IFMR Rural Channels launched its fourth KGFS – Vellaru Kshetriya Gramin Financial Services to serve Ariyalur, Perambalur and Cuddalore districts of Tamil Nadu. This is in addition to Pudhuaaru KGFS (Thanjavur), Dhanei KGFS (Ganjam, Orissa) and Sahastradhaara KGFS (Uttarakhand). The KGFS network has 106 branches that serve about 200,000 households in these regions.

The KGFS model is aimed at improving the financial well-being of households and enterprises in remote rural India by providing them access to a comprehensive range of financial services. Its wealth management approach emphasises understanding each household and customising a portfolio of financial services to meet its unique requirements.

At the Vilagam branch of Vellaru KGFS

Speaking at this occasion, Anil Kumar S.G, CEO, IFMR Rural Channels said, “This is another important milestone in our journey towards achieving our mission. Over the past few months we have worked on completing our suite of products, which now includes Pensions and term life among other products. We have also been able to make significant progress on our wealth management approach. The branches in the new entity would be offering this complete suite of products under the wealth management framework.”

By Deepti George, IFMR Finance Foundation

IFMR Finance Foundation worked with Pension Fund Regulatory and Development Authority of India (PFRDA) as Knowledge Partners to help organise their first NPS-Lite Aggregators’ Meet in New Delhi, on 21st November, 2011. The meet brought together aggregators and other stakeholders under one roof, to address issues and concerns faced while distributing NPS-Lite and Swavalamban benefits to their customers.

The Meet was attended by all senior members of PFRDA along with representatives from over 25 different organisations, including Nitin Chaudhary and Anil SG from IFMR Rural Finance who were also part of separate panels. PFRDA used this opportunity to announce a new incentive scheme for the Aggregators where they increased the fixed incentive from INR 50 to INR 100. PFRDA also released new communication material that was designed specific to each aggregator’s need and could be used for creating awareness about pensions, NPS-Lite and Swavalamban benefits.

The Meet kicked off with an opening statement by Mr.P Upadyay, Chief General Manager, PFRDA, and was followed by an overview of the two pension schemes, NPS Lite and Swavalamban, by Ms.Padma Iyer Kaul, Executive Director, PFRDA. She set the tone of the conference by highlighting the importance of pensions and guided the participants in practically addressing the issues they may have with the two schemes. Mr.Yogesh Agarwal, Chairperson, PFRDA, also graced the conference with his presence and thanked the aggregators for their efforts.

The Meet brought critical issues to the table resulting in lively discussions on product design, need for a standardised method of delivery through technology and the inherent necessity for a nation-wide awareness campaign to highlight pensions as an important financial planning tool.

In response to the concerns highlighted, some fascinating suggestions and innovations, few already being implemented by Aggregators, were brought to light. Some of these are:

1) The need to develop communication and marketing collateral that can be deployed to create awareness and educate people on pensions – in order to create the market for pensions
In this context, Nitin Chaudhary, explained the Wealth Management approach of KGFS entities that has enabled it to become the leading aggregator in terms of penetration of NPS Lite. He also shared training materials and flipcharts currently being used at KGFS. South Indian Bank also shared their ideas they had successfully implemented, where they used the NPS-Lite logo on all receipts and envelopes. A few aggregators like Bandhan Financial Services and Department of Women and Child Development (DWCD) had even developed their own passbooks to record transactions and help customers keep track of their contributions.

2) The importance of building technology platforms which help to reduce transaction and process costs along with reducing operational risk
Mr. Amit Sinha, from NSDL suggested that using mobile phones for transactions would encourage portability across aggregators and increase efficiency. It was also suggested that creating applications that could be shared with all Points of Purchase was cost-effective and easy to build, also paving way for inter-operability between aggregators, and across locations. Inter-operability was especially important given that the access to this long-term product must not be aggregator-dependent.

