Pudhuaaru KGFS Turns 9 – The Journey of the First Branch


Reorienting Financial Well-being through FWR 2.0


By Dhivya S, IFMR Rural Finance

For an institution focussed on delivering high-quality and customised financial services to low-income households, the Wealth Management approach has been the one of the key underlying layers that is core to the KGFS Model. The sole objective of the approach is to maximise the financial well-being of households by offering tailor-made and suitable recommendations to them. Financial well-being, in this case, is to help customer households achieve financial goals, as per their priorities, in a secured and sustainable manner.

We have always believed that this approach of understanding the inherent composition and risks of rural households to help create their financial road-map requires a deep level of expertise. This approach of wealth management proves to be very different from the traditional approach of providing a full suite of products to customers and ask them to choose based on their financial needs. In this context, IFMR Rural Finance (IRF) first designed a Financial Well-being Report (FWR) five years ago to meaningfully engage with the bottom of the pyramid clientele.

The FWR is an automated customer-centric financial planning tool that uses customer data and back-end algorithms to make specific and actionable financial recommendations to enrolled rural households. The concept of devising wealth management conversations with customers takes us to the basic premise that there is a customer touch point that is primarily driven by the front-end staff called Wealth Managers. Wealth Managers across all KGFS branches rely on the FWR report as a guide to provide suitable financial advice aimed at enabling customers to realize their financial goals. To better understand the household, comprehensive data collection process by way of enrolments is undertaken, capturing data on the household’s members, demographics, cash-flows and financial goals. Post on-boarding the customer, a complete cash-flow analysis and risk assessment of the household is done to determine the customer’s financial position. While the data collection process forms the building block; the crux of wealth management however rests with capturing the financial goals of households. This is a continuous process of engagement with the customers that gets refined with subsequent conversations and financial transactions at the KGFS.

FWR in its current version has evolved over the years through a process of continuous improvement. However, there are several substantial improvements that need to undergo in order to make it even more customer-centric. Through anecdotal experiences and client interactions, we realised that wealth management isn’t simply about making financial plans for one’s future but is a means of realising one’s priorities. It is in this process, we realised that the process of customer engagement needs to be improved across various components as a precursor to having quality wealth management conversations. These conversations with customer focus on helping them achieve their financial goals by identifying and prioritizing what is most important to them. This then becomes imperative for us to make the entire process simple, intuitive and easy for both the Wealth Managers as well as the customers to achieve its full potential.

This post explains the perspective on the approach, methodology and design of FWR 2.0 and seeks feedback on improving the tool.

In the latest version of the FWR 2.0, the report aims to build on the legacy of the earlier system and is intended to be a significant leap towards delving much deeper into the financial lives of households. FWR 2.0 is engineered with concepts of Human Centered Design (HCD) to offer more practical and actionable insights keeping the customer interests at its core. The key areas of development that we plan to include in its redesign are:

1) Strengthening the data collection process to ensure high quality inputs from customers to have an in-depth understanding of their financial lives – Data collection is a fundamental process whose quality in turn determines the quality of financial advice given to the customer. For instance, if the Wealth Managers at KGFS are unaware of the customers’ high social expenses or if they have borrowed through informal channels or they are just a few thousands short of cash to achieve their goal; the Wealth Managers would not be able to provide appropriate recommendations which may have an adverse effect on the household’s financial lifecycle.

The scope of FWR 2.0 seeks to bridge the lacunae created through the development of various prototypes of a smart tool and associated processes. The aim is to identify the best technique of asking questions to customers during enrolment in a way that they are able to relate the most. We also plan to design a data quality score to explicitly measure the quality of data captured. This would be one of the key constituents to make sure that the entire wealth management process is based on sound first-principles.

2) Process redesign for achieving customer goals – Greater emphasis would be laid upon the quality of engagement with customers to enable them to reflect on their financial situation, identify and prioritize their individual & household goals. Assuming the data collected is of good quality, there are other important factors impacting the conversation that are to be rethought of. Some of the immediate alterations thought of are related to articulation of goals and logistics of organising wealth management conversations – for instance, should we have these conversations at home or at the branch; should we use laptops or just record customer stories and so on.

3) Better customer connect and usability through intuitive services – In regards to redesigning inclusive and progressive wealth management process, the aim is also to enhance the interface for mobiles and tablets through responsive web design and effective visualisation. The revamp would entail interface and visual improvements that are intuitive enough for the Wealth Managers to have meaningful conversations with customers.

We plan to finalise the above stated areas by creating various contending prototypes that aim to fulfil the stated objectives of FWR 2.0. These sets of prototypes would be tested in KGFS branches with existing and potential customers.  The revamp would entail conceptual, process and system related modifications that would be intuitive enough for both the staff and customers to equally participate in wealth management conversations. We are also scoping through the feasibility of creating a customer version of Financial Well-being Report that can be offered to the customer at the end of every conversation.

FWR 2.0 would not only aid in augmenting KGFS business performance and minimising business related risks due to improper or erroneous recommendation, but most importantly, would lead to an even more improved and meaningful customer engagement and retention.


Thinking about Micro-insurance Penetration and Entrenchment

Guest post by Renuka Sane


Insurance contracts to lower income households (micro-insurance) are typically for one year. This implies that when the contract expires, the household needs to renew the purchase for the next year. It is intuitively appealing to consider that micro-insurance is truly an effective means of smoothing consumption when households continuously renew their contract. The question arises: do households choose to repurchase micro-insurance and enjoy continued cover? In Sane and Thomas (2016), From participation to repurchase: Low income households and micro-insurance, we evaluate this question for life and accident micro-insurance, along with what drives the repurchase. We also ask how long it takes customers to repurchase once the policy has expired. We especially focus on two such drivers: access to credit and wealth.


We use data from the IFMR Rural Channels and Services Private Limited (IRCS) which implements the Kshetriya Gramin Financial Services (KGFS) branch-based model of distributing financial products across India. KGFS branches distribute two insurance products: the term life (TLI) which covers mortality risk, and the personal accident insurance (PAI) which covers mortality risk or permanent disability risk of customers arising due to accident. The data includes demographic and wealth information for 132,000 micro-insurance customers whose first policy expired between March 2011 and March 2014. The data also includes information about micro-credit contracts between KGFS and these customers prior to the purchase of the micro-insurance. To this dataset we add rainfall data gathered for the relevant districts and time periods, to indicate if the policy expired in a period when rainfall was scanty, versus when rainfall was normal.


We find that 65 percent of the sample renewed their insurance policy at least once, after their first policy expire. Five characteristics stand out:

First, there is a large difference in re-purchase probability (almost 33 percent) between the group with a micro-finance (Joint Liability Group) loan before the original purchase of the insurance policy, compared to those without a JLG loan. What could be the reasons?

When we examined the date of insurance repurchase and the take-up of a JLG loan, we find that 17 percent of those who renew insurance have taken a new JLG loan within 7 days of the insurance purchase, and another 18 percent have taken a new loan within 14 days of the insurance purchase. This suggests that while some part of the loan may be used to pay the insurance premium, it does not appear to be an over-whelming driver for the purchase, at least for two-thirds of those who renewed insurance.

A popular voiced perception is that life or accident insurance acts to protect the credit payments in case the borrower dies or suffers a debilitating injury. In this case however, most lenders would waive repayment of loans in the event of death of the debtor, giving customers little reason to purchase insurance to ensure repayment. Further, the insurance producer has nothing to gain from the point of view of repayment. There is little incentive for either intermediary to push the insurance product only to loan clients.

However, a common financial intermediary for credit and insurance may be important in other ways. Since credit and insurance are offered in the same branch, a higher demand for credit may translate into higher repurchase of insurance as customers visit the branch more frequently, and get more exposed to other financial products, and are perhaps able to build trust about the financial service provider.

Finally, there could be unobserved differences between those who have chosen to take a JLG loan and those who have not. It could be these differences that are driving the result, except that we are unable to test for this in the present data-set.

A second feature is that when the policy expires in months with scanty rainfall, the repurchase probability reduces by almost 7 percent. This is statistically significant at 1 percent. It suggests that collecting premiums during a lean period (caused by poor rainfall) restricts the ability to pay premiums.

The third feature is that repurchase probability rises with assets, but falls for those in the highest asset quartiles. This suggests that individuals only consider the purchase of insurance when they do not have enough buffer stock wealth. Households primarily demand life insurance when they lack accumulated reserves, or wealth, for self-insurance.

A fourth feature is that the largest number of repurchases occur within the first one to two months of expiry. Repurchases then continue to fall further after 12 months. This implies that if an insurance customer does not repurchase her policy within 12 months of expiry, she is unlikely to do so after. This helps to guide policy on improving insurance uptake: the first few months are the right time for an intervention to improve repurchases.

The fifth feature is that only 28 percent of those who repurchase the policy, increase the amount of cover purchased. We also find that 47 percent of those who increased their cover had gone from having one policy (accident cover, for example) to purchasing both policies (accident and term life cover).


