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Three Areas DFS Providers Prioritise to Enhance Agent Networks

Posted on by MSC

Since its launch in 2013, The Helix Institute has offered evidence-based insights, practical training and technical assistance on agent networks to DFS providers across Africa and Asia. We asked providers to tell us what steps they have taken to improve their agent networks after engaging with The Helix. This blog sums up their actions, classified into three broad areas: 1) enhancing network size, distribution and make-up; 2) boosting service reliability, and 3) ensuring network sustainability.

1. Network size, distribution and make-up

To deliver on DFS deployment objectives, providers must get the right agents in the right places to serve their customers. The Helix trainings facilitate learning that helps providers review and refine their approaches to achieving the scale, reach and characteristics of the network best suited to support their deployment objectives.

  • Overhaul Agent Network Strategy: Eleven providers have overhauled their agent network strategies following The Helix training and exposure to peers from other markets. To respond to growing demands of managing and scaling agent networks, some opted to outsource the agent network management, while others adopted master agent models. Providers were inspired to let go of exclusivity clauses and guidelines on mandatory agent spacing (within a specific radius of each other), which has improved agent accessibility and increased transaction volume.
  • Strategic Agent Selection: Many DFS providers struggle with agent dormancy, which often stems from poorly targeted agent recruitment. Consultations with The Helix have driven 14 providers to refine their agent selection criteria to ensure their agents will actively transact. For example, providers have enhanced their geographic targeting to ensure agents are recruited from strategic locations and/or raised minimum start-up capital requirements to boost agent liquidity. Others through evaluating agent performance determined which agents do not add value to the network. In some countries, providers are now deactivating non-performing agents in an effort to streamline their networks. This is remarkable since in the past providers were reluctant to reduce agent numbers, viewed as a manifestation of scale.
  • Mandatory Agent Training: Research by The Helix has shown that trained agents perform better than their untrained counterparts. Our courses further emphasise topics essential for agent training. At least 14 providers reviewed their agent on-boarding approach: those who previously had no training structures have set up training departments to handle agent training needs; others have formalised agent training curricula and created training of trainer manuals. Providers came up with creative ways to identify gaps (e.g. by tallying agent call centre issues) and ensured curriculum included those issues. For example, several providers launched modules on fraud to increase agent awareness of the potential sources, prevention and mitigation measures. Some providers have encouraged agents to use social media as a platform for sharing their experiences and tips.

2. Service reliability

The success of digital financial services relies on how available, accessible and reliable they are to their customers. Without a reliable distribution channel, providers are unlikely to see high take-up and usage, regardless of the merits of their product design. A reliable service ensures customers are able to access DFS wherever and whenever they need it.

  • Enhance Liquidity Management: On the whole, float management is among the biggest hurdles in agent operations. Most financial providers in Africa have delegated the responsibility of managing float to agents. However, agent illiquidity undermines customer trust in the service and poses a threat to provider reputation. The Helix training and experience sharing between African and South Asian providers has led 23 providers to step up efforts to assist their agents with liquidity management. Some have engaged liquidity runners to deliver e-float or cash to agents, while others started facilitating access to lines of credit to boost agent’s working capital. In addition, some providers have opted to situate rebalancing points closer to the agent outlets and streamline rebalancing processes to enable real time float deposits at partner bank.
  • Minimise Network Downtime: Connectivity in most developing countries has proved a major hurdle in the deployment of agent networks. Some providers have therefore chosen to locate their agents only in areas where there are masts to ensure that agents can carry out transactions and reduce inactivity. Other providers have initiated system upgrades based on recommendations from peers.
  • Regular Agent Monitoring: Agent monitoring boosts agent loyalty. It builds a relationship between the agent and the provider, enhancing the business partnership. Exposure to best practices during The Helix training sessions have encouraged providers to introduce defined agent monitoring structures. These include outsourcing to 3rd parties and automation of agent monitoring processes to enhance effectiveness and cut cost. Additionally, some providers have formalised fraud and risk mitigation measures in regions with high incidence of fraud.