3) Concerns relating to delayed issuance of PRAN (Permanent Retirement Account Number) cards and data errors
IFMR Rural Finance shared its feat of being the first aggregator to generate PRAN directly in collaboration with the CRA (Central Recordkeeping Agency), thereby reducing TAT to less than 24 hours. Data errors could also be minimized by IFMR Rural Finance as the application forms get pre-populated with customer details directly from the Customer Management System rather than by a manual data-entry process.

4) KYC requirements contribute to exclusion of segments of the population, especially the migrant population
Ms. Gayathri of Labour Net suggested that given the essentially floating nature of migrant populations that could potentially enroll for NPS-Lite, a more diverse set of documents could be included for KYC norms to enhance outreach. It was also suggested that a “one-size-fits-all” strategy should not be adopted for the given target population, but rather there is a need to develop different strategies for different segments such as the stable urban poor, the daily wage migrants, seasonal migrants, and so on.

5) Increase benefits and attractiveness of NPS Lite and Swavalamban for the customer
It was suggested by Anil SG of IFMR Rural Finance that NPS-Lite may be clubbed with health insurance products like RSBY to take care of a gamut of eventualities that the individual or household may face, which may otherwise force them to dip into their pension corpus. This would provide customers with multiple benefits, mitigating not only longevity risk but also addressing to an extent, health shocks.

Going forward, IFMR Finance Foundation is drafting a report collating recommendations of participants from the Meet and suggesting ways forward for the NPS-Lite and Swavalamban schemes.

This is the fourth in the series of posts under the topic “Understanding the KGFS Customer”. The authors, Sowmya Vedula and Shilpa Sathe of IFMR Rural Finance, present data regarding occupations of KGFS customer. The information is as declared by the customer at the time of enrolment or at the time of any periodic updating of data.

Notes:

• This blog post displays data of all enrolled members of the three KGFSs, Pudhuaaru (Tamil Nadu), Dhanei (Orissa) and Sahastradhara (Uttarakhand), obtained from the datasets of the Customer Management System (CMS)
• Data considered for this post is as of November 15, 2011
• Pudhuaaru has 1,29,380 enrolled customers, Dhanei has 25,654 and Sahastradhara has 20,253 which brings the number to a total of 1,75,287
• This post also uses data of family members of enrolled customers from our customer management database. The total number of individuals considered for the analysis is thus 3,35,627 for Pudhuaaru, 88,452 for Dhanei and 85,316 for Sahastradhara
• Customers can enrol at any time throughout the year and hence the data collected is at different points in time
• Categories of occupations in the database are –Agriculture and allied activities, labour, migrant labour, student, business, working abroad, salaried, unemployed, housewife, and retired/pensioner. Agriculture and allied activities include people who own land and cultivate it, people engaged in fishing and dairy, and traders of crops and agricultural products. ‘Others’ includes income from self-employment in activities other than those listed above.

Overall Occupation Distribution

Chart 1 below shows the overall distribution of occupations by categories. Students are the largest in number (26.58%) followed by labourers (22.4%) and people pursuing agriculture and allied activities (13.6%). The number of unemployed people is also significant and amounts to 13.27%.

Chart 2 shows the distribution of occupations by KGFS entity. In Pudhuaaru and Dhanei, students and labourers form majority of the population. The unemployment rate is higher in these two geographies (14%-16%). The proportion of labourers migrating within India is the highest in Dhanei as compared to the other two geographies while the number of international migrants is higher in Pudhuaaru. In Sahastradhara, about one third of the population is students followed by agriculture and allied activities (25.4%) and salaried people (13%).

Most Common Combinations of Occupations

There are 15,648 individuals (5.77% of the total earning population) who are involved in more than one occupation overall. Chart 3 shows the most common combinations of occupations followed by these individuals. For example, there are 6771 labourers who also cultivate their own land, 2037 businessmen who are involved in dairy and 1175 labourers who are also involved in dairy activities.

