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.


Estimating Loss Distribution for a Securitisation Transaction

In the latest edition of The Securitisation & Structured Finance Handbook 2018 (published by Capital Markets Intelligence) Vishal Saxena and Dilip Mohan from IFMR Capital have authored a chapter as part of the publication. The chapter discusses an approach to estimate the loss distribution for a loan portfolio. This loss distribution can be used to calculate the expected loss in an securitisation transaction, loan loss reserves, economic capital and value-at-risk. The authors first derive the limiting distribution of the portfolio loss as presented in papers by Vasicek (1987 & 1991) and then describe how they have extended the model to take care of the non-homogeneous subgroups in the portfolio.

Click here to download the paper.


The Nature of Financial Advice for Low-income Households

By Bindu Ananth

I was at an excellent behavioural finance conference organised by the Michigan University’s Centre on Finance, Law & Policy last week. One of the panels on investor protection debated issues including the impacts of disclosures, choice architecture and social norms marketing on investor behaviour. There was also an interesting discussion on role of advice and advisors in de-biasing investors or exacerbating weaknesses.

In the audience Q & A, in response to a question on the role of financial advice for low-income investors, one of the panelists responded that failures in the market for advice were less of an issue here since by and large, the right answer in most cases is just “save more for the future”. I found myself disagreeing with this notion strongly and one more reminder that the field of household finance has failed to examine the financial lives of low-income families in sufficient detail. In this post, I attempt to share from our KGFS work what are some of the other important aspects where advice seems to matter.

One, given that human capital (NPV of net lifetime earnings) dominates financial capital (wealth) for a low-income household, all of the issues around protecting that human capital is critical because that might make the difference between bankruptcy & resilience in the face of illness/accident/death. Most advice tends to focus on investments and the portfolio allocation question and surprisingly, pays little attention to insurance. Ibbotson et al (2007) provide a comprehensive framework to understand how human capital interacts with investment and insurance decisions. With limited resources, which members of the household should buy insurance? How much insurance should you buy? We find these are important aspects where households benefit from good advice. Specifically, insuring young, adult members of the household for the full value of their human capital is an important step. (One dilemma we faced was that a significant investment in increasing human capital that is made by households is higher education for children. The return to this investment depends greatly on the specific program and employability potential. We did not have the expertise to advise clients on this aspect but it feels like an area closely linked to the role of a financial advisor in this context)

Second, low-income households are typically saving and borrowing simultaneously despite a significant wedge between lending and savings rates (upwards of 20% most times). We don’t understand very well the determinants of this behaviour. Clearly, it is not always the right answer to save. High rates of return on micro-enterprises have been documented by Christopher Woodruff and others. Often it makes sense for households, particularly with surplus labour to borrow to put together the initial capital required to undertake such enterprises. Similarly, households with low but stable cash-flows (the village municipal worker for instance) may find it reasonable to borrow to build a house rather than wait to save up for the same. Working with the household to determine when to borrow and when to save and even combination strategies such as save for the down payment or borrow to save strategies could be very valuable interventions.

Third, the balance sheet of a low-income household has a combination of physical and financial assets. Physical assets such as land and gold dominate. On the liabilities side, there is a combination of formal and informal loans of different maturities. It requires serious skill to arrive at the APR of some informal loans! Which loans to refinance now that advances in financial inclusion are making formal credit more accessible? Which assets may be “dud assets” (ex: a piece of land that is not being cultivated) that could be sold to bring down debt burden? Which loans have a repayment structure that adds to the financial stress of the household? Working with the household to arrive at this comprehensive “balance sheet view” seems like an important role of an advisor.

Of course, there are significant challenges in converting advice into action and requires more careful work and business model experimentation. Equally, careful research and creating the building blocks for good advice for low-income households is also necessary and cannot be extensions of existing advice frameworks. The myth that these households have simple problems that require simple fixes & simple products needs to be challenged by researchers and pioneering providers.


Stress Testing Methodology – Brief Comparison Across Regulators

By Nishanth K & Madhu Srinivas, IFMR Finance Foundation

The below table summarises, along some key dimensions, the stress testing methodologies adopted by the central banks in India, US, UK and EU to assess the stability of their banking system. It is to be noted here that the stress tests that individual banks conduct by themselves, as part of their Internal Capital Adequacy and Assessment Process (ICAAP), do not figure in our comparison. Also the below analysis is based on the stability/stress test reports of the respective regulators for the year 2016.

All data for the above comparison was taken from the following references:

Click here for PDF of the infographic.


Natural Catastrophe Insurance – In Conversation with Mr. Ulrich Hess

By Vipul Sekhsaria, IFMR Holdings

In the below video we share a brief conversation with Mr. Ulrich Hess, GIZ. Mr. Hess is currently a Senior Advisor, InsuResilience Initiative at GIZ, and has worked extensively in the field of natural catastrophe risk insurance market. In the video he shares his insights on the impact of natural disasters on the livelihoods of households and the risks associated with it. He also talks about the challenges in designing a natural catastrophe insurance product and addressing issues associated with both inefficiencies and effective delivery of the product.