3. Network sustainability

Agent networks represent a large proportion of providers’ investment in digital finance deployments. Both agent commissions and management costs add up to significant sums expected to be covered by transaction fees. Balancing service affordability and agent remuneration is an art, elusive to many. The Helix-facilitated training and networking has inspired providers to target sustainability from three key directions:

  • Attractive Agent Business Terms: Whether agents see their business as lucrative affects how much effort they invest in growing the business. As such, agents condition providers’ DFS business growth. Following The Helix training, 13 providers were compelled to review the value proposition for agents to make it more attractive. Some have boosted commissions; others introduced performance bonuses or commissions for customer education and registration. Providers also sought to entice agents with non-monetary benefits like agent portals that facilitate business management or opportunities to address their fellow agents and share best practices for high performing agents. In over-the-counter (OTC) markets, where competitive commissions decide which service is accessed, The Helix spurred provider collaboration to standardise commissions and halt commission wars.
  • Revamp Customer Value Proposition: The utility of DFS to customers determines whether they use the service and the corresponding agent network. This is a function of the range of products provider offers and how well they meet customer needs. Twenty providers have gone back to revamp their customer value proposition as a result of The Helix trainings. Some redefined the product portfolio by introducing new use cases, multi-language functionality and repositioning the whole digital offering (e.g. shifting focus to merchant payments). Others dropped transaction fees and introduced airtime bonuses to reward usage. Another group returned to square one, undertaking market research and extensive customer consultations to tailor products to client needs.
  • Diversify Marketing and Communication Activities: The Helix curriculum sensitises providers to the importance of thoughtful and deliberate marketing activities. Thirteen providers have shifted emphasis from impersonal above-the-line campaigns to targeted below-the-line activities. Some providers in African markets have replicated creative approaches by peers, taking advantage of existing channels such as ‘town criers’ and market days to implement their marketing and communication activities. Many are now using community activation days to encourage customers to visit an agent. Providers are increasingly recognising the agent’s potential and role in communicating and educating the customer. This is being done in conjunction with activities that build trust in the agent network such as locating agents within the banking halls.

Recently, The Helix Institute convened DFS industry experts on regulatory, strategic and operational issues to reimagine agent network management for the future. This blog along with this summary of our learnings from years of interactions with agents and providers set the stage for their exchanges. Subsequent blogs present ideas on how to reinvent liquidity management, Interoperability – A Regulatory Perspective, Progress and Challenges with KYC and Digital ID emerging from the workshop.

Data in this blog is based on survey responses from about half of the MNO (36), Bank (46), 3rd party (13), Microfinance institution (8) senior managers from 33 countries who attended our training courses to share experiences, exchange ideas and draw inspiration from guest speakers and site visits with generous support from Bill & Melinda Gates Foundation.

 

Why is Digital Credit such a Huge Opportunity and Challenge

Posted on by MSC

Digital Credit is one of the fastest moving segments in financial product innovation. Today, digital credit provides quick funding for businesses and is capable of being an important source of revenue for DFS providers.

Setting Digital Credit Right – Is it Time For a Major Re-think?

Posted on by MSC

 

MicroSave’s Graham Wright expertly highlighted a worrying trend in an article on digital credit published in January 2017. His article highlights the fact that negative listing is shutting out millions of users from accessing microlending services. This, in turn, has affected financial inclusion. Mr Wright estimates that around 2.7 million people in Kenya – around 10% of the entire adult population – have been unduly denied service. Often, as in the case of 400,000 of those folks, financial exclusion is the penalty for defaulting on loans of less than $2!

More worrisome is the fact that these consumers, who may not have fully understood the terms and conditions, then frequently return to the grey and black markets. While costs and risks abound in these markets, positive borrowing behaviour fails to be digitally captured. Such shadowy and usurious forms of exclusion are precisely what microfinance was invented to combat in the first place. This is a major regression for inclusion, as digital disruption should be all about education and democratisation.

Rethinking Financial Services

Smartphone adoption is dramatically changing this landscape. While feature phones utilising USSD undoubtedly remain a core channel for reaching the base of the pyramid, that dynamic is shifting rapidly. In its place are emerging entirely new modes of communication, consumption and connectivity, enabled through newer and smarter devices.

These Internet-enabled, data-creating devices now connect 3.2 billion unique worldwide users. These users include not only loved ones around the corner or across a border but also an array of providers, old and new, who are ready to deliver the next wave of financial services. Among these providers are banks, microfinance institutions, microinsurance providers, e-commerce enablers, you name it. By 2020, the GSMA predicts smartphone penetration to surpass 5.7 billion subscribers.

 

Worldwide, mobile operators are assessing paths and gearing up to realise their potential as major enablers of the next generation of financial services. Some operators will embrace their evolution and innovate into major players in digital financial services, others will remain marred in the status quo of declining ARPU and loyalty. The providers who fail to adapt adequately would end up losing out to more innovative competitors and OTT challengers.

