The value of having a data centric approach to set up a high performing DFS agent network

Morne van der Westhuizen
30 May 2018

Introduction

Africa is at a turning point. It has been growing at a significant rate over the past years, but the economic growth has not transformed into the same level of financial inclusion. Economic growth has resulted in more opportunities for low income households, but with this comes a need for more affordable and accessible financial services. DFS providers have been playing a vital role in servicing the market segment traditionally excluded from the formal financial sector.

Mobile money is a crucial service for the previously unbanked. It improves the drive for a cashless environment and provides communities with a platform to transact in a similar way that you would have at a bank. Setting up a network of mobile money operators, termed “agents”, plays an essential part in the success of the ecosystem that provides basic financial services.   But setting up this agent network can also be a daunting task if not done in a supervised manner.  Data analytics providers can benefit, achieving a high performing DFS agent network.  This blog post looks at three key metrics that should be considered for optimization of resources allocation and the profitability and efficacy of the agent network.

As part of embedding responsible finance practices into an organisation, and enabling financial inclusion in underbanked markets, it is important to have a high performing, motivated and well distributed agent network.  DFS providers have the responsibility to serve clients with respect, ensure that their information is securely stored, provide an infrastructure that is liquid, well trained, available during trading hours, well located and educated in the available product offerings.  Although there is a human element involved in creating the mentioned environment, good data analytics and alternative data sources assists providers to achieve agent networks that are both high performing and aligned with the principles of responsible finance.

Population density

The physical location of any part of the network is very important, and understanding where people live, where they work and how they move between the two locations can be a proxy for a good location. Traditional government statistics (usually outdated, and only captured on province/district/suburb level) and alternative data sources (like http://www.afripop.org/) can be used more successfully together. For instance, Afripop uses satellite imagery from NASA, and found there to be a direct correlation between the night lights that can be seen from space, and population density. Using these datasets, DFS providers can get localised population density in 1x1km square arrays, that provides granular, geospatial information about the potential customer base. It allows providers to make informed data driven decisions for developing agent networks based on timeous population density statistics. The below image is an example of available Afripop data with estimated localised population density for Kenya, demonstrating how population is concentrated in certain areas of the country.

[1]

 Money Corridors

A money corridor is a high value/volume route between money senders and receivers. The traditional model for sending and receiving money consisted success of public transport and relied on a network of taxis and buses. Understanding these traditional ways increases the value from the insights as to where traders, parents, farmers and others need to send money, and where to expand an agent network. In turn, it improves the optimal agent placement and liquidity management. The below image demonstrates how public data sources can be used to provide insights into international money corridors, and the same principles can be applied to intra country corridors.[2]

Liquidity

Liquidity management is probably the most difficult factor in any network. As an agent/partner needs to balance the need between having enough e-money as well as cash, the rebalancing of this can be quite tricky and nuanced. Various factors complicate this, like proximity to ATMs, banks and other financial access points that enable the replenishment of either e-money or cash. In the rural areas and typically unbanked areas rebalancing can be a big problem as formal financial institutions and banks may not have a presence. Knowing where formal and informal financial institutions are to aid in managing liquidity, is a tremendous benefit.

Proper liquidity management and financial planning allows agents to balance cash flow more smoothly. As a result, agents can process more transactions and serve customers better and more responsibly, if they ensure that they have enough cash to satisfy the demand. If not, liquidity gaps will appear, affecting agent performance and customer experience. Institutions like FSD (Financial Sector Deepening) have done some remarkable research on this, producing insightful datasets that can be used to estimate proximity to available formal and informal financial institutions. The below image shows a summarised geospatial plot of financial access points in Zambia. Using this data, DFS providers can see where there are support networks that may add in potential partnerships for rebalancing float and managing liquidity of an agent network.

[3]

Historically the set-up of a network was a daunting task, requiring significant field research and man hours. Finding the right location that serves the community, creates money corridors, and is also profitable to the agent and the DFS provider is vital. Some companies have been very successful in applying a data centric approach to agent roll-out strategies as well as to manage the performance and impact of the network. Data Analytics and Digital Financial Services Handbook[4] summarizes various DFS cases studies, providing practical insights and lessons learned.

