Privacy on the Line: What do Indians think about privacy & data protection?

This post is authored by the Future of Finance Team at Dvara Research.

We met Sulekha[1] in a village in Uttarakhand. She was talking about the information she considered most important to her: her ration card, Aadhaar card, NREGA job card and her phone number. When asked how much she would sell this information for, she visibly withdrew saying she did not want any money for it. What would she need to share this information? She replied simply: a guarantee that it would not be misused.

Sulekha was one of the 50 people we spoke to as part of a small, deeply qualitative study on which the Future of Finance Initiative (FFI) at Dvara Research partnered with Dalberg Design and CGAP. We set out to understand: ‘how do ordinary citizens of India think and act on their privacy and data protection?’ Across four regions of the country (Maharashtra, Uttarakhand, Tamil Nadu and Delhi) we used the Human Centred Design (HCD) method to have discussions to understand not just what people say, but how they think, act and feel. The final report on the study is available here.

Our conversations in the field revealed that contrary to common perception, people in India care deeply about their personal data and privacy. Respondents were surprised that service providers could share their personal information with third parties and wanted to be informed of such sharing. People were also sensitive about sharing their personal data such as photos, messages and browsing histories—even with their family—and were unwilling to sell certain types of personal data like their telephone numbers.

Even the data that they were willing to share in order to receive services came with conditions. People wanted to know how their data was handled. They also, much like Sulekha, wanted an assurance from providers that no harm would come to them through the use of their data. Many of the interviewees recognised their inability to understand standard notice clauses and wanted more visual forms of consent that they could easily understand without relying on others.

Alarmingly, most interviewees had experienced fraud (especially via phone impersonators), and did not know how to protect themselves or seek redressal. Women, in particular, were highly vulnerable to reputational harms, and self-censored themselves (for example by not sharing phone numbers or photos) to protect themselves.

Although the government and its institutions inspired universal trust, people working in government institutions were not trusted with personal data – unless the employees came from the same community group or geographic area. Agents of banks and mobile network providers were also recognised as common perpetrators of illicit disclosures of personal data.

In cases where harm was caused to them as a result of a data breach, the respondents wanted easy access to seek redressal, and wanted to be compensated fully.

We heard individuals asserting their right to have their personal information treated responsibly. They indicated clear and strong preferences for a system that provides them agency and control over their data. Citizens at the grassroots want a data protection regime where providers are held accountable and are obligated to treat personal data responsibly.

You can read the full report here and watch the below video on the study.

[1] Name changed. Note: The details of the respondents in the main report were included with their permission and after informing them that a report would be released on this topic.


Notes from a Kenya trip

By Bindu Ananth

Last week, the leadership team of the IFMR Group had an opportunity to visit Kenya and among other things, it was a fantastic opportunity for us to get a sense of the trajectory of Kenya’s financial sector development relative to ours in India. This post is intended to share some of our broad impressions.

We arrived to headlines of a banking crisis in the local newspapers. Chase Bank, Kenya’s 11th largest Bank, experienced a run on the bank and had just been placed in receivership – the third such bank in the last few months. The issues seemed idiosyncratic – bad loans and governance failures and also appeared limited to the small-sized banks. Kenya has 42 commercial banks for a population of ~ 44 mn. (Contrast with India’s 100 or so Scheduled Commercial Banks for a population of 1.2 billion). In the meanwhile, new bank licenses have been put on hold by Governor Njoroge.

We spent a couple of hours understanding the business model of M-Kopa (excellent FT interview here). They have developed an excellent pay-as-you go solar energy solution for households who are off-grid and the service is seamlessly integrated with M-Pesa (the founding team is heavily drawn from Safaricom), rendering it a fully cashless operation. Interestingly, since the SIM-controlled unit gives them a “kill switch”, they are able to leverage the household’s eventual ownership of the solar unit to also finance other consumer durables such as water tanks and cook stoves. A neat solution to moral hazard! They have also launched a personal finance product that appeared to be in the same category as M-Shwari, the Commercial Bank of Africa’s unsecured loan product based on M-Pesa usage patterns that has rapidly scaled up since launch. The CFO explained to us their working capital cycle linked to consumer behaviour on payments (inevitable delays and non-trivial but stable write-offs) and the severe constraints on local financing options. In their case, all consumer payments directly are escrowed by the partner bank, eliminating servicer risk on M-Kopa although product risk remains key.


We were excited to hear their plans to launch in India although significant work will be required to replicate the cashless environment and to co-exist sustainably with the Indian Government’s rural electrification drive. We were very impressed with M-Kopa’s mission focus and the small, thoughtful touches everywhere on their beautiful campus. See this exhibit for instance:

This is the amount of kerosene that a typical rural Kenyan household uses that can be substituted for by an M-Kopa unit.

M-Pesa continues to be a significant driver of the financial system. Findex (2014) reports that almost 60% of adults in Kenya above the age of 15 have a mobile money account while that number in India is about 3%. It took us a passport and less than 5 minutes to open an account and start transacting with an agent near the airport. We overheard that ~ 40% of the Kenyan GDP now flows through M-Pesa. Even the Post Bank of Kenya, who we briefly met, uses the M-Pesa network. While the concerns on market dominance continue, the Kenyan Government apparently owns 35% in Safaricom Kenya now as a strategy to limit this risk.

In the Mor Committee’s Report, we talk about our vision of a “Payments Highway” – comprising a shared back-end of Aadhar, e-KYC, AEPS, UPI and the Payment Banks providing the agent networks for cash-in/cash-out. The belief is that once this infrastructure is in place, several users including lenders, consumer goods distributors and so on could leverage it seamlessly for payments without having to invest in creating a proprietary network. In Kenya, it felt like we saw this Payment Highway in action; with an important design difference though. Despite our significantly later start, if Payment Banks in India are able to pull off activation of a nation-wide agent network in the next few months, we would have a system characterised by publicly owned infrastructure at the back-end and atleast a few private players managing agent networks. In Kenya, the infrastructure and the agent networks are all controlled by a single provider. Our design should, in theory, enable more competitive outcomes for consumers.


Microfinance through a Data Lens

By Vaibhav Anand & Aryasilpa Adhikari, IFMR Capital

In the last five years, the microfinance sector in India has grown into a stable and well-regulated sector thanks to a strengthened regulatory framework and credit bureau infrastructure. The regulatory oversight on NBFC MFIs and the advent of the microfinance credit bureau infrastructure with mandatory reporting requirements have made it possible to go beyond anecdotal evidence and analyse these issues in a robust manner which is the objective of this post. NBFC MFIs are generally registered with Equifax and/or Highmark to do credit bureau checks and report credit information on their portfolio periodically. Under RBI guidelines, NBFC MFIs cannot lend to a customer who already has two MFI lenders or whose total indebtedness exceeds INR 1,00,000. Gaps in the information available on bank lending to microfinance clients through the Self Help Group (‘SHG’) model are expected to be addressed over a period of time as banks start complying with the recent RBI guidelines relating to bank reporting on their SHG portfolio. NBFC MFIs also make higher provisioning including on standard assets of 1%. The silos in credit bureau market infrastructure are also expected to reduce as all credit institutions start reporting information to all credit bureaus under the guidelines issued by RBI in January 2015.

How far has the microfinance sector come since 2010? What is the quality of growth of rapidly growing MFIs? Is growth adversely affecting customer well-being? In this post, we attempt to understand these issues based on rigorous data. Given that a large proportion of excluded Indians, women in particular, depend on MFIs for their only source of financing, conclusions must be drawn with a great deal of responsibility.

Sources of data/information

Field observations discussed in this blog are based on over 200 field surveillance and monitoring visits conducted by IFMR Capital since April 2013. During these regular visits, the team covered more than 1000 microfinance centre meetings across 200 districts and 20 states, including interview-cum-discussion meetings with branch managers, loan officers, JLG borrowers and senior management at Head Office.

The microfinance sector performance data discussed in this blog is based on mainly two sources: (a) Microfinance sector level pincode reports for more than 500 districts subscribed from Equifax, one of the largest microfinance credit information bureaus in India, and (b) Performance data on microfinance portfolio based transactions, with underlying microfinance loans worth more than INR 3500 crores, structured by IFMR Capital.

Question 1: Is pipelining rampant?

Credit pipelining is the practice of borrowers routing the availed loan amount to another person, who may be a member of the group or a third person, referred to as beneficiary. The beneficiary usually uses the money for her/his own purpose and makes periodic repayments through the group. In lieu of their KYC and attendance at the centre meetings, borrowers may receive compensation (commission) from the beneficiary. If repayments are regular, it is difficult to identify such cases; it is only when the beneficiary is under financial distress and he/she finds it difficult to repay instalments, that pipelining is discovered. Such pipelining incidences are neither uncommon nor limited to a particular district, region or state. Credit pipelining, as discussed subsequently in this blog, is often a result of operational process dilution. Sporadic cases are observed across states during regular monitoring visits by IFMR Capital.

The recently highlighted instances (link) of credit pipelining and multiple lending, and events which unfolded subsequently in a village in Eastern UP are extremely unfortunate. Such instances undoubtedly require prompt and effective corrective action that must be institutionalised at the highest levels in an MFI. We completely disapprove practices that result in such instances and our position towards these is enshrined in our underwriting guidelines. While we continue seeing sporadic episodes across the country, our field analysis and data however don’t support the wide-spread nature of such issues. Based on pincode level data, we drilled down to district level and looked at the trend of portfolio performance, portfolio growth and multiple lending for the three eastern UP districts in recent focus – Azamgarh, Chandauli and Varanasi.   The data does not show evidence of excessive multiple lending by MFIs in these districts with less than 3% clients having more than two MFI loans.

Portfolio growth, proportion of clients with more than two MFI loans and delinquency levels in Azamgarh, Chandauli and Varanasi
*Source: Equifax

It is important to note that the underlying factors behind pipelining as well as the mitigating steps needed to curb pipelining are not new to the sector. Though it is true that pipelining is difficult to uncover, sooner or later such cases result in repayment delays by the involved members bringing these to the attention of the lender. Credit pipelining is often seen in centres where some or all of the following factors are present: (a) borrowers are not aware of the consequences of payment default such as negative profile in credit bureau and possible denial of credit in future, (b) Loan utilization checks are weak, (c) dilution of group formation and origination processes such as group recognition test (GRT) and continuous group training (CGT) and (d) MFI loan officer’s reliance on a single centre member (‘centre leader’) for centre operations such as group formation and collection of payments during the centre meeting. It is true that increasing competition and pressure to raise loan officer productivity may result in dilution in key processes and reliance on influential centre lenders.

In our discussions with management during monitoring visits, we have found that MFIs are aware of the underlying factors which result in such incidents and are increasingly focusing on mitigating mechanisms such as (i) Ceiling on origination linked incentives for loan officers (ii) disbursement conditional on strict compliance to CGT and GRT (iii) residence verification and meeting borrower’s family or spouse to ascertain loan utilization and to restate implications of credit default (iv) adherence to loan utilization checks (v) compliance to credit bureau processes and (vi) strengthening internal audit.

Question 2: Is there overheating?

Another concern discussed often in microfinance is over-indebtedness and the issue of borrowing by JLG members from multiple sources including MFIs. During monitoring visits to our NBFC MFI partners, we have observed a very high compliance to regulatory guidelines on multiple lending and borrower indebtedness across the sector. While the challenge posed by the usage of multiple KYCs by borrower is not unfounded, our understanding is that the prevalence and impact is limited. In addition to KYC identifiers, microfinance credit bureaus also rely on advanced algorithms to track borrowers in their database by matching borrower name, spouse or relative name, address string and address pincode. Based on the loan level portfolio scrub data queried from one of the credit bureaus, we observed that same client with different KYC documents and ID numbers was identified based on name and address string. Such algorithms reduce the risk of multiple lending due to multiple KYC IDs, alerting the lending institution on matches found in the credit bureau information even when different KYCs are used.

We used pincode level microfinance loan performance data available for nearly 6000 pincodes across more than 500 districts, to measure the prevalence and impact of multiple lending. We define multiple lending as availing of more than two MFI loans by a single client at a given time. We measured multiple lending in a district as the proportion of clients with more than two MFI loans.

Our findings suggest that a median multiple lending of 2.35% and 1.50% as of Mar-2015 and Mar-2014 respectively. For 90% of districts, which incidentally also account for 90% of microfinance portfolio, the multiple lending measured is less than 9% as of Mar-2015 (up from 6% as of Mar-2014). It should be noted that our measure of MFI multiple lending is fairly conservative. Even during monitoring visits, we often see clients with three MFI loans. One of the primary reasons is the lag between the disbursal of a new loan and its reporting to the credit bureau by the MFI. During such period, the client may receive loan from another MFI (or MFIs) as credit bureau check may not show the existing loans accurately in the short transition period. However, the prevalence of this operational issue is limited. With many NBFC MFIs moving to weekly sharing of performance data with credit bureaus, we expect to see a decline in multiple loan instances due to such operational reasons.