The biggest victors, however, will undoubtedly be the 2.5 billion unbanked folks, cheering on as device prices plummet and 3G/4G networks multiply.

Smartphone-driven Financial Services – a Marathon, Not a Sprint

In 2016, Mozilla released a fascinating, highly-recommended report called Stepping Into Digital Life. The in-depth research project spanned 12 months and tracked first-time smartphone users in Kenya. “Adoption is socially motivated,” the report concludes. “Owning and operating a smartphone can thereby elevate their status in society, and the resulting sense of pride plays an important role in adoption and learning.”

However, the report cited a couple of critical insights:

  1. Without the right skills, smartphones can exacerbate adoption challenges, instead of alleviating them.
  2. First-time smartphone users have little understanding of their role as consumers.

On its own, therefore, technology is insufficient to improve financial inclusion without concurrently solving for its entrenched legacy impediments: access, ability, and need. Financial technical literacy is a process enabled, but not resolved, by smartphones. Without engaged and educated consumers, the offerings of service providers as well as the lives of consumers will continue to be marred by risk.

Identity is the Core of Inclusion

The core of inclusion – and the inverse of risk – is identity. Who is the applicant and what is their financial history? How can they improve their standing and gain increased access? Well, problematically, 3/5th of the world’s unbanked people lack a legally-recognised form of identification. Moreover, in most countries, credit bureaus cover less than a quarter of the local population.

I recently joined a Women’s World Banking roundtable in New York City, where I came to know some startling facts. Women make up a disproportionately large share of the unbanked. For example, while 46% of men in developing countries have a bank account, only 37% of women in these countries have access to banking. The gap is even larger among those in poverty. Women who live below $2 a day are 28% less likely than men to have a bank account.

So, how do you assess an applicant without a footprint? And how do you level that playing field across gender, geography, class and access?

Meeting Users Where They Are Today

Currently, 77% of the world consumes mobile services on a prepaid basis. This implies that every year, mobile network operators process a trillion dollars’ worth prepaid top-ups – $.30 or $.40 here, $1.50 there. These top-ups are of low value, used in huge volumes, and are exceptionally inconvenient for everyone.

Because these customers predominantly transact in cash, lacking access to credit, they must travel in-person to a top-up shop to add balance to their phones. The distance is sometimes a few blocks, and often a bus ride away. Imagine doing this every week or every day, regardless of how otherwise busy or cash-strapped you may be. Imagine having to decide between topping-up your phone and buying diapers.

Prepaid Top-ups are the Formal Financial Transactions of Unbanked Consumers 

At Juvo, we are rethinking digital financial services. We partner with mobile operators to provide intuitive tools to consumers in emerging markets, enabling them to create, capture and benefit from their own positive financial behaviour. Rather than focus on extending as many loans as possible, we are taking a responsible, deliberate and personalised approach to building up users’ identities to mitigate risk for all parties involved.

We believe in rewarding users for these micro-transactions, generating the building blocks for upstream financial service access. Our products offer prepaid users an engaging and frictionless journey, from anonymous SIM card to robust financial identity. The products are based on the simple, frequent and standard behaviour of borrowing and paying back digital airtime credit extensions. We leverage prepaid subscriber data to allow anyone, regardless of the quality or fidelity of their financial identity, to obtain for a digital no-fee, interest-free product loan, delivered straight to their phone within seconds. As users borrow and pay back these loans, they advance from beginner to bronze and up to diamond status. This paves the path to build up an identity and unlock innovative digital financial products.

For carriers, our solution has proven to lift Average Revenue Per User (ARPU) by 10-15%, drive loyalty, and reduce churn by 50-90%. It simultaneously adds convenience, access and identity to the financial lives of our users. All this adoption and engagement, filtered by our data scientists, results in a greenfield database of millions of well-defined and segmented prepaid mobile consumers.

The Path to Upstream Services

Every mobile operator has been repeatedly told that they are optimally situated to deliver the next wave of financial services, given their distribution networks, communication channels, massive user bases and strong brands, However – and operators are acutely aware of this – the path to mobile money success is riddled numerous obstacles. They include high cost, severe risk, burdensome or ambiguous regulations, massive account dormancy rates and, ultimately, failure.

Juvo mitigates these challenges by offering an alternate path. We reduce the risk and cost of providing financial services by generating engaged users and rewarding that engagement. We then segment our users with game mechanics and data science. This deliberate and sequential approach enables us to offer personalised, timely and targeted financial services from only the most innovative FSPs to the right users.

We partner with international FSPs, such as Mastercard and MoneyGram, alongside local providers of microinsurance, microfinance, digital credit, savings products, energy solutions, handset financing, among others, to create channel-specific products hand-tailored to our users. We emphasise on personalised offers, consumer education, and sustainable financial habits. We understand that forcing the adoption of a feature-bloated, one-size-fits-all app at an entire population demands massive behavioural change and education, as is the case with most Mobile Money Operators. Most often, this approach simply does not work. Here at Juvo, we, therefore, are banking on a personalised future of finance.

Juvo is a San Francisco-based fintech company that was founded with an overarching mission: to walk billions of people worldwide who are creditworthy, yet financially excluded, up a pathway to financial inclusion, starting with their mobile phone. Juvo’s proprietary Identity Scoring technology uses data science, machine learning, and game mechanics to create financial identities for anonymous prepaid mobile subscribers across the globe, providing ongoing access to otherwise unattainable financial services. The company has recently closed its Series B round, raising $40 million in funding led by NEA and Wing Venture Capital.

How Can Providers Make Digital Credit More Profitable?

Posted on by MSC

Many commentators have raised concerns about the interest rates charged for digital credit. And, given that the entire process is automated and dependent on computer algorithms rather than expensive human intervention and analysis, this seems reasonable. On the face of it, it is strange that the interest rates charged for digital credit should be closer to those common in the informal sector than those charged for other formal sector loans. So what is going on?

There are three key drivers of the high interest rates: 1. The small size of loans; 2. The cost of data analytics; and 3. The risk premium priced in.

  1. Small Loans: We all know that, broadly-speaking, it costs the same amount of money to make a $10 or a $10,000 loan. Digital credit loans, absent the personal relationship, start by lending small amounts (typically $10-20) to gauge repayment behaviours and base future lending decisions (largely) on the basis of these. The interest on these minimal amounts is often inadequate to cover even the variable costs associated with making a digital loan (SMSs or data charges etc.).
  2. Data Analytics: Digital credit providers not only need to invest significant amounts upfront to build their platforms and algorithms, but also on an on-going basis to keep refining them as they learn through the behaviour of their customers. One large provider tells us that they are spending $200-300,000 per month on analysts to maintain and develop their system.
  3. Risk Premium: MicroSave’s recent analysis of a credit reference bureau’s data has highlighted the extraordinarily high default rates amongst digital credit borrowers in Kenya, where the best data is available. We can safely assume that this is a pervasive problem. Inevitably, providers of digital credit have to price these losses into the interest rates charged for loans. This means that all borrowers (whether they repay on a timely basis or not) have to pay the risk premium for those that default.

While providers of digital credit will always struggle with the mathematics and economics of small loans and the cost of data analytics, there is clear opportunity to reduce the level of defaults and thus the risk premium that has to be charged … and perhaps that smart algorithms alone will not be enough to do so.

CGAP’s Greg Chen highlights six early errors made by digital credit pilots and deployments. Several of these contribute to the high levels of default.

  1. Offering credit without a strong remote identification system. When you can’t verify customer identity, offering remote services is difficult, especially at scale.
  2. Poor targeting, where credit offerings attract a high-risk applicant pool.
  3. Cumbersome loan application processes so that only those higher risk borrowers, who are unable to secure credit from other sources, apply.
  4. Poor product design, which does not adequate recognise and reward those that do repay regularly and on time.
  5. An excessive focus on credit scoring but the absence of a sound collections strategy.
  6. Credit scoring models that were too conservative and did not allow credit to be extended to more than a small fraction of applicants.

Addressing 1. – 5. could allow providers of digital credit to improve targeting, increase loyalty and reduce both risk and default … thus increasing the profitability of providers of digital credit.

1. Identification systems: A growing number of countries are introducing formal identification systems – many of which are bio-metrically enabled. And, even where no such systems are available, app-based ID systems (including for example Yoti, Taqanu, Trulio) are increasingly common. These, of course, require smart phones, but the growing penetration of smartphones continues despite some set-backs with low cost smart phones. Digital credit providers will need to leverage these ID systems to have a firm fix on their customers – this will be key to identification, credit assessment, collection and delinquency management. ID will also be key to running effective credit bureaus. Unfortunately, few countries outside Kenya have credit reference bureaus designed to help with the management of the small loans offered by digital credit … and thus to allow people to develop a credit history. While Kenya’s credit reference bureau is still finding its feet, it is playing an immensely important role in creating transparency and allowing those who do repay on time to create a positive records.