Among them is the case of Zoona, the leading DFS provider in Zambia, offering OTC transactions through a network of dedicated agents. Zoona has a data-driven company culture and tasks a centralized team of data analysts to constantly refine the sophistication and effectiveness of its services and operations. Zoona has developed an in-house simulator to determine the optimum location for agent kiosks. The approach uses Monte Carlo simulations[5] to test millions of possible agent location scenarios to identify which configurations maximize business growth. These simulations provide scenarios that introduce complexity and uncertainty in the roll-out strategy, and can provide confidence intervals around the expected outcomes. To ensure reliability, modelled scenarios are cross-referenced with input from the field sales team, which has local knowledge of the area and the outlets under the most pressure. Together this establishes a customer centric approach to selecting the most impactful location. Zoona keeps a close eye on the agent lifecycle, and have a robust way of setting targets to agents in order to measure performance. To set targets in a way that it is both motivating and realistic, it acknowledges the fact that agent location and age can add nuance to performance expectation. Zoona analyses the agent data to project the performance expectations for agent segments, such as urban and rural, producing ‘performance over time’ cohort curves for each agent, down to the suburb level. These KPIs support a model for good, robust agent management methods. As a result, the Zoona’s network consists out of 1,300 agents across Zambia, and have a 60 days active consumer base of more than 1.5 million consumers, with a product suite consisting of domestic money transfers, a low-cost overdraft facility for agents, an e-wallet and more. Such results demonstrated DFS that act as a responsible financial service provider that serves the community and works tirelessly for financial inclusion.

Conclusion

Using organizational data and alternative data sources proves to be valuable in setting up a high performing DFS network. Although some of the alternative data sources might not be as up to date as organizational data, it still adds value by blending it with existing DFS data sets. Applying responsible finance practices to DFS providers means setting up liquid, high performing networks to improve financial inclusion. Using a combination of alternative and traditional datasets proves to be extremely effective in reducing the unknown variables during the management and expansion of networks, and the impact it has on financial inclusion is enormous.

 

Adapted from a case study and additional content presented in the Data Analytics and Digital Financial Services Handbook (June, 2017), this post was authored by Morne van der Westhuizen, IFC-Mastercard Foundation Partnership for Financial Inclusion, for the Responsible Finance Forum.

 

[1] Figure 1. Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. Source: http://www.worldpop.org.uk

[2] Figure 2: Estimated Cross border remittances from Ghana during 2016. Source: http://www.worldbank.org/en/topic/migrationremittancesdiasporaissues/brief/migration-remittances-data

[3] Figure 3. Extract from the FSD Geospatial Project showcasing the geospatial plots of MFI’s and SACCO’s. Source: http://www.fsdzambia.org/updated-geospatial-map-for-financial-service-access-points-in-zambia/

[4] https://responsiblefinanceforum.org/wp-content/uploads/2017/06/IFC-Data-HandBook.pdf

[5] Monte Carlo simulations take samples from a probability distribution for each variable to produce thousands of possible outcomes. The results are analysed to get probabilities of different outcomes occurring.

Caveat Venditor: A New Model for Buyer Selection in Responsible Microfinance Equity Exits

Daniel Rozas and Sam Mendelson
23 May 2018

For most, socially responsible investing means just that – investing in a manner that not only generates financial returns but also produces positive social value. But what does it mean for an investor to be “responsible” when selling their holdings? How does one stay responsible at the very moment when one ceases to be an investor?

This is a basic challenge facing investors seeking to “exit,” i.e. sell their equity stakes to a new buyer. The issue isn’t entirely new. It first emerged in the mid-2010s, when several microfinance investment vehicles (MIVs) were starting to reach the end of their 10-year terms and were seeking to divest their assets. This issue was first addressed in the financial inclusion sector by a 2014 papercommissioned by CGAP and CFI, which first defined many of the key questions that socially responsible investors need to address when selling their equity stakes.

