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Transformational Upskilling: How teaching skills to digital platform users can increase value for all

An opportunity

Platforms—a dozen massive ones, and perhaps 500 smaller ones—play an increasingly critical role in the livelihoods of hundreds of millions of people around the world. In almost every economic sector, platforms have introduced new “multi-sided” markets, matching buyers and sellers in attention, goods, services, and labor at massive scales. Websites and electronic supply chains are replacing bazaars and storefronts. Gig work is augmenting salaried work. Algorithms and finely-tuned digital user experiences are supercharging traditional buyer/seller relationships.

Platformization will transform markets and livelihoods everywhere, but perhaps nowhere more so than Africa, where there is an urgent need—and a unique opportunity—to provide new forms of livelihoods to a young, growing population eager for better access to dignified work.

In this project of the Mastercard Partnership for Finance in a Digital Africa (FiDA), we seek to improve the relationships between platforms and sellers—the small-scale producers, the self-employed, and the gig workers that rely on platforms to sell their goods, services, and time.

These platform-seller relationships can be either at arm’s-length or more mutually beneficial. Our work in 2019 and beyond is to engage directly with platforms to explore and promote the latter. The key is “transformational upskilling.”

Transformational Upskilling

Transformational upskilling allows platforms to prosper by facilitating learning, in win-win-win relationships with participants and labor markets. Platforms win by accelerating sales and increasing the quality of goods and services on offer. Producers win by learning new skills and improving their craft. Regions and countries win by increasing the human capital of their workforce.

There is a great deal of training happening on platforms around the world. In regions with high Internet use, Amazon, Facebook, eBay, and other platforms invest considerable resources in training their producers in order to improve the quality and volume of transactions on the platform.

In frontier markets, global platforms as well as dozens of regional ones are discovering the best ways to help the digitally nascent become more adept digital producers. These range from Africa’s e-commerce giant, Jumia, which offers financial literacy lessons to help entrepreneurs do their books, and Kenya’s Lynk, which provides training modules to gig-seekers, to the ridesharing platform Bolt (formerly Taxify) that texts the locations where users can maximize their earnings to drivers in 20 African cities.

Why is some training transformational? Because it creates value for several parties

Our core research question for 2019 revolves around what forms of training can be called transformational —whether because they give producers skills that they can bring to other jobs, and/or because the training increases the level of human capital in the workforce of a city, region, or country. Both are critical to providing more and better livelihoods for young people in Africa and beyond. Transformational upskilling is win-win-win:

Platforms win by accelerating sales and increasing the quality of goods and services on offer. Training can coach or nudge producers to be more effective, ultimately leading to more sales of higher quality goods and services on the platform.

Producers win by learning new skills and improving their craft. Producers can diversify their professional and business skills and thereby keep up with market demands.Cities, regions, and countries win by increasing the human capital of their workforce. Training modules have the potential to transform the workforce towards higher paying digital jobs across cities, regions, and countries.

Our process

Now. We’re talking to micro-entrepreneurs in Kenya about their platform practices and learning needs. We’re also doing a landscape of the best practices in transformational upskilling.

Soon. We’ll be working directly with platforms in Africa to (1) identify their producers’ key learning needs and (2) help delineate specific, cost-effective but high-impact improvements to training that platforms can use to support their users and the economies in which they work. And a bit later. We’ll be inviting more platforms around the world to get involved, sharing best practices about the potential of transformational upskilling, and offering specific toolkits and support to bring more transformational upskilling to more platforms, users, and regions.

What we’re learning about different kinds of Transformational Upskilling

We’ve observed several types of Transformational Upskilling: Direct Methods deliver training clearly labeled as such (e.g., training videos). Indirect Methods deliver cues through nudges and feedback (e.g., ratings systems). Third-Party Methods arrange or endorse off-platform training with support from the platform (e.g., in-person training with subject-matter experts).

We’ve also distinguished three primary content areas where platforms upskill producers: Domain-Specific Practices (i.e., how to be a better professional); Digital Literacies (e.g., what makes a successful e-commerce advertisement); and Financial Literacies (e.g., tips on how to manage finances as a freelancer).

