees in current customer protection rules and banking industry codes, the Right to Suitability has been enshrined by the RBI for the first time for retail customers of banks. The Committee on Comprehensive Financial Services for Small Businesses and Low Income Households (CCFS) had recommended that ‘every low-income household and small business must have a legally protected right to be offered only ‘suitable’ financial services’. The RBI has in its press release, also pointed out that all scheduled commercial banks, regional rural banks and urban cooperative banks are expected to prepare their own board-approved policy incorporating the five Rights of the Charter, and that such a policy must also contain monitoring and oversight mechanisms to be followed for ensuring that rights are not violated. This will pave the way for customer protection to shift from being an ex-post redressal process at the Banking Ombudsman along with self-regulation through industry bodies, to becoming an ex-ante prerogative of the Boards of banks.

Payments Banks become a reality:

The RBI published the final Guidelines for Licensing of Payments Banks in India in November 2014. The CCFS report had proposed developing a vertically differentiated banking structure, in addition to the horizontally differentiated model in place currently, in which banks specialise in one or more of three functions- payments, credit delivery and retail deposit taking. The Committee recommended licensing of new categories of specialised banks including Payments Banks that would be engaged in collecting demand deposits (ie, savings bank deposits and current deposits) and provide payments and remittance services, but not credit services. The RBI Guidelines state that the “primary objective of setting up of Payments Banks will be to further financial inclusion by providing (i) small savings accounts and (ii) payments / remittance services to migrant labour workforce, low income households, small businesses, other unorganised sector entities and other users, by enabling high volume-low value transactions in deposits and payments / remittance services in a secured technology driven environment”.

Guidelines for Licensing of “Small Banks”:

With a view that small local banks can play an important role in the supply of credit to micro and small enterprises, agriculture and banking services in unbanked and under-banked regions in the country, the RBI in November 2014 issued guidelines for licensing of “Small Banks” in the private sector.

Pradhan Mantri Jan Dhan Yojana:

In August, the Prime Minister announced the Pradhan Mantri Jan Dhan Yojna (PMJDY) programme in his first Independence Day speech at the Red Fort. Launched with a mission to provide universal access to banking facilities, the program has brought unprecedented spotlight on the financial inclusion agenda. As of 24 Dec 2014, 100 million bank accounts have been opened under the PMJDY. In its first phase (15 August 2014 to 14 August 2015), the PMJDY aims to cover all households with at least one Basic Banking Account with RuPay debit card, accident insurance cover of Rs.1 lakh and an overdraft facility of Rs.5000 permitted in Aadhaar-enabled accounts. In its second phase from 15 August 2015 to 14 August 2018, the scheme expects to provide micro-insurance and pensions.

‘In-principle’ approval of Bank Licenses to IDFC & Bandhan:

From amongst a pool of 25 applicants, RBI shortlisted the two applicants to set-up full-services banks in India. These institutions have been given a time frame of 18 months to comply with the Guidelines of RBI, following which the RBI would consider granting a licence for commencement of banking operations. Terming its approach as conservative in this round, RBI has indicated that it intends to use the learning from this licensing exercise and issue subsequent licenses “on-tap” going forward.

We would like to wish our readers a very happy and peaceful new year and look forward to your continued readership in the coming year. You can follow us via Twitter or subscribe to receive email updates from our blog by signing up here.

Once again, happy new year!

19
Dec

Video: Best Way to Interact with Clients: High-Touch or Low-Touch

The Master Card Foundation recently organised a Symposium on Financial Inclusion that explored the theme of “Clients at the Center” by focusing on the “Client Journey”. The event brought together key industry professionals ranging from practitioners, influencers and thinkers who are actively involved in the space. You can read the proceeds from the Symposium here.

Bindu Ananth participated in one of the panel discussions that was organised to debate on the Best Way to Interact with Clients: High-Touch or Low-Touch. Kim Wilson of Tufts University moderated the debate on the following proposition: The future of financial services for the poor will rest primarily in highly automated, low-touch models for reaching clients.

Arguing for the high-touch approach, watch Bindu participate in the panel discussion with other participants in the below video: