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.


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.


Leveraging Mapping for the Rural Economy – Part 2

By Balajee GE, with inputs from Shilpa Bhaskar & Gayathri V, IFMR Rural Finance

This post is a follow-up to our earlier post which briefed about the mapping exercise that IFMR Rural Finance had undertaken to study the service area of a KGFS Branch.

Once a location to set up a KGFS entity was identified, (Krishnagiri district in this case), team members Shilpa Bhaskar, Gayathri Vijayaraghavan and Noble Joseph of IFMR Rural Finance put in place a field team to survey the entire district first hand.

The surveyors mapped the infrastructure details (as mentioned in the earlier post) using an open source application that was customised on the mobile platform for this specific purpose. By switching from traditional paper-based survey to electronic format, the team straight away was able to act upon its findings. For example, every detail geo-tagged on the mobile was updated live and it was easy to track the surveyor real-time throughout the day. This close monitoring in-turn reduced data errors drastically.

This data was then coded on to the mapping application.

In addition to the general infrastructure details of the village, the team also mapped every household in the village for enrolment. Once again, each household was coded to show up on maps.

At any point of time, it became possible to see the list of households that had enrolled with the KGFS entity and the ones that had not, giving valuable spatial perspective to the decision makers in the KGFS entity.

Mappin - Part 2
A mapped district. (Graphics by Rohini Rajavel)

The potential use of this visual interpretation of data is immensely varied and powerful. Some of them could be:

  1. By mapping the infrastructure details, it is possible to sense what locations are at the brink of urbanisation and thus adjust the KGFS branch location to suit its Remote Rural criteria.
  2. The geographical spread of a particular product take-up gives valuable information about the economic activities in that area. For example, a cluster of households in a specific area availing livestock loan could mean a thriving dairy economy in that area and so further suitable products can be recommended to the customers from there.
  3. The data captured is a rich source of inputs to help design the right products for a geographical location. For example, an area with a large number of retail shops would be a valuable input to the product design team who can then design a very specific set of products for these retailers.
  4. Households can be followed up for enrolment on a regular basis; areas with least enrolments can have a more intense awareness campaign.
  5. The wealth manager (field staff) can plan his/her day efficiently.

The entire exercise took about 3 months to complete, but by the end of it, the data on hand proves to be very valuable that could help organisations like KGFS entities to create lasting positive impact on the economy of the villages.


Leveraging Mapping for the Rural Economy – Part 1

By Balajee GE, with inputs from Shilpa Bhaskar & Gayathri V, IFMR Rural Finance

How do spatial parameters like distance and accessibility impact financial inclusion? How can an organisation striving to achieve financial inclusion locate itself strategically such that it becomes truly inclusive in every sense of the word?

When Shilpa Bhaskar and team visited a KGFS branch on an assignment, they noticed that the product renewals by customers who were in villages around 4 or more kilometres away was comparatively lower than by the customers who were close by. This was despite the fact that enrolment penetration was high across these areas.

This might not appear unusual at first look, as it is understandable that frequency of visit thins out as one goes further away from the office of the service provider. However, in this case, the said village was specifically mapped to the KGFS branch. This means that the service area of the branch was fixed and the branch was dedicated to serve customers from these areas and yet customers did not make full use of it. The team went back and decided to look into this a bit deeper.

They then learnt that, the existing method of fixing a branch service area as an approximate five-kilometre radius purely based on political map boundaries and relying on secondary data sources and manual recces could be made more efficient. Though the distance may be optimal, there could be other factors in play. For example, though the distance to the branch was less, the customers might not have access to bus services and might have to cross flooded fields to reach the KGFS branch. Or the commercial centre of the said village could be on the other side and villagers hardly found any reason to travel in the direction of the branch.

They also learnt that purely relying on secondary data for details such as population and occupation was not enough. Usually, before optimally locating a KGFS branch, demographic details were collected from the Village Administrative Officer and the Panchayat. Branch locations were then chosen based on this data. But unfortunately, one is a revenue office and the other is a development node. There were clear differences in the way these two offices looked at the same data. There had to be a way to triangulate and arrive at the right way of looking at this data. There seemed to be no better way than doing it first-hand.

When IFMR Rural Channels, the current licensee of the KGFS model, decided to open a new KGFS entity in Krishnagiri, the team decided to map the entire service area from scratch.

What followed next was an intense mapping exercise (the details of which will be explained in Part 2) of the proposed branch locations of the new KGFS entity. The exercise involved manually geo-tagging all important locations in the village along with photographs on Google Earth.

While the exercise was initiated to just map the service area and help identify the ideal branch locations that would help achieve its mission, the same has now grown in size and the spin-offs could lead to interesting and impactful developments.

Along with mapping every household in the village, the team decided to geo-tag details of infrastructure and public utilities. A team of surveyors has mapped out every single bank branch, school, telephone exchange, medical facilities, and bus stops along with details of economic activities at the village level, block level and at the district level.

The result is a detailed yet simple and intuitive map visually representing every important economic detail in the village.

(Map view of Krishnagiri district – Bus stops, financial institutions, Medical facilities, schools have been manually geo-tagged.)

The KGFS entity can now use this data and map to plan its annual operations and deploy a very detailed and tactical plan to ensure financial services delivery reaches the remotest of the locations.

(In Part 2 we discuss in detail the mapping methodology and the possible uses of the outcome of the exercise.)


IFMR Rural Finance is now ISO Certified

By Biswaranjan Mohanty, IFMR Rural Finance

IFMR Rural Finance is now an ISO 9001:2008 certified organisation. IFMR Rural Finance, an organisation with a mission of promoting high-quality Financial Institutions, has undergone the certification for the design work it carries out in various fields of finance. The exercise is extended to various verticals of IFMR Rural Finance viz. Technology, Products, Training, Wealth management, Business Development and Design to look into the internal processes that have been followed in ensuring the quality of deliverables for clients.

Commenting on the certification, Puneet Gupta, CEO, IFMR Rural Finance said, “We started this exercise almost a year back and we had different teams run through multiple rounds of process refinement. It sure was a challenge because we operate in a unique space, but at the same time, it was exciting because we were setting new standards in the field.”

The exercise resulted in setting a common cycle of design for each of the verticals, which will ensure smoother communication, proper knowledge management, continual improvement and superior channel management.

In the due process, IFMR Rural Finance has also framed the following:

  • A Quality System Manual, which is an overarching framework that defines various processes to ensure quality at various levels of design life cycle.
  • A Quality System Procedure which defines procedures, templates, information sharing mechanisms for various processes like receiving client input, internal discussions, approvals, design & testing and feedback mechanism.
  • Quantifiable Objectives – an objective for each of the department to monitor the qualitative parameters at regular intervals and for improving the same.