Proportion of more than 2 MFI loans in districts
*Source: Equifax

We also checked for the microfinance portfolio being originated in districts with different proportion of multiple lending. In simple words: are districts with higher multiple lending contributing disproportionately more to the sector portfolio outstanding? The graph below shows that nearly 90% of portfolio is originated in districts where less than 9% clients have more than 2 loans from MFIs. Similarly, nearly 95% of portfolio is originated in districts where less than 15% clients have more than 2 loans from MFIs. Certainly, this data does not account for loans taken by borrowers under SHG scheme as well as loans from non-NBFC MFIs, private money lenders, and other such sources which do not report to credit bureaus. Also, the data may include limited microfinance portfolio originated by banks through business correspondents.

Cumulative portfolio contribution by districts with multiple lending

*Source: Equifax

Additionally, we also look at portfolio performance in districts with high proportion of clients with more than 2 MFI loans. We found little overlap between the top ten percentile districts with highest delinquencies and districts with highest proportion of clients with more than 2 MFI loans as of Mar-2015.

Top 10 percentile districts: Delinquency vs Clients with >2 MFI Loans

*Source: Equifax
**Grey dots show districts with MFI presence. The representation is to show microfinance presence and is not an official map of the country or state.

Question 3: Is growth affecting credit quality?

We looked at data to see if higher growth would result in process dilution and subsequently lower quality portfolio. The performance of microfinance portfolio transactions structure by IFMR Capital shows a significant improvement over the last few years.

Loss and Default percent on microfinance portfolio transactions structured by IFMR Capital (based on 286 pools underlying loans worth INR 3.5 thousand crores)
*Source: IFMR Capital

Sector level microfinance performance data also substantiates a stable and healthy growth for the sector. Based on the district level portfolio growth from FY13-14 to FY14-15 and fresh delinquency occurrence (loans overdue by 0 to 180 days) as of FY14-15, we identified the top 10 percentile and top 20 percentile districts in both categories. One would expect that if portfolio growth is unrestrained in a district, it would result in higher delinquency. We found little overlap between the two group of districts, i.e. top growth and top delinquent districts, suggesting that growth is higher but not unrestrained, and is not necessarily resulting in process dilution and subsequent lower quality portfolio.

Comparison of top portfolio growth and top delinquency districts
*Source: Equifax
**Grey dots show districts with MFI presence. The representation is to show microfinance presence and is not an official map of the country or state.


Our data does not support wide-spread prevalence of pipelining or over-lending. Neither does it support deterioration in portfolio quality due to growth. The effective implementation and usage of credit information bureaus by MFIs is key to controlling these issues. Challenges related to KYC documents of borrowers exist. Using technology, credit bureaus have tried to address this problem by matching borrower and spouse names as well as address strings to identify duplicate borrowers in the system. Further, to mitigate the risk of using multiple KYC documents, MFIN has provided guidelines to all member MFIs to adopt the Aadhaar number as the unique KYC identifier over the next two years. There is also a need to bring other sources of credit in the formal regulated regime. Credit performance data under the self-help group (SHG) program should also be brought onto the credit bureau reporting system to ensure complete visibility on indebtedness and credit performance. In a significant step towards ensuring this, RBI recently issued another circular (link) directing banks to complete reporting of SHG data to credit bureaus in two phases by July 2017.


Difference in Client Response to Micro-Lending – Few observations

By Aryasilpa Adhikari, IFMR Capital

The post focuses on the specific differences in client’s response to various aspects and attributes of micro-lending principles and practice, in rural and urban areas. It is based on observations* from the field visits done as part of our regular monitoring visits.

The complete oeuvre of micro-finance was conceptualised and framed by Prof. Yunus with the strong premise of “Poor are Bankable”. This was designed with a strong sense of imbedded social collateral (in place of conventional collateral), group discipline and small but frequent repayments. This was started with a belief that when poor are provided with timely credit, they try their best to make productive use of it. In practice it was ensured that the principles are followed in day-to-day operations of Grameen Bank. When the micro-lending model was replicated in India, the micro-finance service providers started operations in rural area (which had very similar socio-economic texture as it was in Bangladesh) and religiously practiced the principles preached by Prof. Yunus.

