Digital Credit In Tanzania: Customer Experiences & Emerging Risks

28 Feb 2018

In June-August, 2017, CGAP, with inputs from FSD Tanzania, conducted surveys in Tanzania to identify customer experiences with digital credit, including uses of digital credit, emerging risks, and the size of the digital credit market. The surveys consisted of:

  • Nationally representative phone survey (representative of phone owners), of N=4,574 Tanzanians.
    • The sample was drawn from random digit dial technique, which relies on a randomly-generated list of mobile phone numbers in the country.
    • The sample is weighted to be representative of adult phone owners in Tanzania.
  • Booster surveys with select lenders, with sample sizes ranging from N=300 to N=500.
    • The samples were drawn from randomly selected customer lists from each lender.
  • The fieldwork was conducted by Innovations in Poverty Action.

CGAP partnered with FSD Kenya to conduct comparable surveys in Kenya, and subsequent analysis will compare market developments in the two countries.

This presentation reports on the findings from the nationally representative sample of phone owners in Tanzania, and includes comparisons to the FinScope 2017 dataset.

Fraud in Mobile Financial Services: Protecting Consumers, Providers, and the System

09 Sep 2017

This Brief highlights how fraud is impacting mobile money providers, agents, and consumers, as well as efforts to reduce risks and vulnerabilities to fraud in mobile money and related services. While it is not possible to remove fraud entirely from any service—mobile money included—the examples addressed here show that fraud is a major issue in several key markets for consumers and agents, and that there are simple steps providers can take to reduce their vulnerability to common fraud types.

These steps include improving internal controls, building agent capacity to protect themselves and their customers, and revisiting procedures such as account access and SIM swaps, where necessary, to prevent common fraud schemes. With the introduction of new products and delivery channels, the types of fraud will continue to evolve, which means that monitoring mechanisms, such as compliance checks and customer feedback channels, will continue to be key elements to effective fraud and risk mitigation.

Building a Secure and Inclusive Global Financial Ecosystem

08 Sep 2017

The 2017 Brookings Financial and Digital Inclusion Project (FDIP) report evaluates access to and usage of affordable financial services by underserved people across 26 geographically, politically, and economically diverse countries. The report assesses these countries’ financial inclusion ecosystems based on four dimensions of financial inclusion: country commitment, mobile capacity, regulatory environment, and adoption of selected traditional and digital financial services.  The report further examines key developments in the global financial inclusion landscape, highlights selected financial inclusion initiatives within the 26 FDIP countries over the previous year, and provides targeted recommendations aimed at advancing financial inclusion.

Enabling Mobile Money Policies in Tanzania

29 Jun 2016

The National Payment System directorate (nPSd) at the Bank of Tanzania (Bot) began its mobile money regulatory journey i 2008, when a visit from one of the country’s mobile network operators (mnos) introduced the idea that a simple mobile handset could do much more than make calls. From this first meeting, the Bot was keen to engage with the mobile industry to learn more about the potential of digital financial inclusion – a new and unfamiliar topic to the Bank.

Seeking to enable digital financial inclusion, but lacking national payment systems legislation to issue regulations, the Bot elected to take an interim step. it issued ‘letters of no objection’ 1 to the partner banks of vodacom’s m-PeSa and Zantel’s Z-Pesa (relaunched in 2012 as “ezy Pesa”), allowing them to launch in 2008. two more deployments followed: Zain’s Zap in 2009 and tigo Pesa in 2010. as the market has continued to develop, the Bot has made concerted efforts to find a legal and regulatory framework that would provide sufficient legal certainty and consistency to support a stable mobile money market, promote financial inclusion, and protect customer draft regulation that allows both banks and non-banks to provide mobile payment services has gone through two iterations and will be adopted. meanwhile, the Bot has taken the lead in developing a national Financial inclusion Framework (nFiF) that articulates the role of mobile money as a key enabler of financial inclusion.

Helping or Hurting? 10 Facts About Digital Credit in Tanzania

Michelle Kaffenberger
28 Feb 2018

Digital credit is a growing phenomenon, surrounded by much excitement and expectation that quick access to credit could help poor households, especially when faced with emergencies or other financial needs. A new nationally representative phone survey conducted by CGAP in Tanzania examines whether it is living up to this hype. We interviewed more than 4,500 Tanzanians, including 1,132 digital credit users, to understand who is using digital credit, how it fits into borrowers’ portfolios, and what risks are emerging. Our findings suggest that while borrowers are using digital credit to meet everyday needs, few turn to it for emergencies. We also find that nearly a third of digital borrowers have defaulted on a digital loan, and more than half have repaid a loan late.