Improving insurance participation of low-income households has become an important objective in the access to finance movement. The market for micro-insurance products will mature once people continuously purchase these products, and also make decisions on the sum assured purchased. Our research on understanding repurchases can provide inputs to the design of government programs as well as private sector initiatives. This is also the start of what we hope is an exciting research agenda on the drivers of sustained participation in micro-insurance.


Sane and Thomas (2016), From participation to repurchase: Low income households and micro-insurance, FRG WP. http://ifrogs.org/releases/SaneThomas2016_microInsurance.html


Preliminary Findings from the KGFS Impact Evaluation Study


By Iris Braun, Research Manager, IFMR Lead

Happy Birthday Pudhuaaru KGFS, and if we may say so as an objective evaluation team: Many happy returns (in every sense of the word)! Today marks the 8th anniversary of Pudhuaaru KGFS and it is laudable that it not only set itself a social purpose on top of business goals but also allowed the results towards this purpose being scrutinized independently and scientifically, almost from the start of its lifetime. As the initial results emerge from the large, randomized control trial that has been following the serviced areas and households over several years (KGFS: Impact Evaluation [IE]), there are clear indications that Pudhuaaru KGFS has more impact on its clients’ life than just any business might.

Over the last eight years, microfinance itself has grown up and evolved, not without many developmental setbacks. Rapid expansion of MFIs in the early 2000s with the view that “all humans are born entrepreneurs” (Muhammad Yunus) and universal access to microloans would move people out of poverty via self-employment, gave way to the 2010 microfinance crisis of Andhra Pradesh with over-indebted farmers taking their lives over the desperation of not being able to pay back their unsustainably large debts. The shock to the industry is evident in the stagnant take-up rates in initial branches around the same time (see figure 2 below). Confidence in the channels and magnitudes of impact was battered further by several evaluations with no detectable or very modest results (see e.g. Banerjee et al, 2013, and 2015) and finally, the concept re-emerged as a more wholesome “financial inclusion” to once again raise hopes for a multitude of social outcomes.

Pudhuaaru KGFS has waited out the storm with confidence in its model and the flexibility to react to the demands of the market and offer a broader product portfolio than most MFIs, has made it stand out from the others in the sector. The KGFS:Impact Evaluation study is researching the impacts of a business model much more resembling banks as we know them for middle class customers than traditional MFIs, including a broad range of loan products, insurance and savings products as well as wealth management advise.

The evaluation is based on the randomized roll-out of branches which ensured that we have comparable “control areas” with no branches and any significant difference we can find between the areas with and without bank branch can be traced back to the opening of a bank, discounting any intervening factors. From 2010 onwards, we have repeatedly interviewed nearly 19,000 households in three districts (3,300 for a large household survey and 15,300 for an additional, shorter social network mapping), including a majority of Pudhuaaru KGFS branch service areas.

The first thing to note is that the engagement with the products is reasonably high. KGFS customer data and census data from the area show that 44% of households in the service area (defined as a five km radius area around the branch) have taken up at least one product at this point. Many have taken multiple products – on average eight – in the first three years of branch operation1.

Second, the take-up of insurance products is equally high as of loans products, with only savings products lagging behind somewhat. This sets this evaluation apart from other similar evaluations that have suffered from low-uptake of the product they are trying to evaluate in an Intent-To-Treat model (i.e. the impact is assessed over all of the possible clients, not just the ones that did indeed take products. Low take-up might lead to difficulty in getting precise estimates of the observed changes).

figure 1 - all product takeup by age
Figure 1: Product take-up in study branches by month of opening and product type

We can also observe that KGFS has improved its model with experience: take-up is more rapid in the waves of branches that opened later, reaching an average of 32% of village households with at least one product one year after opening (vs. 23% in the initial branches).

figure 2 - all product takeup
Figure 2: Product take-up in study branches over time

We have so far collected only two thirds of the final data set, and thus we are cautious to make definite statements, however, it is very clear that KGFS is taking away business from the informal sector and moneylenders in particular. While the share of population with formal loans is 65% in treatment areas, it is only 60% in control areas and additionally, informal borrowing is 4 percentage points higher in control areas. Especially moneylender borrowing is affected, which is 8 percentage point lower in treatment areas; this is a 20% decrease compared to the matched areas without a branch. From our survey of informal financiers servicing the same regions, we know that they are charging much higher interest rates for loan products, nearing 60% annual rate, while KGFS is at 25% for its products. Based on this, we are hopeful to see some further changes in the structure of employment, investment or consumption. Overall indebtedness is only slightly higher, at 89% of households in treatment vs. 86% in control areas with outstanding loans. A social network map of the study area also shows that people might have to rely less on far away friends and family when borrowing for emergencies and hence reduce their borrowing from them. We will substantiate these first indications in the months to come until the end of the evaluation period at the close of this calendar year.

figure 3 - sources of outstanding loans
Figure 3: Sources of outstanding loans in the KGFS treatment and control areas

The evaluation also shows up the limits of Pudhuaaru KGFS’s model as it currently stands. In a dedicated agricultural component (as part of the global Agricultural Technology Adaption Initiative), we are seeing that, while formal loans make up a large share of loans before the beginning of the season, they are not deemed to be flexible enough to still be of relevance once the season starts (see figure 4, more information here).