Processes and procedures aligned to the premises and principles of micro-credit were developed and practised in full rigour. Checks and balances were slowly built into the operation that has resulted in a matured micro-finance industry. Operations are now largely standardised across different players in the industry – client data sharing with credit bureaus (HighMark & Equifax) is a norm now; the industry has MFIN as an SRO, which actively engages with various stakeholders within the industry and the RBI as the regulatory body for NBFC-MFIs that defines the larger regulatory and compliance framework for them. Post-Andhra Pradesh crisis (October 2010), the industry has rebounded lucratively and as of today promises, significant growth opportunities, both in terms of attractive investment opportunities for investors and financial sustainability and the ability to realise the social dream of achieving financial inclusion and institutional outreach.

The target customer segment of the micro-finance service provider over the last few years has gradually moved towards low-income households in urban areas. The service providers have tried to replicate the process and procedures as they used to do in the rural context. However, the response of the urban client to the process and procedure is not the same as it was by rural clients. The group dynamics is not as prominent in urban set-up as it is in the rural set-up. The nature of peer pressure and repayment in case of group guarantee invocation varies across rural and urban area. The appetite for higher ticket size loan, the loan use pattern and the underlying mechanism and dynamics that govern the loan utilisation are significantly different.

The following table highlights some of the difference in response, to the premises and principles of providing micro-credit. These are based on some of the observations gathered during visits to client locations of NBFC MFIs.




Rural Set up


Urban Set-up

Importance of group


Clients perceive affiliation to group (which is also an indicator of socio-economic status) as crucial to their access to credit. In the absence of group, the client has hardly any access to reliable, affordable formal credit due to poor penetration of formal financial institutions in rural areas.


Clients perceive “groups” as a means to an accessible loan; Micro-credit in urban set-up is easier for clients to access because of the absence of collateral and ease of repayment – staffs collects the instalment at doorstep. In the absence of group, clients can still approach banks for loan although this is less accessible.

Discipline (in terms of attendance, group register maintenance)


Clients perceive presence in centre meeting an important task and manage their time for daily chores in a way, which allows them to attend meetings. Attendance is on an average 90-100%. Practices that reinforce group bonding such as reciting group pledge etc. is routinely followed. Group documents are properly maintained.

Attending centre meeting is not the key priority of the clients (as most of the clients work outside their house). Centre attendance may be as low as 25-30% in some locations. Reciting group promises is barely done, which in a way is indicative of how strong is the group adherence.
Repayment and Invocation of Group guarantee (GG)


While repayment responsibility is largely of the woman (the borrower), the repayment is largely funded by supply of repayment instalment from household’s occupation as a whole.

In the event of earning member’s unwillingness/inability to supply the requisite funds, women borrower substitute funds from their ‘expenditure-saving’ activities, daily subsistence resources and resort to distressed consumption. In case, of GG invocation, the shared instalment of the defaulting member is actually paid from collective household income of other group members.


Since most of the borrowers in urban set-up are employed – either self-employed or offer service, repayment of instalment of defaulting member, in case of GG invocation, is largely met by the woman herself through her own individual income.

Peer pressure


The key driver of default avoidance is stress generated by peer group pressure to avoid default on loans by any of their members with the prime objective to maintain group’s future creditworthiness. As member assign social status in adhering to specific groups, rarely do members consider moving to new groups.


The key driver of default avoidance is client’s concern about individual credit worthiness (calculated in terms of DPD (Days past due) count Highmark report); peer group pressure does not play as important role as it assumes in rural set-up. Clients find alternative groups and usually have floating loyalty towards group membership.

Appetite for higher ticket size loan


Clients asking for higher ticket size loan are guided by their understanding of how well they can utilise the loan (either for consumptive or productive purpose). Very often, customers do mention that they do not want to take loan unless they foresee a real need for cash. Clients do understand that higher ticket size loan translates to higher instalment amount, which may or may not be, under their loan servicing ability.


Urban clients have relatively higher appetite for higher ticket size loan. This is based on the client’s understanding of certainty and regularity of her domestic cash flows and the potential increase in income, which she perceives because of the productive usage of offered loan.

It may be necessary for micro-credit service providers catering to urban customer segment to identify, understand and appreciate these differences and design products, processes, procedures and practices based on an in-depth understanding of urban-client behaviour. It will benefit both the service providers and clients equally, as suitably designed micro-credit/finance service delivery will ensure increased acceptability and enhanced efficiency within the service provider as well as more effective risk management practices.

* – These observations are based on interactions with around 200 rural customers in the rural areas of Uttar Pradesh, Madhya Pradesh, Gujarat and 140 urban clients from cities like Mumbai, Pune and Bhopal.


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.