Digital credit has been available in Tanzania since 2014, with the launch of M-Pawa which now boasts nearly 5 million subscribers. As our findings show, while digital credit has the potential to bring benefits to low-income individuals with limited access to formal credit, it also carries potential risks that need to be balanced and understood. Better understanding digital borrowers and their experiences with loans is critical for determining steps regulators and providers should take to maximize benefits and mitigate risks.

Who is using digital credit?

  • A fifth of Tanzanian phone owners have taken out a digital loan. The digital credit market is dominated by three lenders: M-Pawa (which 48 percent of digital borrowers have used), Airtel Timiza (39 percent), and Tigo Nivushe (29 percent); numbers add to more than 100 percent as many borrowers have used more than one digital lender.
  • Digital borrowers tend to be men between the ages of 26 and 45. They tend to be better educated than people who have not used digital credit, but still the majority have only primary education. They are more likely than the average person to live in urban areas.
  • Digital borrowers are much more likely to have used banks and other financial services. They are three times more likely to have a bank account than the typical Tanzanian adult (44 percent vs. 13 percent, respectively), and 11 times more likely to have taken a bank loan (11 percent vs. 1 percent).

Nationally representative sample of N=4,574 phone owners in Tanzania, of whom 1,132 have used digital credit. “All Tanzanian adults” was calculated based on the nationally representative FinScope survey dataset with sample N=9,459. Multiple responses allowed.

  • While most digital borrowers are self-employed, other income sources are mixed. Seventy-two percent are self-employed, compared with just 15 percent of Tanzanian adults overall. A large portion of digital borrowers rely to some degree on income from family, friends or government aid transfers (“dependents” in the graph). Yet digital borrowers are also more likely than average to be wage earners, which is correlated with higher-than-average education levels. This suggests that digital credit is reaching a few distinct segments: a segment of educated wage earners, a potentially less-well-off segment that depends on others for income, and segments that fall somewhere in between.

Nationally representative sample of N=4,574 phone owners in Tanzania, of whom 1,132 have used digital credit. “All Tanzanian adults” was calculated based on the nationally representative FinScope survey dataset with sample N=9,459. Multiple responses allowed.

How is digital credit being used?

  • Although most digital borrowers are self-employed, only a third report using a digital loan for business purposes. Even within the self-employed segment, fewer than 40 percent of borrowers report using a digital loan for business. Digital loans are most commonly used to meet ordinary household needs and to purchase airtime. Only 9 percent of borrowers report having used the loans for medical expenses, including emergencies, and fewer than 1 percent have used them for any other type of emergency. The only use case for which digital credit is more commonly used than other sources of credit (informal or formal) is for purchasing airtime.

Numbers based on nationally representative sample of N=4,574 phone owners in Tanzania, of whom 1,132 have used digital credit.

  • Digital credit primarily complements existing loan sources. Two-thirds of digital borrowers have not reduced their use of other loan sources since gaining access to digital credit, while one-third has.
  • Digital credit is a frequently used part of borrowers’ portfolios. Sixty percent of digital borrowers had at least one outstanding digital loan at the time of the survey, and 67 percent had taken a loan out in the last 90 days. This active rate is much higher than the 31 percent typical of mobile money accounts.

What risks are emerging from digital credit use?

  • Late repayment and default are widespread. Nearly a third of digital borrowers have defaulted on a digital loan, and more than half have repaid late. Late repayments and defaults are surprisingly consistent across segments. While casual workers and those reliant on income from others are the most likely to have defaulted, even among employed borrowers and those with tertiary education a quarter have defaulted. And this is just the percentage that admitted to defaulting, so the true figure could be even higher.

Numbers based on nationally representative sample of N=4,574 phone owners in Tanzania, of whom 1,132 have used digital credit.

  • Poor transparency means borrowers may not understand fees or repayment requirements. Just over a quarter of digital borrowers report that they were charged fees they didn’t expect, that they did not fully understand the costs associated with a loan, or that a lender unexpectedly withdrew money from their account. Poor transparency has follow-on repercussions, as those who reported poor transparency were more likely to have repaid a loan late or defaulted.
  • Nearly 10 percent of digital borrowers report that they have reduced food purchases to repay a loan. This group is more likely to have defaulted on a loan (37 percent) and more likely to have repaid late (82 percent). They are also more likely to have been balancing multiple loans at the same time, suggesting that they may be carrying too heavy of a debt load (though further research would be needed to rigorously assess the impact of digital credit on borrower financial portfolios).