2. Poor targeting: Getting the right balance between credit scoring models that are too conservative and those that are too liberal is key to building an effective system. But there are other drivers of poor targeting. Digital credit lenders will also need to achieve the right balance between aggressive “push” marketing and ensuring that their product is properly understood in the market. As we have seen, too many people respond to push marketing by borrowing out of curiosity, and without any real need or purpose in mind. Providers can clarify their marketing to give customers a better understanding of their terms and conditions, as well as the penalties for non-repayment. This approach would also allow them to address challenges with consumer protection. They can also use behavioural nudges to facilitate appropriate behaviour.

Mobile network operators (MNOs) can also reduce targeting risk by completing initial credit screening through lending airtime credit. Airtime has marginal costs for an MNO, and thus represents a much lower risk than e-value credit. Thus this approach could allow MNOs to test borrower’s credit behaviour at much lower cost before opening a window to borrowing e-value.

 

3. Loan application processes: Many SMS-based and USSD-based digital credit systems make it almost too easy to access credit, thus potentially encouraging frivolous applications for credit. This, may need management through behavioural nudges – for example to encourage the potential borrower to view the terms and conditions, or to reaffirm the need for the loan after a nominal “cooling off” period. In contrast most app-based systems require the user to go through many screens (and, in some cases, what are seen as invasive requests for data and photographs) before they are given their loan. These systems need a thorough review to ensure that each step in the process is optimised, really adds value and does not put off high potential borrowers.

4. Poor product design: Currently, few of the digital credit products available reward those that consistently repay on a timely basis – except by offering larger loans. As borrowers demonstrate their credit-worthiness it would make sense to reduce the risk premium (and thus the interest rate) that they have to pay for each successive loan. This approach might be further reinforced and optimally communicated by creating a tiered status system (similar to those for airline miles) so that borrowers can aspire to move up the tiers and thus qualify for lower interest rates, larger loans, variable repayment periods and other benefits. Additional product innovation might include: 1. Loans with a tenure of a day for market traders who are currently having to use loans repayable over weeks or a month to finance their business cycles, which run from early morning to afternoon; 2. Goal-based savings/loan products with an appropriate financial planning tool embedded in the app or USSD interface; 3. Longer-term loans for those with an excellent credit record who want to borrow for their business – once again these need to reflect their business cycles.

5. Absence of a sound collections strategy: At present most digital credit providers use SMS to encourage repayment, but otherwise have little interaction with their borrowers. Only a few are using call centres to talk to borrowers struggling to repay. The important human touch is missing, and thus digital credit loans are last on the list to repay amongst households with multiple loans outstanding. For larger loans it may also be valuable to involve agents in both loan origination and repayment/delinquency management.

Readers will note that none of the above refers to using “big data” – in a way that has been so successfully done in the developed world (for example by Lending Club in the US). This is because the vast majority of low income people in the developing world do not leave adequately deep “digital footprints” to reliably inform credit decisions. This will change over time, but for now the most effective (and commonly used) indicators of credit worthiness lie in credit history and behaviour, and (to a lesser extent) top-up and call/SMS behaviour. It maybe that for larger loans app-based providers of digital credit may also want to use psychometric indictors to assess willingness to pay. However, this would be dependent on reducing the typical screening questionnaire from 200-300 down to 40-50 questions without losing predictive capability – quite a challenge.

There is a clear need to reduce the risk premium for borrowers of digital credit. While this may be difficult (but by no means impossible) to do for the first couple of loan cycles, it should be eminently feasible for later loans cycles once the borrower has established credit history and wants to borrow larger amounts. Doing so should incentivise timely repayment and increase borrower loyalty … and thus profitability of the providers of digital credit

Give us Some Credit! Meet the Digital Borrowers in Kenya

Posted on by MSC

It is 3 am in Nairobi. The city, known for its vibrant nightlife, is wide awake. Entertainment spots in the bustling capital of Kenya overflow as the night goes on. The streets are a sea of activity, filled with pleasure-seekers. Interestingly, it is between 3 am and 5 am that a third of all the digital loans from providers are taken. Could this be just a coincidence?