Gender Wise Distribution of Occupations

Chart 4, 5 and 6 show the distribution of occupations by gender across the three KGFS entities. Noticeably, it’s a good sign to see almost equal number of male and female students across the three geographies. Wage labour is the most common activity taken-up by women in Pudhuaaru (25.89%) and Dhanei (19.37%) whereas in Sahastradhara majority of women are involved in agriculture and allied activities (43.70%). Migration among women is not a common trend and only 0.24% of women in all three geographies migrate for work within the country and abroad. The percentages of women who are unemployed are as follows: Pudhuaaru – 18.78%, Dhanei – 21.16% and Sahastradhara – 17.35%.

Among men, the distribution of occupations varies widely among categories in the three geographies. In Pudhuaaru, majority of men are either labourers (30.30%) or are involved in agriculture and allied activities (14.88%) whereas in Dhanei they are more or less equally distributed among agriculture & allied activities (13.94%), business (15.20%), labour work (15.40%) and domestic migration (13.80%). In Sahastradhara however, majority of the men are salaried (22.03%), followed by business (14.91%) and agriculture & allied activities (10.93%).

Distribution of Occupations by Age-Group

Charts 7, 8 and 9 represent the distribution of occupations by age-group. In all three geographies majority of individuals between the age of 1 and 20 are students. In the age group of 20 to 60, which is the productive age, majority of individuals are labourers (25.44%) in Pudhuaaru, while in Dhanei the majority is labourers (14.32%) and businessmen (9.48%). In Sahastradhara, most people are involved in agriculture (22.73%) and salaried employment (12.71%). Overall, domestic and international migration is observed mainly within the age group of 20 and 40 (73.33% of international migrants and 65.65% of domestic migrants). In the age group beyond 60, there is a significant difference in occupations among Pudhuaaru, Dhanei and Sahastradhara. In Pudhuaaru and Dhanei most individuals in this category (39.68% and 49.50% respectively) are unemployed while in Sahastradhara, this number is less than 19%.

Distribution of Occupations by Level of Education

Next, we look at the distribution of occupations by the highest level of education completed. Table 1 shows the overall distribution, while charts 10, 11 and 12 show the KGFS-wise distribution. Overall, 37.5% of the population has an education between 6th and 12th standard. Only 6% of the population has pursued graduate, post-graduate or vocational courses and the majority of these individuals are salaried employees, businessmen or are working abroad. Around 50% of the unemployed individuals cannot read and write.

In all three geographies, majority of the people who have pursued graduation, post-graduation and vocational courses have a salaried job. However, in Pudhuaaru alone, about 20% of people in these categories are unemployed. Among people who have studied till the 10th standard, majority of them are labourers In Pudhuaaru, and labourers or business people in Dhanei. In Sahastradhara, majority of them are involved in agriculture & allied activities. Pudhuaaru has a large chunk (44%) of unemployed people who cannot read and write, followed by Dhanei (36%) and Sahastradhara (30%).

Our next blog post will talk in detail about the distribution of incomes for all the above parameters.

By Shilpa Sathe and Amit Shah, IFMR Rural Finance

In this third post under the series “Understanding the KGFS Customer” we present data regarding expenditure patterns of rural households enrolled with KGFS. We also try to understand what the Planning Commission’s recently proposed poverty line cut-off (Rs.26 per person per day expenditure in rural areas) means for the average KGFS customer.

Notes:

• This blog post displays data of all enrolled households of the three KGFSs, Pudhuaaru (Tamil Nadu), Dhanei (Orissa) and Sahastradhara (Uttarakhand), obtained from the datasets of the Customer Management System (CMS).
• Data considered for this post is as of October 5th, 2011. The information is as declared by the customer at the time of enrolment or at the time of any periodic updating of data.
• Customers can enrol at any time during the year and hence the data collected is at different points in time.
• Data for expenses is captured household-wise (as against individual customer-wise). Pudhuaaru has 79,120 enrolled households, Dhanei has 18,216 and Sahastradhara has 17,513 which brings the number to a total of 114,849 households. There may be more than one customer per household; hence the total number of customers is much higher.
• With respect to household expenses, the CMS database contains data on expenses on food, clothing, health, travel, electricity, telephone and festivals. Expenses in the ‘others’ category includes rent, cable connection charges, and miscellaneous cash outflows as declared by the customer.
• It is important to note that recall periods vary for each category. The CMS database includes options for daily, weekly, monthly, quarterly and annual frequencies for every expenditure category and the frequency is chosen based on one at which the customer recalls expenditure accurately.
• Throughout the post we have used the term Monthly Per capita Consumption Expenditure (MPCE) which is the household expenditure per month divided by number of members in the household.

The distribution of Monthly Per capita Consumption Expenditure (MPCE) for an average household for the three geographies is shown in the graph below. Pudhuaaru has the highest average MPCE at Rs.837, followed by Dhanei (Rs.625) and Sahastradhara (Rs.618). We can observe that the overall expenditure is highest on food lowest on telephone across all three geographies.

Expenses on education and festivals are almost double (Rs.79 and Rs.68) in Pudhuaaru compared to that of Dhanei (Rs.31 and Rs.36) and Sahastradhara (Rs.32 and Rs.35). In rural areas of Thanjavur where Pudhuaaru KGFS operates, expenses on travel are higher as compared to rural Ganjam (Dhanei) and rural Garhwal (Sahastradhara). Electricity and ‘others’ are the only two categories where MPCE is higher for Dhanei and Sahastradhara as compared to Pudhuaaru.

The break-up of the average MPCE on food and non-food items is given in graph below. It is interesting to note that in absolute terms, the MPCE on food items is highest in Pudhuaaru, followed by Sahastradhara and Dhanei. However, non-food items have a greater share in the average household’s overall MPCE for Pudhuaaru as compared to Dhanei and Sahastradhara.

The 3 graphs below show the percentage-wise distribution of MPCE figures for KGFS customers. Overall, 62% of the enrolled households have a per capita daily expenditure of less than or equal to Rs.26 (monthly Rs. 780) which is the proposed poverty line definition of the Planning Commission. In Pudhuaaru, approximately 58% of the households have an overall MPCE of less than or equal to Rs.780. This number increases to 75% for Dhanei and 78% for Sahastradhara.

For food items alone in the below graph, 91% of the households in Pudhuaaru have an MPCE of less than or equal to Rs.780. This number increases only slightly to 92% for Dhanei and 93% for Sahastradhara.

The graphs below show the distribution of MPCE by income quartiles and by geography. Overall, MPCE increases with increase in income and the difference is the greatest between the third and the fourth quartile.

However, as we can see in the below graphs, MPCE on food items increases at a slower rate than non-food items as income increases. In other words, richer customers are spending a lesser proportion of their overall expenditure on food as compared to non-food items.

This is the second in the series of posts under the topic “Understanding the KGFS Customer”. The author, Sowmya Vedula, of IFMR Rural Finance, presents data regarding KYC documents (both ID and address proof documents) furnished by potential customers when they enrol with KGFS. The author also presents data of existing financial services that customers were already availing at the time of visiting KGFS. The information is as declared by the customer at the time of enrolment or at the time of any periodic data updation.

This blog post displays data of all enrolled members of the three KGFSs, Pudhuaaru (Tamil Nadu), Dhanei (Orissa) and Sahastradhara (Uttarakhand), obtained from the datasets of the Customer Management System (CMS).

Note:
•    Data considered for this post is as of September 20th 2011
•    The enrolment statistics for the three KGFSs is: Pudhuaaru – 126,082; Dhanei – 24,793; Sahastradhara – 18,404; Total=169,279

ID Proof Documents

ID proof documents are collected from customers during enrolment with KGFS. Voter ID is the most commonly submitted ID proof document across the three geographies. The major document in the ‘Others’ category for Pudhuaaru and Dhanei are driving licence, bank passbook, PAN card and passport, while for Sahastradhara it is driving licence, PAN Card and passport.

The ‘Panchayat Certificate’ category comprises of letters obtained from a gazetted officer or the Panchayat Head or the Village Admin officer (VAO). Slightly more than 10% of all enrolments in Pudhuaaru and Dhanei used this category of ID proof. In Pudhuaaru, 72% of these enrolments were by females while in Dhanei, females in this category formed only 41%. A major portion of these enrolled customers were labourers or housewives.

PAN Card

PAN card (Permanent Account Number issued by the Income Tax Department) applications had been facilitated by the KGFS branches for its customers up to March 2010.

Address Proof Documents

The break-up of the document types collected as address proof are given above. The major document types in the ‘Others’ category for Pudhuaaru and Dhanei are driving licence, bank passbook and passport, while for Sahastradhara it is driving licence and passport.

Other financial services

The above charts give information about other financial services being availed by the KGFS Customer – bank account and other loans. The breakup of the source of the loans is given in the panel on the right.

IFMR Rural Finance has been appointed as one of the first Interested Non-Governmental Agencies (INGA) by the Ministry of Labour and Employment, Government of India, to participate in its Rashtriya Swastya Bima Yojana (RSBY).

The RSBY is a Government of India flagship initiative to provide insurance coverage for Below Poverty Line (BPL) families. Hospitalisation coverage up to Rs. 30,000 (arising out of health shocks) is provided under RSBY. The RSBY has already enrolled close to 23.5 million households in the BPL category.

As a result of this appointment, customers of Kshetriya Gramin Financial Services (KGFS) can now avail health insurance under the RSBY. More than 170,000 rural households in Tamil Nadu, Orissa, and Uttarakhand that are already being serviced by IFMR Rural Finance can now benefit from the scheme.

 “Health shocks are a major threat to the income earning capacity of households and lack of access to affordable and quality health care systems in rural areas make these households even more vulnerable. Health insurance, which provides a safety net by protecting the human capital, plays an important role in ensuring the financial well-being of these rural households. Partnering with RSBY will help us offer this much needed product to our customers, who will also be able to benefit from the scheme’s technology driven platform to access an extensive network of quality health care services”, said SG Anil Kumar, CEO, IFMR Rural Finance

Under this arrangement, it is envisaged that the KGFS will be responsible for customer identification, creating awareness about the benefits and premium collection, while the insurers and RSBY will be responsible for managing the hospital network and administering of the insurance programme. The product portfolio of KGFS includes investment, credit, remittance and insurance products.IFMR Rural Finance is already an aggregator for PFRDA’s NPS-Lite. At present, customers can avail Personal Accident, Life, Livestock and Shopkeepers insurance through KGFS. The latest development offers the much needed protection against health shocks for remote rural households.

We had the pleasure of hosting Dr. Subir Gokarn, Deputy Governor, RBI, on a branch visit to the Pudhuaaru KGFS recently. At the branch, Dr. Gokarn interacted with the team and participated in the wealth management ritual. He remarked on the need to find ways to help customers bridge the gap between their financial goals and what their current surpluses were as well as the need to manage risks arising from livelihood shocks. He emphasised the importance of product design based on an understanding of household insights and internalising some of these insights across the organisation.

Dr. Subir Gokarn (left) interacting with Bindu Ananth, President, IFMR Trust;  Anil SG, CEO, IFMR Rural Finance; and the Wealth Managers.

By Shweta Aggarwal, IFMR Finance Foundation

The KGFS model is structured around the concept of financial well-being and aims to maximize the financial well-being of every individual and every enterprise. Among the questions that immediately arise are: What is financial well-being? How do we know that it is being maximized?

These questions are not always easy to answer as there is no one universally accepted definition and method of measuring financial well-being. IFMR Rural Finance and IFMR Finance Foundation is working together to develop the notion of financial well-being in order to assess the impact and viability of the KGFS model. The team is also developing an index to measure changes in financial well-being of households on an ongoing basis. This is a complex task as financial decisions are intricately linked and extremely dependent on each other and hence, not easy to discern.

We define financial well-being as:

The state in which a household can optimally choose patterns of consumption over time and in uncertain states of the world

In other words, a household’s ability to grow, manage liquidity and weather downturns across different periods of time and states of the world can be used to determine its financial well-being. Using this working definition, we structure the index around four domains: Protection, Liquidity Management, Diversification and Growth. The core wealth management methodology adopted by KGFS is designed around these four domains, allowing and encouraging households to achieve their goals and dreams over time.

Developing the Index

Each of the four domains is specified by indicators that capture a household’s ability to maximize its financial well-being. All the indicators have identical underlying characteristics:

1.    Efficiency: Relying largely on institutional data (i.e. CMS, CBS), rather than survey data. This will make data collection process continuous and cost effective
2.    Reliability and Validity: Ensuring consistency and exactness of measurement of the intended variable
3.    Timeliness: Easy to obtain and amenable for periodic updation
4.    Comparability: Fair measure of comparison across regions, taking into consideration region specific factors that may affect outcomes

Effort has been made to ensure that all indicators have these characteristics. However, efforts at refining and fine-tuning them, specifically for comparability, are underway.

The Domains

The table below gives an overview of the four domains and potential indicators. Column 3 offers the underlying rationale for using these indicators in assessing financial well-being of a household, while column 4 highlights how a movement in these indicators will effect the overall index. We discuss some of these indicators later.

*Assumptions for measuring the Sharpe Ratio will take into consideration diversification across financial and non-financial assets along with geographical diversification of physical assets, such as accounting for land within the district as opposed to land owned in another district.
**We account for value of human capital in NAV and include investments in education and other vocational courses made as contributing to human capital.

Protection: The ability of households to safeguard their assets and spread and manage their liabilities effectively.

To capture a household’s level of protection, independent of growth, and taking into account optimality and, individual needs and preferences, proved much harder than expected.

We decided to use the Life Wealth Envelope (LIWE) simulator developed by IFMR Trust, for advising households on the optimal levels of financial services they need, depending on their current and future financial needs. The graph below highlights how we can use the simulator to measure protection levels of a household.

The optimal level of protection is reflected through minimizing the area between the line reflecting cash flows in the best and worst states of the world, given their acquired level of insurance – life, health, accident and livestock. This is generated specific to each household, taking into account current income states and future financial needs, requirements and obligations. This measure also takes into account volatility in a household’s cash flow and can be updated regularly to reflect any change in a household’s financial state.

Liquidity: The ability of households to manage cash flows and maintain a balance between current and future requirements, to maximize the utility derived from lifecycle consumption.

To successfully manage liquidity, a household must be able to smoothen consumption and income over a period of time (Morduch, 1995). Given below is a method to comprehensively capture liquidity of a household: Standard Deviation of Monthly Consumption (adjusted for seasonality)

Consumption includes “all expenditures on nondurables plus imputed service flows from consumer durables, income, savings (net worth), and borrowed funds. It refers to that part of disposable income (income after taxes paid and transfer payments received) that is not included in savings”. Given this definition of consumption, we can even account for expenditure on education and other vocational courses while estimating cash flow of a household.

We recognize that accessing and gathering such data on consumption using institutional resources may be difficult.  However, given the need and effort made to smoothen consumption, this information is essential and the indicator above will help in aligning all future services and products for the estimation of household cash flows.

Diversification: Diversification is defined as the ability of households to reduce the volatility of return on the asset portfolio by investing in a variety of assets, including financial and non-financial.

Diversification is imperative to maintain an ideal risk-return portfolio that increases the probability of a smooth cash flow in all states of the world.