With another four years of multiple exits under the sector’s belt, NpM, Netherlands Platform for Inclusive Finance, along with the Financial Inclusion Equity Council (FIEC) and the European Microfinance Platform (e-MFP) asked us to take a closer look at one particularly tricky part of the exit process – selecting a buyer that is suitable for the microfinance institution (MFI), its staff and ultimately its clients. The result is Caveat Venditor: Towards a Conceptual Framework for Buyer Selection in Responsible Microfinance Exits – a new paper that goes beyond raising questions, and seeks to provide a template to help investors navigate the complex terrain of “responsible exits.”

The research – an investor survey, several in-depth interviews and a workshop during European Microfinance Week – found a mix of approaches applied by different investors. But these nevertheless shared many common elements aimed at making sure that the buyer will honor and pursue the social mission of the institution being sold. We consolidated these elements into a “Conceptual Framework for Buyer Selection” – a flowchart representation (plus explanatory notes) of the steps and criteria inherent in responsible buyer selection in microfinance equity exits.

 

Caveat Venditor: A New Model for Buyer Selection in Responsible Microfinance Equity Exits

Overall, the consensus among investors was that social responsibility in the context of an exit largely means excluding those potential buyers who are deemed unsuitable and then applying the financial offer (how much a buyer is willing to pay) to select among those remaining. But that exclusionary process is driven by exceptionalism, i.e. buyers have to be obviously unsuitable to be eliminated from consideration, and such exclusions tend to be rare, based on factors like unclear ownership of the buyer, inability to trace the source of the buyer’s funds or suspicion of the buyer’s motives. And because such cases are rare, what this means in practice is that the financial offer is the dominant factor in the decision, much as in the world of purely commercial investors. We refer to this by a term borrowed from medicine, the FirstDo No Harm principle.

However, there was an important dissenting view among some investors, which holds that the Do No Harm exclusionary criteria are insufficient for a social investor. After all, a commitment to a social mission is a positive one; it must do good, and not simply avoid doing harm. In effect, this view seeks to invert the process, first deciding whether the financial offer meets the selling investor’s predefined financial objectives, then considering its value to the institution’s social mission. That value need not be strictly mission-driven, nor is there any expectation that the ideal buyers are socially motivated NGOs. Rather, the question is of organizational fit. Extending the medical analogy somewhat, we call this the Best Interests approach. We believe that this model, with its positive obligation on the seller(s), is better aligned with pursuing a social mission while delivering a reasonable financial return – which is at the core of the social investment value proposition.

The framework consolidates the practices of different investors we spoke to but also advocates an evaluation process that moves beyond Do No Harm toward Best Interests while incorporating elements of both. It is structured so that questions are organized based on the type of transaction being contemplated: a minority or majority stake being sold, as part of a consortium of shareholders, or by a single investor.

The framework is not designed to be – nor could it be – one-size-fits-all: Each exit is dependent on the investee’s mission and the context in which it operates, as well as the seller’s own objectives. The framework should be thought of as providing a rubric that each seller can expand upon themselves. It can be thought of as a three-stage process:

  1. Are there exclusionary factors that mean the potential buyer is manifestly unsuitable; and, if not, is there any reason to believe that regulatory approval for the purchase would be difficult or unlikely?
  2. If not, is the initial, indicative financial offer within a predefined range that is acceptable to the seller(s) based on the overall double-bottom line objectives of the fund?
  3. If so, how does the proposed buyer, and its strategic objectives for the MFI, align with the social mission and the other best interests of the MFI?

We believe that the responsibility of finding the right buyer lies very much with those doing the selling. And if the sale means handing over control – a majority stake – this creates an even greater burden. As we argue in the conclusion, “A buyer selection practice which gives primacy to the financial offer and considers social mission and strategic value to the investee – the investee’s best interests – only to reject egregiously unsuitable buyers, fails to keep in mind that the best interests of the MFI and its clients are, for the investors who put funds into the MIV, arguably the primary reason for investing in the financial inclusion sector in the first place.”