MicroSave conducted a qualitative study in Nairobi and Meru to understand the perceptions and motivations of low-income Kenyans to use digital credit. Using our customer-centric Market Insights for Innovation and Design (MI4ID) approach, we identified three profiles of digital borrowers: Repayer (Muthoni), Juggler (Makena) and Defaulter (Nyachae). In this blog, we analyse their use of digital credit and suggest ways to adapt existing products to better serve these customers.

Muthoni, 35, is a trader at Gikomba market. She buys fresh vegetables from Wakulima market at 3 am. She finances this purchase through digital credit. She has a choice pool of seventeen providers, and no longer uses informal lenders, who would charge her 10% interest per day. Her credit limit is $200, achieved through on-time repayment and disciplined saving.

Muthoni is a “Repayer”. Repayers are the premium customers for digital credit providers. They rarely default and can take multiple loans in a month. To reward this customer segment, providers could enable access to shorter or longer term loans, multiple/concurrent loans and increase credit limits. As is the case with Branch, other providers could also implement loyalty programmes that reduce interest rates and facilitation fees based on the size of loans taken, as well as on-time repayment. This would create ‘stickiness’ and reduce customer churn, as they tend to graduate to products with cheaper variable costs.

Makena, 37, runs a grocery shop in Igoji town. She is married and has four children. At any given month, she services over three digital loans in addition to traditional loans. Currently, she uses M-Shwari for emergencies and to boost her business, KCB M-Pesa for ease of consumption and Equitel to pay school fees. Occasionally, she uses Airtel Kopa Cash for sports-betting. She also has an $8,000 land loan from Cicido SACCO and another one from Equity Bank, which was used to restock her shop after it was looted. She usually repays late, but right before being negatively listed to ensure she can borrow again. ‘I prioritise [repaying] the Sacco loan as opposed to digital loans due to the huge penalties on default imposed by the Sacco. In some cases, they take your personal assets.’

Makena belongs to the “Juggler” segment of digital borrowers, who face capital scarcity leading to the use and “animation” of different credit instruments to meet various financial needs. In this context, we recommend that providers cater to such customers by offering more flexible repayment periods and options to borrow in tandem and pay in instalments. The providers should also have clear incentives for on-time repayment (such as simultaneous access to multiple loans).

Nyachae, 26, is a savvy entrepreneur who runs an African fashion attire business in Nairobi. In 2015 he took a $5 M-Shwari loan to test the product but has not repaid it. He claims that reminder SMSs from the provider cannot scare him: “I delete the reminder messages. They don’t know me so they can’t find me”. Something about borrowing digitally feels less serious to the defaulter. Nyachae postponed the repayment until he was negatively listed with the Credit Reference Bureau (CRB). He would now have to pay $22 as clearance fees on top of his outstanding $5 loan to pass a credit check. Nyachae is currently servicing a $700 loan from the church SACCO. Recently, he managed to get a $20 loan from a provider despite being negatively listed.

Nyachae is a “Defaulter”. To better serve this segment, providers could include a personal dimension in the digital collection process. This could be done, for instance, through follow-up calls or through agent engagement in the case of larger loans. In addition, providers should better understand the defaulters’ intentions for borrowing as well as motivations and abilities for loan repayment. CRB regulations should accommodate the realities of digital credit, for example, by having different tiers of clearance fees that are commensurate with the loan amounts.

While interacting with Muthoni, Makena and Nyachae, we came upon a number of insights. These are enumerated below:

  • Borrowers only had a limited understanding of the terms and conditions. This was because these were presented in legal jargon and accessing them through a weblink created real technological, cost-related, and psychological barriers. There is, therefore, a need for salient and simple terms and conditions presented before a customer accepts the loan (that is, through pop-up messages for STK, or inbuilt messages for mobile apps). It would also be ideal to separate the interest rate from the principal. This will limit confusion and enhance understanding of the repayment amount.
  • There is clearly an element of gaming the system to influence loan limits. In this regard, providers can use interactive SMS to understand the context of customers’ loan uptake. Is it taken in an emergency? Is the loan availed as a trial without consideration for long-term implications? Or is the borrower trying to “game the system”? Engaging customers before they take the loan can reduce uninformed borrowing and delinquency. It can help avoid a situation like Nyachae’s unrepaid $5 loan.
  • Reminders messages sent at different times of the day do not elicit repayment behaviour (Nyachae and Makena ignore messages sent in the morning). Providers should instead customise repayment reminder messages and incentives in terms of the customer segment and ensure that the reminders are goal based, so customers may see the value of timely repayment. Including a personalised touch, such as follow-up calls, can also drive repayment. This has been seen in the case of providers like KCB M-Pesa that uses a dedicated call centre to follow up with loan defaulters, Tala, which uses a collection agent, and Nimble Kenya, which also call to follow up with defaulters.
  • Ultimately, to encourage timely repayment from customers like Makena and Nyachae, there is need to use behavioural levers to drive repayment. These could include the following:
    • Priming ‘good borrower’ identities during the loan application stage. (‘Only prompt repayers take this loan, do you wish to proceed?’);
    • Framing loan default as having serious consequences (‘You will not be able to borrow in future if negatively listed on the CRB’);
    • Using social proof to elicit on-time repayment (see the adjoining Tala chat screen).