This insight gets reinforced by the survey of informal financiers who quote turnaround times and flexibility in loan payback frequencies that might not be replicable for KGFS. Some of the changes in product design were already implemented by KGFS, for example door step collection of repayments has by now become a common feature even of branch banking. One can see from this survey that customers are willing to pay extra for the service of flexible repayment windows and absence of formal documentation.

figure 4 - agri loans
Figure 4: Farmers use formal loans mostly at the start of season

It is not the case that only financially excluded people resort to moneylender loans: in fact around two-thirds of moneylender customers have loans with formal institutions. Providers like KGFS will have to be creative and innovative in their product design and use of technology to match some of these features. This will determine whether they will be able to reach out and affect the life of the customers in their areas even more widely than has happened in the last eight years.


1 – This data is from the 36 branches that opened in the last three years, which makes them more comparable than the earlier 8 branches.
Banerjee, Abhijit V., et al. “The miracle of microfinance? Evidence from a randomized evaluation.” (2013).
Banerjee, Abhijit, Dean Karlan, and Jonathan Zinman. “Six randomized evaluations of microcredit: introduction and further steps.” American Economic Journal: Applied Economics 7.1 (2015): 1-21.


Insights from a Deep dive exercise in Sahastradhara KGFS, Uttarakhand

By Arjun Sood & Gayathri V, IFMR Rural Finance

Sahastradhara KGFS started its operations in the year 2008 with a mission “to maximise the financial wellbeing of every individual and every enterprise by providing complete financial services in remote rural Garhwal”. Currently, we have thirty one branches serving the districts of Uttrakashi, Chamoli, Rudraprayag, Tehri, Dehradun.

While Sahastradhara KGFS has gone where no private financial institution has gone before and proven its commercial case, we continue to think there is enormous untapped potential and room for improvement in the customer experience. This can be seen in terms of low activation rates, time delays between customer enrolment and activation and relatively low wealth manager productivity. Against this context, Sahastradhara KGFS commissioned an exercise which was internally dubbed as “Mission Deep Dive Sahastradhara” to uncover the root causes for this and suggest strategies for improvement.

Sahastradhara KGFS BranchLocation1
Sahastradhara KGFS Branch locations

In the extensive field engagement that followed, a cross functional team involving members from diverse roles dwelt deeper into the operations of the entity. Out of this extensive study three themes emerged as the ones that needed most attention immediately:

  1. Data Integrity – How well do we know our customers and how is this being leveraged for business decision making?
  2. Process improvements – How efficiently are we able to provide service to our customers?
  3. Organisation development and training – How well trained and empowered is the field staff to carry on branch operations smoothly?

The team came up with following suggestions and tools to address these gaps:

i) Beat plan for the Wealth Managers – The service area of a branch can go up to 25 kilometres from the branch location. Owing to the hilly terrain, the hamlets and habitations are not always accessible by road; they have to be accessed on foot. For a Wealth Manager to reach up to these villages a mix of options have to be chosen i.e. public or private transport and foot. Field visit to such villages[1] not only demand a significant investment of daily time but also physical effort. To plan the daily schedule of Wealth Managers and to maintain regular interface with the customers, a beat plan for each branch was proposed. As per the beat, Wealth Managers are expected to visit a particular village or set of villages on a pre-defined date.

ii) Prioritisation matrix – As part of the beat, once the Wealth Manager has reached a village, the prioritisation matrix[2] would suggest which customers have to be met and for what activity? The rules that govern the prioritisation matrix tool are flexible and can be defined/ altered on the basis of changing business priorities. As a starting point, we added the following rules:

  • customers whose data needs to be updated (re-enrolment),
  • high priority insurance customers (human capital, shop and livestock),
  • customers who have goals coming up in this year (lead for credit products) and
  • customers who have high surpluses and long term goals (lead for investment products)

iii) Focus on Tier 1 areas – Tier 1 areas, are the areas that are located within a range of 10 kms from the branch by road and where the Wealth Manager does not have to cover more than 3 kms on foot. In order to increase the business numbers, Tier 1 areas or the areas that are easily accessible by road or foot from the branch were shortlisted. The target was set at achieving a minimum of 50% household level activation for asset, insurance and investment products.