We use a variation of the Sharpe Ratio to measure diversification. Our assumptions take into account issues of extra-local versus local investments, along with diversification over physical and financial assets. The correlation and covariance matrices designed for this purpose will reflect all such assumptions. To measure improvements in a household’s level of diversification, we can measure the movement towards an ideal Sharpe Ratio overtime.

Growth: Growth may be defined as the ability of households to access capital and identify avenues to help increase their cash flow.

Measuring growth of a household over time is important to capture the effect of financial services on their overall financial wealth and worth. We identify two indicators to capture growth – Net Assets Value (NAV) and  using LIWE  by maximizing the area between wealth in the best states across time.

The NAV measure is an indicator of how much the household’s wealth has changed since last year. We include investments in education and other vocational courses as contributing to an increase in the total value of human capital, thereby increasing the net value of assets.

Our second option for measuring growth is to use the LIWE framework again and capture movement of the upper bound of the envelope. This will capture the impact on cash flows of a household over time, taking into account all assets, earnings and liabilities. An increase in the area between the current upper bound and previous upper bound will positively affect the index, as it will reflect growth in a household’s wealth.

Moving Forward

Moving forward, we must address a number of complex issues. We have managed to tackle some of them effectively by providing solutions to contentious issues such as measurement and collection, as well as conceptual and alignment issues. Questions that remain are those related to aggregation and normalization that are currently being discussed and debated. Some of the key questions we addressed are:

1. Do these indicators capture each domain effectively, taking into consideration a household’s short and long-term needs, while keeping in mind current status?

2. How can we make sure that the data collected is accurate and includes all attributes defined in the index? For instance, a dynamic indicator, such as consumption, is not only difficult to define but even harder to measure comprehensively and accurately.

3. Given the list of indicators, what method of aggregation can be used to construct an index without losing information and compromising on accuracy? The existing indices, such as the HDI and the Grameen PPI, are options worth considering, but cannot be taken as the final choice. We could give each indicator a score – a higher score if they are close to optimality and a lower score if they are further away. However, given that we have four domains, aggregating four scores to a single score may result in loss of information. Given this over-simplification process, a choice of a 4-number index, one for each domain may be a more comprehensive indicator of financial well-being.

In conclusion, the Index should represent and measure our mission of providing complete access to financial services to each household and enterprise, while maximizing their financial well-being. Building such an index is a complex task and is being done keeping in mind precision, fairness and comprehensibility.

By Arun Kumar & Anand Sahasranaman

With the novel objective of making Tamil Nadu the first ‘hut free State’ in the country by 2016, the state government launched a rural housing scheme called “Kalaignar Veedu Vazhangum Thittam” (KVVT) for rural areas with the objective of replacing all thatched houses in villages with robust all-weather houses over a 6-year period. The KVVT scheme comprises a grant of Rs. 75000 per house, which includes both cash and material components. Both cash and material grants are made in 4 tranches.

Only one problem!

Each tranche is disbursed upon the completion of a ‘stage’ of construction (such as upon the completion of the basement, or walls, etc), and clients have found it difficult to raise the finance required for the completion of each stage upfront.

To plug this gap, IFMR Rural Finance has devised a Housing Finance loan product that is currently being piloted in Alakkudi and Karambayam villages of Thanjavur district. This product serves as both a bridge and additional financing option for people eligible under the KVVT scheme.

The loan amount is in the range of Rs. 12000 to Rs. 25000, with monthly repayments and having a tenor of 3 years. One of the salient features of the product is that the customer can pre-pay or pre-close their loan when they get their tranche from the government for the portion constructed.

Mr. Shanmugavelu Mottaiyapillai of Alakudi, the first housing finance loan customer of Pudhuaaru KGFS who had availed a loan of Rs. 25000.

Amongst the basic pre-requisites for the loan are: 1) The customer should be eligible for the KVVT grant 2) The customer should have a clear title deed/patta for the property.