There is a clear demand for digital credit. A growing range of providers experimenting with approaches to respond to this demand bodes well for the future. However, to serve the wide range of borrowers better, providers should design products that leverage both rigorous data analyses as well as demand-side customer-centric research to understand the wide range of behaviours, contextual challenges and client experiences. Over time, they should incorporate learning within the product to educate customers on personal savings goals, and make these accessible to customers before and/or after disbursement.

Regulators also have an important role to play and should make it mandatory for all providers (including app-based lenders located outside the country) to use Credit Reference Bureaus to share data on digital borrowers. Regulators should set minimum standards for customer recourse channels and coordination by partners to address issues/complaints raised by customers and drive long-term usage and customer loyalty.

Digital credit is still a nascent industry with much scope for learning. Understanding the needs, aspirations, perceptions, and behaviour of customers should allow providers to design products and ‘lend smarter’, rather than depend on the risk premium-inflated interest rates to secure their business case.

Digital Credit – Have We Not Been Here Before With Microfinance?

Posted on by MSC

I worry that I may be getting old and cynical; but I am quite sure I’m suffering déjà vu.
As we continue to celebrate the important breakthrough that digital credit provides in efforts to lend to the poor, I cannot help myself comparing it with microfinance. The parallels are clear to see:

  1. Insufficient emphasis on savings,
  2. Loan amounts too small to make a real difference,
  3. Reliance on repayment behaviour,
  4. Drop out patterns,
  5. Multiple borrowing to get a useful sum,
  6. One loan used to settle another and
  7. Rising delinquency.

We seem set to have to learn the same old lessons all over again, the hard way.

I got into microcredit (for that is really what “microfinance” usually is) I could not believe that the industry could place such little emphasis on the importance of savings. Savings that were mobilised by microcredit institutions were typically compulsory, and used as a basis to determine loan size and to act as collateral. Digital credit offerings, when backed by a bank (for example M-Shwari, EazzyLoan or M-Pawa), mercifully do not make savings compulsory and inaccessible to the client, but (when you examine the literature or press coverage) they are still the secondary service that feeds the algorithms that dictate loan amounts. While many customers do indeed use the savings services (and some are thoughtfully designed and structured), many potential borrowers deposit and withdraw in an attempt to game the system to raise the loan amount for which they are eligible.

In common with microfinance, the initial digital credit loans are typically too small to be of any real value – except for consumption smoothing, very short-term trading or responding to emergencies. These are all very valid and important reasons to use the service; but the rhetoric and hype around financing enterprises and lifting borrowers out of poverty seems optimistic. This is confirmed by Julie Zollman’s analysis of the FinAccess 2016 data (see graph below) which shows that less than 16% of these loans are used for enterprise.

This leads us to another similarity. For too long microcredit had lived the lie that loans were used for enterprise and simply assumed that microcredit’s impact was beneficial: borrowers were repaying and taking more loans, so there must be positive impact. We seem to be falling into the same trap in our romance with digital credit – perhaps it is time for some rigorous evaluation?

Microcredit institutions have had to simplify and shorten their on-boarding (“training”) processes as competition grew. We can safely expect a similar trend in digital credit. In contrast to the relative simplicity of applying for a loan from M-Shwari, EazzyLoan and other SMS-based systems, app-based lenders’ lengthy and complex application steps discourage many from taking up the product.

The rhetoric around using big data to make loan assessments also seems misleading. Our recent experiment involved working with low income people to apply for loans from all the major providers in Kenya. This allowed us to assess the customer journey, the levels of disclosure of terms and conditions and the resultant loan amounts offered. This exercise demonstrated that (perhaps because of their very limited digital footprints) a poor borrower can put almost any numbers they want into the app-based lending sign-up screens in Kenya and they will receive a standard loan of Ksh.2,000 (US$20) or Ksh.1,000 (US$10) depending on the provider. Thereafter, in common with M-Shwari, EazzyLoan and other SMS-based systems (and indeed microcredit institutions), it is probably your repayment history that will, above all else, determine the size of your next loan.