While digital credit may be providing benefits for many borrowers, perhaps smoothing consumption when it is used for household needs, it doesn’t seem to be living up to the hype of helping households cope with medical expenses, emergencies or paying school fees when income is tight. And there seems to be at least one segment for whom it may be having detrimental effects, as families struggle to purchase food while repaying. Smart regulations, such as requiring better transparency as well as suitability and needs assessment rules, and proactive steps by providers, such as better identifying borrower repayment abilities to limit over-indebtedness, could go a long way towards maximizing benefits and minimizing risks as the digital credit market evolves.

Note: This post was originally published on CGAP website.

To learn more about the Tanzania phone survey, see Digital Credit in Tanzania: Customer Experiences and Emerging Risks. CGAP conducted the same survey in Kenya in partnership with FSD Kenya. Future analysis will compare market development and customer experiences in the two countries.

Photo: Hendri Lombard / World Bank

Interactive SMS Drives Digital Savings and Borrowing in Tanzania

By Rafe Mazer
21 Aug 2016

For anyone who has ever stood in front of a classroom after delivering a lesson, the question “what did they learn, and what difference will it make?” has surely come to mind.

In many cases, this is a black box, including when it comes to traditional financial education, where in-person trainings are often delinked from the desired actions consumers will take – they may learn about interest rates now, but won’t need to borrow for a few months, or even years. This may be why recent analysis (see, for example, Fernandes [2014]) has questioned the behavioral change that traditional financial education programs can have.

But what if you could deliver the learning content at the moment in time it is most needed, and directly monitor not only how the content was accessed but its subsequent impact on actual financial behavior?

This was the premise behind an interactive SMS project for users of M-Pawa, Vodacom and Commercial Bank of Africa’s fully digital mobile money savings and credit product. The platform was run by Arifu, a personalized learning tool that provides customized learning content based on consumers’ preferences and responses. The project targeted farmers in rural Tanzania, as part of the Connected Farmers Alliance, who were receiving in-person trainings.

Using the farmers’ own feedback from initial user testing, CGAP, Arifu, TechnoServe, Vodacom and the Busara Centre for Behavioral Research developed a series of interactive SMS scripts that let farmers guide their own learning on their phones, and to do so when they wanted and on the content they wanted.

For example, those more interested in loans could learn how to check their loan limit or how to use a cost calculator tool; while those interested in savings could read a story of a farmer like them who saved on M-Pawa for their business or set their own personal savings goal.

The team also tested a range of different types of behavioral messages to drive uptake of the SMS content and different learning approaches – narrative, facts, introducing the Arifu name in the SMS as a personal learning guide to increase personalization of the experience – to see which messages worked best.

Overall, the results for the six-month pilot were striking. A total of 33,782 invitations was sent to farmers. The 2,862 farmers who accessed the Arifu learning platform saved at rates more than five times those of farmers who did not access the learning platform.

Graph showing savings for M-PAWA customers in Tanzania

Similarly, farmers who accessed the Arifu learning content – regardless of the specific delivery method – took out larger loan amounts and repaid at higher rates than those who did not access the learning content.

Graph showing loan repayment statistics based on education received

On average, the more screens a farmer viewed, the more financial activity they had on M-Pawa. Even more interesting still, the treatment that introduced Arifu as their learning guide from the beginning had the highest impact on financial behavior – as shown in the graph on loan amounts. This shows how personification can help amplify digital learning. During pilot testing of SMS content with farmers, several farmers inquired about Arifu, asking questions such as “Arifu nii nani?” (Who is Arifu?) and frequently assuming it was another person communicating with them.

Besides the evidence this research provides on the power of timely, user-guided digital learning content, the research also highlights two key principles for consumer protection and responsible delivery of digital finance.

First, the use of well-built, interactive content about product features – including costs – helped increase use of the product and positive financial outcomes. While we have noted in the past how many digital lenders offer poor or insufficient disclosure of terms, these findings show how robust disclosure can be a good thing for providers as well as consumers (something also documented in CGAP’s digital credit experiment with Jumo in Kenya.)

Second, the interaction between the savings and borrowing aspects of M-Pawa in this experiment show how important it is to offer not only credit, but a place to store value. By driving up savings on M-Pawa, farmers were able to more accurately share their cash flow, and receive larger, more accurate loan amounts, leading to larger amounts borrowed and higher repayment rates for those who accessed the Arifu learning content.

We hope these results demonstrate the utility of enhanced consumer engagement, and that we see more approaches like this by digital financial service providers in the future, leading to increased consumer understanding, product use and benefit for consumer and provider alike.