Combined, these two classifications offer a “menu” of Transformational Upskilling opportunities.

How to get involved

We’re looking to spend the better part of this year using this chart, our research, and our interactions with platforms to better understand the strategy, approach, value proposition, and execution of transformational upskilling in Africa. We would value your support throughout the process. Here’s how to get involved:

Community of practice: We want to create a center of excellence on Transformational Upskilling, covering different focus areas (social media, e-commerce, online work) across the globe by finding the best examples of Transformational Upskilling and champions who can evangelize its value.

Consultative engagement: We’re also interested in identifying a couple platforms to work directly alongside us in research with small scale producers or self-employed workers, to expand learning opportunities within digitizing markets around the world.

Do you want to be a part of the community of practice, or the research engagements, or both? Do you have feedback, or examples of transformational upskilling you’d like to share? Please send an email to to let us know! And in the meantime, be sure to watch for further updates from the FiDA team as our research unfolds.

DFS use among digital Kenyans

The Mastercard Foundation Partnership for Finance in a Digital Africa is pleased to share the final report from its first exploration of “DFS Use by Digital Kenyans”.

The report is based on a representative sample of 1,000 Kenyans with data-enabled mobile phones—“Digital Kenyans”—gathered between September and November 2017 by Caribou Data.

Caribou Data transparently recruits and compensates individuals for participating in its panels, using cutting-edge techniques such as differential privacy and on-device anonymization to ensure that participants are effectively anonymous and cannot be re-identified. As a result, Caribou Data can offer data and insights previously unavailable to most of the DFS community.  

The research includes insights on DFS use in the context of other phone-based activities, explores DFS use by activity, and offers an illustrative segmentation focused on the distinctions between “basic” and advanced DFS use.

Our findings suggest that DFS use is common but not without challenges. Most of the smartphones in our sample are older/outdated, underpowered, or nearly full—all factors detrimental to the user experience. Urban users take advantage of 3G and Wi-Fi coverage, while rural users spend the majority of their time under slower 2G signals. A “metered mindset” alongside uncertain income streams lead many users to top up data in small amounts. And yet, people still spend more time in DFS sessions than WhatsApp or SMS, perhaps because SIM menus remain the most popular interface for payments.

Our data reaffirms and reflects existing observations: not everyone in Kenya is an active DFS user, and not everyone uses DFS in the same way. Indeed, only 13% of 90-day active users in our panel used DFS  frequently enough to be considered effectively “daily” users . Our “cash flow dashboard” illustrates how some users keep cash digital, while others may be quicker churners. And, even though we have found that top-ups, P2P, and cash-in/out are common among active users, savings and loans are much rarer.

To illustrate some of these differences in DFS use, we created a behavioral segmentation of our 90-day active user base, contrasting infrequent vs. frequent users, and  “savers” vs. “borrowers”.

Considerable differences emerge between these segments, including demographic (borrowers trend male; savers, female) and the degree to which users keep cash digital (infrequent users keep funds digital longer than daily users). Even among “advanced” users in the saving and borrowing segments, there are indications of challenges to effective use, notably evidence of overpaying for unused data, and gambling.

Products need to be designed for simplicity and resource-constraint. Short codes still outpace in-app behaviors. Feature phones are common, and many smartphones are underpowered, old, and “full”.

Nevertheless this analysis underscores that there there is plenty of room (and need) for digital Kenyans to move into more advanced and regular DFS use. Only about half of our sample were active DFS users, and of that, the majority only infrequent users of “basic”.

We offer this for product managers, fintech entrepreneurs, policymakers, and DFS practitioners alike. Please download and share, and let us know your thoughts.

What forward-looking considerations could impact conversational interfaces in the future?

In this post we highlight factors that might influence how conversational interfaces (CIs) are viewed, used, and interacted with across emerging markets in the future.

Risks in using messaging APIs:

Stemming from the recent GDPR legislation on data sovereignty, privacy, and security, companies like Facebook and Telegram quickly had to adjust their messaging applications to, ultimately, restrict the amount of information CIs can extract from users. Given these new rules, and potential threats to these companies’ core business, messaging application companies may choose to restrict and close access to their messaging API, making it considerably more difficult for CI providers to build on these platforms.

To hedge against this risk,  CI providers should plan to build across different channels (SMS, different messaging applications, web). They should also limit dependency on these applications for critical business functions and instead focus on using CIs as acquisition channels rather than for business functions. So, hypothetically, Messenger can be used to acquire customers through Facebook ads; once acquired and customers have bought into the tool, users could potentially be migrated from Messenger to an independent application.

Considerations around disclosure:

Throughout our research, we heard questions and concerns around disclosure : Is the conversational interface obligated to let the customer know they are talking with a bot instead of a human?

Whether or not CI’s are ethically obligated to do so is unclear, but currently, none of the conversational interfaces we’ve investigated volunteer that information. While some insinuated that the interaction is automated through button pushes and stock responses, many prefer to not explicitly disclose that a bot is in play.

Regardless, some users—particularly those in rural and peri-urban settings with limited digital experience—are willing to try the interface if it helps answer their questions. As they become more familiar and comfortable with the interface, it is likely that s/he will trust whatever information the interface provides. If the content is accurate and well sourced, the user will may well benefit; however, if the content is inaccurate or fake, the user may suffer negative consequences.

There is no immediate solution, but in time, new digital users will become savvy enough to understand that they are talking with a machine and, hopefully, savvy enough to discriminate between flagrantly false information and accurate information. Meanwhile, it is unclear how to address this cost-effectively. On one hand, if the conversational interface openly informs the user that they are talking with a machine versus a human, it could discourage use of the interface. On the other hand, by not letting the user know they are talking with a machine, it may be unethical to mislead the user into thinking they are talking to a person when they are in fact talking to a machine.

Emergence of voice as alternate interaction tool for conversational interfaces:

Text-based tools to interact with customers will continue to grow, evolve, and reach new populations globally, while the illiterate will continue to face barriers. This demographic, who likely cannot fully understand messages received and/or struggles to type out their own messages, will presumably be locked out of these new services and further marginalized.

However, some companies, such as Hishab and Maya Apa in Bangladesh,, are developing voice solutions to address literacy challenges. Unlike interactive voice response (IVR), these solutions interact with users through voice rather than number prompts, creating a more authentic and lifelike experience. As this technology matures, it should help address literacy considerations and make for a more meaningful user experience that can be easily accessed over airtime rather than through mobile data.

However, it is our understanding that building an emerging market language voice solution like the one Maya Apa built currently requires an extensive amount of time, technological capability, and data storage. According to these two providers, building the language database in Bangla took several years and required painstaking recordings of different pronunciations of words and scripts to produce a minimum viable language database capable of responding to basic conversation prompts.  Building a similar oral language database from scratch in sub-Saharan Africa—given the dearth of local language content —would probably take just as long .

Nevertheless, it is encouraging to see that companies are making the effort to solve for this often slighted demographic. Applied to financial services, this technology could make real headway in addressing issues relating to education and capacity building that would otherwise be ignored due to literacy challenges.

Path forward

Conversation interfaces are starting to emerge as a viable business resource for financial service providers across the emerging market. Their ability to cost- effectively interact with users and extend an array of different services, from the transactional to the educational, may help businesses connect with new customers while providing consistent and reliable support to existing ones. And, as the technology barriers to building a conversational interface continue to fall, we expect that more and more businesses will  look to these tools as critical revenue generating and cost reduction strategies.

Nevertheless, these tools will only succeed if they consistently solve for customer needs. To best calibrate expectations with what customers challenges they can address, businesses need to not only understand the technology that powers CIs but also users’ digital behavior . Accordingly, businesses need to source content that is accurate, present it in a way that is easily understood by the user, and construct the application so that it is intuitive and reliable. If they are able to accomplish this, the CI will likely earn the user’s trust, which would ensure the tools value. However, if the CI either begins to provide inaccurate information or operates inconsistently, the user is unlikely to trust these tools, potentially rendering them obsolete. It is our understanding that, because these tools are so new, the emerging market user is often unwilling to retry them after a failure  Thus, it is imperative that, once deployed, these CIs work and work effectively.

Conversational Interfaces: Revenue Models

It is often easier and less costly for CI providers to white label their conversational interface to a revenue partner rather than go direct to customer.

CIs can be broken into two business structures:

Business to customer [b2c]:

These CIs (i.e., PIA, extend access to basic financial services directly to customers without the support and backing of a more recognized brand. Because of that, CIs have to build their credibility directly with the customer through either word of mouth or through advertising ( radio, TV, or Facebook ads). Under this business model, the CI would generate revenue through either transaction or subscription fees: the customer would pay to send money or to access learning modules.

We suspect that operating a CI through this business structure makes it easier to get the conversational interface online and ready to use but considerably more difficult to gain traction among customers. Though it is relatively simple to construct a CI, it is quite to difficult to convince a user that the conversational interface can solve one of their pain points and is worth trying, let alone using routinely.

Business to business [b2b]:

Under this business structure, conversational interfaces (i.e., Juntos, Arifu) license their product directly to a partner, who then redesigns the interface under their brand and specifications. The partner markets the conversational interface under their brand to their customers through different analog [e.g., brick and mortar advertisements, billboards, radio, and television] and digital [e.g., Facebook page, company’s website] means. And as part of the licensing agreement, the CI provider will likely need to meet different key performance indicators—from usage to the number of new subscriptions purchased to capacity building—r to receive payment.

Under this business structure, the CI provider leverages the brand recognition of the third-party partner to reach customers. This is significantly easier and more efficient than trying to reach customers directly but requires not only educating a potential partner about conversational interfaces but convincing them of their value proposition and return on investment.

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MoMo: The Orange Money Digital Assistant

This is post number 4 of the Conversational Interfaces Blog Series and has been prepared by Teller Technologies, Inc., with whom some of our research was conducted, based on their pilot with Orange Money Madagascar.

Two of every three adults worldwide are financially illiterate, meaning they lack the knowledge and skills to use their financial resources effectively for lifetime financial security. This staggering share of people is disproportionately concentrated in developing countries, where formal financial services and financial assistance programs are often lacking. Among the financially illiterate, women, the poor, and lower educated respondents are more likely to suffer from gaps in financial knowledge [1].

In Africa, there are very few resources available to access and learn about financial services. The resources that do exist, such as getting help from banks, present their own problems. Talking to a bank teller can be extremely intimidating for a number of reasons: the bank may be far from home, there is a lot of confusing paperwork and financial jargon, and you may feel judged for having a small balance. In addition to the many studies that support this, we have also seen this first hand while interviewing low-income people across Africa [2].

Our company, Teller, aims to tackle financial literacy by bringing financial services into the hands of people in Africa, through integration with their favorite messaging apps. We partner with financial institutions to launch an automated ‘chatbot’ that can simplify account opening, provide tips for first-time customers, and make getting help easier — all via messaging apps like SMS. N

Our case study covers the details of our pilot with Orange Money in Madagascar, one of the leading mobile network operators and mobile-money providers. Orange Money currently provides feature-phone enabled financial services to over 40 million customers, mostly in francophone Africa. Teller helped Orange Money launch “MoMo”, an SMS-based financial assistant that helps customers learn how to open an account, look up fees, check their balance, and access customer service.

The full case study is available here

A webinar featuring the Teller’s experienced in building bots for the emerging markets, an overview of their work with Orange Money Madagacar and a short WhatsApp based demo!

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Partnership Considerations for Conversational Interfaces

Conversational interfaces (CIs) are currently being deployed to help extend access to financial education and services across emerging markets with strong results. In one pilot in Tanzania, CGAP found that farmers who used Arifu’s interactive learning platform saved at rates five times that of farmers who did not use the platform. Although these findings are preliminary, they suggest these tools have a significant potential to change behavior.

While many partner organizations are excited by such findings, they often struggle to deploy a conversational interface because of their inability to answer two questions:

  1. Where to find evidence behind the value of using conversational interfaces?
  2. How to decide whether to build a conversational interface in-house or outsource to a third party?

Below are our thoughts on these two questions:

Where to find evidence behind the value of using conversational interfaces?

Multiple organizations told us that despite their curiosity about what conversational interfaces could do for their businesses, they were unsure of how CIs could be integrated and with what results. Many did not understand the inner mechanics of how a conversational interface worked, the design considerations needed when building for different demographics, and, more broadly, the value proposition they offered in practice. The lack of publicly available primers, case studies, and evidence exacerbated their confusion and misunderstanding, which could be addressed if more research reports were made available.

Reports such as this, as well as FIBR’s Artificial Intelligence: Practical Superpowers; GSMA’s Messaging as a Platform; Accion Venture Lab’s Can Chatbots Promote Financial Inclusion; and CGAP’s Interactive SMS Drives Digital Savings and Borrowing in Tanzania are important first steps in addressing the learning curve. The next step is to supplement these reports with in-depth case studies that capture the challenges to be addressed, the value proposition offered by a CI provider, and results observed thus far. This deep dive would not only familiarize partners with the different companies working in this space but also better orient the companies around how best to build, design, and implement CI for different customer demographics.

How to decide whether to build a conversational interface in-house or outsource to a third party?

Below are a few of the considerations organizations should factor into their choice:

  • Security considerations: As described in the previous blog posts, conversational interfaces can provide customers access to information, transaction services, and tailored advice. For the first category—information—the conversational interface can likely provide answers to targeted questions without needing access to personal information, such as transaction records or bank account information. But  the CI will probably need access to the user’s personal information to offer transaction services or tailored advice. This would require the user to submit the personal information over the messaging application, a potential security concern when managed by an outsourced entity or conducted over a third-party messaging application like Messenger or Telegram. To work around this, some use cases will support using a one time password for authentication—similar to PIA when using the interface to access M-PESA. But for use cases that require more in-depth information about the user, organization will most likely need to develop their  CI application in-house and in adherence with the company’s security protocols.
Figure 1: CI PIA requiring an OTP to transact over its application
  • Experience of the technology team: Through the many different open source tools (natural language processing engine; cloud storage; analytic software), non-technical staff can effectively build CIs. However, in some cases, the open source tools either do not work or do not exist for emerging markets. For instance, one company we spoke with built a customized two-way SMS gateway for one of their emerging market clients because the service did not exist in that country. In situations like this—particularly when the technology has not been used before—it may be appropriate to outsource the work to an organization with experience building and working with conversational interfaces rather than trying to build in-house.

As the field of conversational interfaces evolves and their value proposition becomes more clear, organizations will probably have less of a learning curve than those pioneering the technology have now. But to address and minimize the learning curve, reports such as this, along with access to grant support and technical mentorship, can help minimize potential risks while building capacity for organizations looking to trial conversational interfaces.

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The role of language in the construction of a Conversational Interface

As mentioned in the primer, using CIs to interact with customers through freeform African languages can be challenging given the limited amount of localized digital content to source.

Of the conversational interfaces we tested, it was consistently challenging to have a human-like conversation with the interface. Although the CI generally understood basic commands that follow standardized spelling and grammar conventions, it often could not understand or follow the cadence of a human conversation. Training a CI to understand syntax, sentiment, and jargon in languages that are well represented online requires sourcing enough content to understand different scenarios, which is both time-consuming and challenging. Given how new these technologies are in emerging markets, it is possible that the technical teams were not familiar with or did not fully understand how to build complicated natural language processing engines.

This becomes considerably more complex in emerging markets where either the user mixes words from their local language into their English-based conversation, or chats with the CI entirely in their local language. Moreover, the limited amount of content in local languages online makes sourcing the requisite content for the CI difficult, if not impossible, and renders the local language interaction ineffective. This means that, until there is a stronger presence of underrepresented languages online, it will likely be impossible to build in native languages in sub-Saharan Africa.

Example 1: Using buttons and dropdown menus to interact with Simply’s conversational interface

To account for these challenges, we have seen conversational interfaces structure the interaction around a combination of natural language, dropdown menus and image based buttons. As seen in the screenshot from Simply’s CI, the user can interact with the interface by using buttons or selecting options on the dropdown menu

In these cases, the natural language processing engine is used for very basic commands that are well understood and require an almost binary response, while the buttons and dropdowns are used for more complicated responses. Using these two features limits how much nuance the user can exercise in responding to a CI and ensures that the CI is able to provide an immediate and accurate response to queries that could otherwise get lost in trying to understand syntax.

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Criteria to consider when choosing between SMS and messaging applications when building a conversational interface

Though functionally similar, it’s important to understand how SMS and messaging applications differ prior to engagement.

When deciding whether to design a CI on SMS or on a messaging application, it is important to consider the following:

Ease of deployment:

SMS: SMS CIs must be able to integrate their product with the mobile network operator’s system. To do so, CI providers can partner either directly with the MNO or with a third party aggregator, like Africa’s Talking, that works on behalf of companies to push their service onto the MNO’s system. In either case, the CI provider depends on another organization to push their service to customers. Accordingly, the CI provider cannot control the delivery. Among the organizations we spoke with, many struggled with the lack of transparency around knowing whether their message was ever sent to the user and, if not, what the bottleneck was.

Messaging applications: Currently, messaging applications companies have different rules and parameters that CIs need to follow in order to build on their service. On one end of the spectrum are companies like WhatsApp that does not allow external, open access to the public API; on the other end are companies like Telegram that offer open and public access to their API. In the middle are companies like Messenger that review each CI built on their application and can deactivate the CI if it breaks any of their rules.

Target audience reach

SMS: SMS offers incredible reach to a large audience of potential customers— regardless of the type of phone used—in one geography that can be easily accessed by users through a shortcode and therefore does not require a data bundle to access. However, SMS cannot easily be shared across different geographies, limiting the number of potential global users who can access the CI.

Messaging applications: Messaging applications are relatively easy and simple to share among other users [regardless of network] through a link over social media applications, have a rapidly growing user base that tends to be digitally savvy and literate, and can easily reach across multiple geographies. However, despite the rapidly growing user base, the vast majority still do not own the feature or smartphone necessary to access a conversational interface. Moreover, WhatsApp, arguably the most popular messaging application in sub-Saharan Africa,does not have an open API, making it incredibly challenging to use the application as a distribution channel.

Service offerings:

SMS: By design, SMS CIs can offer only a limited number of features and services to the user. Each SMS is capped at 160 characters that cannot link to information outside the message and is limited to interacting with the user purely through  text ( not visual or audio). These restrictions make it difficult to offer a robust and dynamic experience for the user. However, its simplicity paves the way for a more instructional experience, particularly around building user competency in different topic areas, like financial literacy.

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Introduction to the Conversational Interfaces Blog Series

As the cost of accessing the latest in machine learning and artificial intelligence drops, more and more organizations are depending on technology to reduce the cost and improve the quality of core business functions. By leveraging this technology in interactive conversations via conversational interfaces (CIs), customers across different demographics can now receive current, guided assistance, whether they want to know more about the latest agricultural practices or new financial services.

With the advent of these technologies, financial service providers (FSPs) are  digitizing more of the support and services that extend basic financial services to new and future customers, especially to last-mile customers. These technologies—data dissemination platforms, data collection and management tools, alternative credit scoring platforms, digital payment platforms, and e-learning platforms —have the potential to transform FSPs in the following ways:

  • Increase the number of potential customers that can access their services by building solutions that leverage the reach of mobile networks;
  • Decrease the cost of extending services to new and current customers by using low-cost digital tools and services;
  • And improve the accuracy of customer service through automation by using chatbots and conversational interfaces to handle customer inquiries.