I strongly suspect that the analysis of “1,000 data points”, social networks and behaviour will be largely incidental to this key indicator of credit worthiness. This may, of course, be different for micro and small business owners using social media to market and digital channels to effect transactions. But for a typical low income consumer who only tops his/her mobile up with a small amount twice a month, and uses a limited number of apps, their digital footprints are probably too light to add much value over and above repayment history. This may evolve with time, as it has done in the United States, where the correlation between Lending Club’s credit rating grade and the borrower’s traditional FICO credit rating score has dropped from 80% in 2007 to 37% in 2015. But this will be highly dependent on low income people beginning to participate more in the digital economy.

When I first arrived in Uganda, all MFIs were offering loans repayable over four months and living with drop-out rates of 30-60% per annum – hardly the basis for a sustainable business! Analysis showed that, many of the drop-outs occurred after the first loan cycle. These were people who had tried a microfinance loan – often out of curiosity or under peer-pressure – and decided that it was not for them. We see exactly the same challenge with digital credit. Our initial analysis of TransUnion credit reference bureau data showed that over half (57% or 1.4 million) of negatively listed digital borrowers had taken digital loans for the first (and only!) time. And over 30% of the first-time borrowers between July 2016 and March 2017 were negatively listed by the end of March 2017.

However, there is one important difference. With digital credit the lack of personal touch, group guarantee and peer pressure means that the loan losses in this first cycle are extraordinarily high, and is driving many digital lenders to set their interest rates at rates that rival those of informal sector moneylenders. Worse, these interest rates typically do not drop as the borrower builds his/her credit history.

In addition to the drop-outs and default after the first loan cycle, Ugandan MFIs saw a rapid growth in drop-outs in the 5-7th loan cycles. The explanation was simple – many borrowers taking larger fourth, fifth and sixth cycle loans are unable to come up with the larger amounts they needed to meet their weekly repayments. While our analysis of the credit reference bureau data does not show this trend, our recent research in Kenya did highlight some instances of similar issues for regular borrowers eligible for larger loan sizes from digital lenders. The requirement to repay the large lump sum within one month is likely to become increasingly difficult as loan sizes increase. In Uganda, MFIs quickly learnt to extend the loan repayment term to 6 and then 12 months – will digital credit providers follow?

One of the major drivers of repayment crises (for example in Bolivia, India and Morocco) has been MFI staff aggressively pushing loans onto customers. This means that customers who do not want, (or need) to borrow take credit for less important uses, or hand it to their friends and family to use. We see similar trends amongst providers of digital credit who aggressively market their loans (particularly through SMS). As a result, our research showed, some borrowers take digital credit out of curiosity or for frivolous uses such as Friday/Saturday night entertainment or sports betting.

In common with microcredit we are also seeing the rise of two dangerous phenomena in digital credit: 1. Borrowers taking multiple loans to cobble together the lump sum they feel that they need; and 2. Borrowing from one lender to pay off the loans of another. Both, of course, increase credit risk. The better MFIs have tried to deal with these issues by better understanding and segmenting their clients. This allows them to make micro-small-medium enterprise (MSME) loans to those who need, and can repay, larger loans; and to support and manage those who are stressed and borrowing from others to repay. This requires personal interaction, and (for larger loans) a revised approach that involves visiting and assessing the borrower’s business.

It may be that digital credit providers will need to start to learn from the lessons of microcredit organisations and introduce a personal touch into the process, at least for the larger loans. This could be done through involving agents (thus providing them with valuable additional commissions for loan initiation). MicroSave’s work in India has shown that agents are willing to take responsibility for, and get involved in the collection of, loans that they have referred. But they are unwilling to burn social capital by chasing loanees for whom they have not vouched. Without this personal touch digital credit loans will remain last on the list to repay.

Let’s be clear, digital credit is an important, high potential and often valuable financial service for the mass market. We need to work to optimise the delivery and the recovery of these loans, for both consumers and providers. There are plenty of opportunities to tweak and significantly improve the current digital credit offerings. It is clearly time for digital lenders to review the hard lessons learned by microcredit institutions over the past 30 years – if they don’t, we’ll continue to see the alarming numbers of “digital delinquents” and people blacklisted on the credit bureaus.