We hope this framework will serve as a resource for investors embarking on an equity sale. We hope it could also: help investors to brief external organizations that assist them in exit trajectories (investment banks, advisory firms, etc.); assist new categories of impact investors that have little experience in exits; and serve as a guide to potential buyers to help understand selection criteria and prevent interested (but unsuitable) buyers from wasting time on a futile due diligence process. We hope too that it will inspire further work on an issue which, as equity sales continue to grow, will only increase in importance.

Daniel Rozas and Sam Mendelson are co-authors of the joint NpM/e-MFP/FIEC research project on buyer selection in responsible exits.

Photos courtesy of Pexels and Braden Hopkins via Unsplash 

This blog was originally posted on NextBillion’s website

Lessons for Financial Inclusion – The Value of using geo-localized Data to drive the Uptake and Usage of Digital Financial Services in Ghana

Sinja Buri
08 May 2018
Introduction

Digital Financial Services (DFS) hold the promise to accelerate and deepen financial inclusion – especially in markets where traditional banking has not yet reached large segments of the population. DFS providers have acquired millions of new customers. But many digital services ecosystems experience intermittent use among the customer base and are working to develop active ecosystems. Data analytics provide an opportunity to help address this activity problem to keep customers engaged. New data analytic methods, combined with responsible use of customer and provider data show strong results for more robust digital financial ecosystems and better financial inclusion.

Geospatial data is one such example. Making use of geospatial data can be especially useful to visualize relationships that would otherwise not come out so clearly and to provide further basis for evidence-based decision making about where to focus strategies and resources for financial inclusion and customer engagement. This blog post explores two ways in which geospatial analytics can be used to drive financial inclusion.

It is key that financial service providers adapt and implement responsible finance practices throughout their operational processes. Customizing their product offering and services based on customer data provides huge potential to improve access to financial services as examples in this blog post show. Doing so responsibly requires managing related risks and educating consumers about the use of their information and data, obtaining their consensus as well as putting procedures in place that allow users to opt out and not share their information is essential and should be a pre-condition for any use of individual data by financial service providers. Or simply not using individualized data at all.

Mapping dimensions of financial inclusion for operational decision making
Uptake and usage of digital financial services

Tigo Cash Ghana was launched in Ghana in April 2011 and quickly established itself as the second-largest mobile money provider in terms of registered users. But despite high registration rates, getting customers to do various transactions through mobile money remains a key challenge and focus. GSMA estimates activity rates on mobile money accounts worldwide to be around 30% on average only.

IFC partnered with Tigo Ghana to deliver a predictive analysis of mobile money adoption and usage. Six months’ worth of call detail records and mobile money transaction data was analyzed using the model and applying it in day-to-day operations.  The data driven approach was successful. It helped Tigo to adopt new data-driven approaches more generally; and the one-off analysis helped Tigo to acquire 70 000 new additional customers – active customers, importantly.

Predicted adoption rates from the modelling were used to develop a district level heat map of potential mobile money adoption. This heat map of potential mobile money adoption was combined with the existing mobile money adoption rates in respective districts resulting in a map of districts that shows where the gap between actual and potential adoption is the highest, presenting also the highest potential for financial inclusion in these districts. Using geospatial analytics at a district level ensures responsible data use by visualizing information at an aggregate level that is disassociated from any individual or even groups of individuals.

The predictive analysis that was conducted in collaboration with Tigo Cash Ghana is also referenced in the Handbook on Data Analytics and Financial Inclusion.

In this example, the geospatial mapping helped identify new strategic areas for Tigo to engage customers – particularly those outside the urban centers in Accra. Using a related but different approach, the following example with another IFC project with Cal Bank also illustrates the benefit of using geospatial data analytics.

Identifying areas of biggest need for digital financial services

Mapping exercises cannot only be useful to improve service adoption and usage after they have been launched. Going one step back, they can also be a tool to inform the roll out and choice of locations for new services to be introduced, such as agent banking.