Over the past few months, the Mastercard Partnership for Finance in a Digital Africa (FiDA Partnership), delivered by Caribou Digital, has been investigating the role that one of these technologies—conversational interfaces—are playing to contribute to financial inclusion across Africa in three ways:

Over the next few weeks, FiDA will release the following blog posts highlighting our findings across different business considerations on the topic:

  • Key considerations when thinking about using a conversational interface: These three sub-posts, spread across a week, will highlight different design and revenue considerations for those building CIs.
  • Partnership considerations: This post will discuss the different knowledge gaps large organizations tend to have when considering using a conversational interface.
  • Future considerations: With different legislation and technologies emerging, this post looks at the role these might play in the future of conversational interfaces in emerging markets.

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What can Nigeria learn from China’s digital economy? Insights on big data, mobile payments, and what China’s model means for financial inclusion in Nigeria

This blog is authored by Ashley Lewis, Investment Officer, West Africa and South Africa, Accion Venture Lab with support from Tunde Kehinde, co-founder of Accion Venture Lab portfolio company Lidya.

As I prepared for my first visit to China, I was curious about what I would discover and hoping that what I learned would help me in my work to build more inclusive financial systems in Africa. I would be joining a delegation of digital finance professionals in Africa, including Tunde Kehinde, co-founder of Accion Venture Lab portfolio company Lidya. The trip was organized by the Mastercard Foundation Partnership for Finance in a Digital Africa (FiDA)  to learn about the ecosystem in China and bring insights and inspiration to our workplaces.

Tunde and I both live and work in Lagos, Nigeria, home to over 20 million people — one of the world’s megacities. We were fascinated to see how China has galvanized their government, businesses, and citizens to adopt financial products and services and how they’re creating a more seamless financial ecosystem for all. During the trip, we took note of everything we discovered and we’ve distilled our findings into lessons for inclusive finance in Nigeria.

From big data to bigger data

Tunde and I were both impressed by the tremendous amount of valuable data sources companies could tap into for lending decisions. As we met with business leaders from companies such as JD Finance, Ant Financial, and WeBank (the first Internet-only bank in China) , we were surprised to learn that they leverage from 30,000  to 100,000 data points on consumers and merchants in their underwriting process. As a comparison, Lidya uses 100 data points, and that’s a pretty robust data set in a region where high-quality and consistent data can be hard to obtain. Despite their advances, one Ant Financial Executive we met claimed that they are “still in the small data age of big data”, and that in 50 years we will look back and laugh at the datasets we have at our disposal today.  

Payments as the underbelly of a rich dataset

During the trip, we saw how a strong payment infrastructure is crucial for collecting data to build successful customer profiles to lend to the underserved. China’s impressive data collection starts when customers make a payment. The QR code system has been widely adopted for transactions in China, making it extremely simple for consumers to scan and pay. The low cost of implementing QR code payment infrastructure at the merchant level has led to high adoption rates, rich data sets, and minimal fraud. In comparison, Nigeria  has two primary approaches to payment facilitation: point-of-sale (POS) devices and USSD payments. Unfortunately, high costs and and inconsistent internet connectivity, meant that POS devices are hard to come by or unreliable.

QR Codes for locker storage and JD Headquarters

Collaboration for advancement

One thing that Tunde and I both found surprising was the high level of collaboration in the Chinese fintech sector. We expected companies to be highly competitive, secretive, and unwilling to work together in their race to the top. Surprisingly, that couldn’t be further from the truth. Instead what we learned is that many share potential customer pipelines. These companies have started to specialize in finance offerings for clients at various income levels and businesses of different sizes, and they found that it was more productive to share a pipeline so each customer segment gets the most valuable experience from the optimal provider for them.

Tunde and I have both traveled a significant amount, but we both walked away transformed by our time in China. It was a thought-provoking trip — one that allowed us to see the tangible progress of a market that has made significant strides in fintech and financial inclusion. At one point I started to panic, wondering if any African nation, let alone the entire continent, could ever catch up. However, what I came to accept was that our collective goal is not necessarily to imitate China’s model but to forge our own path, centered on the needs of African consumers and small businesses. That path may look entirely different from China’s. There is no one solution for building a financially inclusive future, and each region and country will have their own challenges and successes along the way. While our visit to China inspired us, Tunde and I returned home most excited for what’s to come here in Nigeria.