To our surprise, however, the need for a clear title deed wasn’t as simple as we thought.

Three weeks into the pilot we found that a majority of the residents of the two villages didn’t have proper title deeds/patta for their property, thus making it difficult for them to avail the loan. Taking this into consideration we had to make changes to our product eligibility requirements to suit some of the different scenarios as below:

• Land title in the name of client and few other people (siblings generally)
• Client’s land title in the name of deceased parent
• Client’s land title in spouse’s name but spouse is not available (out of country, state)

In special cases, where the government allots property to individuals, but passes on a certificate of ownership but not the title deed, we encourage such cases to avail a customised JLG Housing Finance loan than the regular one. This product has monthly repayments and a tenor of 18 months.

So far, out of 55 individuals in Karambayam and 26 individuals in Alakkudi who are eligible under the KVVT scheme norms, 9 in total have availed of our product. Working in close coordination with the local gram panchayat, the team intends to expand the pilot to other villages and adopt the learnings to refine the product. Watch this space for more updates on the progress.

By Chandrachudan V and Rajesh E, IFMR Rural Finance

For a business operation, a great deal of what happens on the field is determined by how equipped its field managers are. In our case, our Wealth Managers (WMs) at KGFSs (Pudhuaaru, Sahastradhara, Dhanei) are the primary interface in our endeavour to ensure access to finance.  Complete, continuous and accessible training therefore is a crucial ingredient in sharpening their skills at all times.

While the current training process involves a 24-day induction training and regular schedules of refresher training, this trainer-led effort with a lot of manual intervention provides a lot of challenges especially as it involves a lot of paperwork. To top this, there is no centralised repository that the WM could be directed to in order to stay updated on happenings and changes relating to products offered, processes and our USP of Wealth Management.

To bridge this gap, we hit upon the idea of an online learning management system which would be a one-stop shop for all learning needs featuring – product modules, process modules, Wealth Management modules, e-test and e-certification, storage of all training related information of each employee which later on can be factored into Performance Management of the respective employee.

Importantly this centralized learning environment will ensure consistency among learners, thus promoting web–based training that encourages self and participative learning, thereby, reducing trainer’s intervention for all training programs.

From ideation to fruition, it involved a lot of research in finding a suitable platform that could be customised to suit our training needs. The search was on to finalise a platform that is robust, user-friendly, easily customisable, secured, is being widely used across top companies with proven records and also a platform which is compliant to training standards like SCORM.  After 6 months of research, we decided on “Moodle”- a popular and prominent, free and open-source e-learning software. Moodle’s ease of installation, features and the level of customisation that could be done to it made it a perfect fit for our needs.

Learning Management System Architecture

Customised into 3 local languages (Tamil – Pudhuaaru KGFS; Oriya – Dhanei KGFS; Hindi – Sahastradhara KGFS) and in English, the learning management system would be a comprehensive tool that in its first phase would provide:

•    E – Learning on all products/processes and concepts of Wealth Management
•    E – Certification for all products/processes
•    Scheduling of training programs and maintaining repository of training data
•    Quiz and score management
•    Training dashboard automation: online extraction of training data – employee/geography wise
•    Automated mailer notifications for users
•    Feedback automation with effective reporting
•    Video based training on key aspects for better understanding
•    Internal chat for clarifying queries instantly
•    Discussion forums for knowledge sharing

Customised homepages of KGFSs (Click on image to enlarge)

The system would make it mandatory for the WMs to pass through the different certification programs on the various products, processes and concepts of wealth management. Also the system would update the training manager/CEO of a particular geography with macro and micro level information as regards the training status and the needs of the local staff so that they can evolve their training efforts.

The system will be live soon with the above-mentioned features. Improvisations and enhancements are planned in Phase II that will also include animated learning on all products/processes and concepts of Wealth Management, which will supplement the current modules.