iv) Re-enrolment and data update of households – We defined metrics that measure the quality of enrolment data based on completeness, validations and vintage. Households that did not satisfy this data quality metric were to be re-enrolled/ data was to be updated for them in the systems. Priority was accorded to households based on their engagement with us – re-enrol active and overdue households first, then dropout households and then never active households. Having quality information about the financial lives of these households would enable the Wealth Manager to offer high quality wealth management advice and hence, the right financial products suited to the household profile.

v) Credit process improvement strategies –

  • Introduction of Cash flow appraisal template – The existing loan appraisal template used to capture data about a business at a point in time i.e. on the day of appraisal. Due to this, we had limited understanding on the seasonality of cash flows of the customers. Appraisal sheet that will capture the month on month cash flows of the business was introduced with an objective to understand the seasonality of cash flows of the customers, eventually leading to us designing customized products.
  • De-centralisation of loan approvals – Loan sanction up to a certain amount was decentralised to the branch staff. This would lead to an increase in ownership of loan underwriting at the branch level and reduce the loan processing time.

The deep dive exercise at Sahastradhara KGFS gave us valuable insights into the running of a KGFS. It allowed us to reassess some of the contours of the KGFS model and align it to our larger mission. Some of the key insights are:

  1. Importance of having recent, triangulated and complete enrolment-appraisal data about our customers: In order to offer suitable financial products to the households it is important to understand their financial profile fully – income, expense, goals, assets and liabilities. We are working to make the KGFS enrolment process a work-flow based data collection system which will ask a limited set of key questions to the customer to understand their financial lives completely. To make the best use of finite customer interaction time, the data declared by the customer will be validated and triangulated at the backend using external data sources and our own historical data.
  1. Importance of understanding month on month cash flows of the households before product sale: Any credit product being given to a household is based on a thorough appraisal of the loan purpose and the asset being created from the loan. This deep dive exercise taught us that while it is important to do that, it is equally important to assess the current cash flows of the household which will support regular repayments. Given the rural markets we operate in, it is also important to be cognizant of the seasonality associated with these incomes. We are working to build this knowledge about income generating assets and the cash flows from that asset into the enrolment-appraisal process.
  1. Better use of the Wealth Manager time and improved customer experience: We are also building predictive models that would assign a score to a customer at every stage of his/ her interaction with us. This would provide better targeting strategies and specific engagement paths based on customer type, thereby improving activation rates, predicting delinquencies and customer attrition over time.
  1. Assessing the quality of enrolment data in other KGFSs: Data quality as a topic has garnered a lot of interest and we are working to come up with a standard set of data quality metrics which will then be published thereby incentivising the branch to collect good data. The branch staffs are also being trained on the importance of having quality data and some methods by which they can prompt the same from the customers at the time of enrolment.
  1. Significance of a process audit: The audit process is a key tool in identifying two things: if the process that has been prescribed is being followed on the ground (voice of process) and if there are any gaps in the process that hinder the customer experience (voice of customer). After the Sahastradhara exercise, we are trying out a couple of methods by which any prescribed process can be audited. This is being tried out in multiple KGFSs and a refined audit process is expected to be launched pan India.
  1. Learning about the roles of the frontend staff: The Sahastradhara deep dive reiterated the role that our field staffs play in the customer experience. It is absolutely crucial that the frontend of the organisation be empowered and owns the success of each of their branches. Decentralising certain amount of decision making will go a long way in bringing about that cultural shift.
  1. Leveraging existing technology: The backbone of the KGFS model is the initial investment we make in technology and how we leverage it to provide quality service to the customers. In this regard, we pushed the branches to use the mobile platform to make on-the-spot product sales and other transactions at the customers’ homes. This coupled with real time biometric authentication, thermal receipts and IVR messages provide a secure way to move product sales closer to the customers.

[1] Village Sangrola under the service area of Lambgaon branch is 25 kms from the branch. It takes 2 hours to cover 23 kms by road and an additional 30 minutes to walk 2 kms uphill, to reach the village. A to and fro journey to the village will consume 5 hours. Customer interaction and transaction time will take additional time. The time and distance were mapped during a field visit in May 2014.

[2] A prioritisation matrix is a demand chart equivalent in an MFI. But unlike MFIs, our Wealth Managers perform a multitude of tasks ranging from enrolment to product sale to appraisal. The prioritisation matrix helps the Wealth Manager keep track of the tasks to be completed while the tasks themselves and the priorities can be dictated from a central level.