CAL Bank in Ghana is about to launch their new agent banking network with an objective to specifically cater to the unbanked. In order to find out where the unbanked customers are concentrated, the IFC partnered with Fraym, a data analytics company, for a mapping of the unbanked as well as target households for agent banking all over Ghana. This approach avoids customer data issues by not using any sensitive data at all. Rather, public survey data, machine learning models and satellite maps allow geo-localized demographic data to be interpolated to gain strategic insights.

The resulting maps drew attention to areas with high concentration of unbanked that the bank was previously not aware of and led them to make informed choices on where to locate agents to maximize the availability of financial services in these areas.

Heat map of Ghana depicting high concentrations of unbanked households – Developed by fraym

Conclusion

Geospatial data analytics can employ responsible data principles and deliver insights that improve operations and drive financial inclusion. These examples illustrate this through using predictive modelling in the first case, whose results are non-specific to individuals. And in the second case, using publicly available and national statistics information to achieve insights on market demographics.

Good quality and reliably geo-coded datasets linked to a broad range of socio-economic indicators are only slowly becoming available, especially in developing and emerging markets or if available, they are often not used to their full potential. Looking forward, more granular geospatial data is likely to become available – either from DFS providers directly, third-party surveys, or other sources. Granular data can introduce risks or individually-identifiable patterns. These risks must be managed, to ensure customer data protection is at the forefront of the analytic approach. As this blog post illustrates, data can be properly managed, non-specific to groups or individuals and still yield powerful insights.

Adapted from a case study and additional content presented in the Data Analytics and Digital Financial Services Handbook (June, 2017), this post was authored by Sinja Buri, IFC-Mastercard Foundation Partnership for Financial Inclusion, for the Responsible Finance Forum Blog April, 2018.

8 reasons to care about the EU’s new data-protection rules

Kelly Ommundsen
22 May 2018

The General Data Protection Regulation (GDPR) comes into effect on 25 May and represents the biggest change to European data privacy and data protection laws in more than 20 years. This new framework aims to gives individuals more control over their personal data and simplify the regulatory environment, so they can more fully benefit from an inclusive and trustworthy digital economy.

Contrary to what many people believe, this is not a result of the recent high-profile instances of data use and misuse. It’s not a mere change in the legal fine print – these companies are preparing for a massive transformation in the regulatory landscape that will have wide-ranging impacts for organizations and users alike.

The General Data Protection Regulation (GDPR) comes into effect on 25 May and represents the biggest change to European data privacy and data protection laws in more than 20 years. This new framework aims to gives individuals more control over their personal data and simplify the regulatory environment, so they can more fully benefit from an inclusive and trustworthy digital economy.

But what does this mean for you?

1. It’s not just for Europeans

While GDPR was passed by the European Commission, it does not only impact Europeans. The new regulation applies to any organization or business operating on European soil, as well as those outside offering goods and services to EU citizens, including online business. Given the global nature of the internet, this means the majority of online services and individuals are affected in some way.

2. All of your sensitive information is protected

The definition of “personal data” has been expanded to include everything from your name, location, photos and bank details – as well as other ways that you could be individually identified online, like your IP address. Your sensitive personal information, such as genetic data or data that would reveal your sexual identity, political opinions, or religious affiliation, is protected under GDPR as well.

3. The right to be forgotten

If your information is no longer required for the purpose for which it was originally collected, was obtained illegally, or you did not consent to have your data collected, you have the right to have your data erased. If your data is incorrect or out of date, but you don’t want it all erased, you also have the option to have it updated.

4. You are in control of your data

Under GDPR, you have choice and control. Explicit consent is required to gather and process your information, which means companies will be requesting permission to collect your data much more frequently – and you will be seeing many more “click to proceed” or “do you agree?” windows popping up in the future.

5. Your boss has to comply as well

Access to data and personal information stored about you also applies to your employer. With these new regulatory tools, if you are located in the EU, you can file a request to have all the data that has been collected about you as a worker – including interviews, performance reviews, payroll and attendance records, as well as any emails to, from, or about you, and your company must comply within 30 days or face severe penalties.

6. You can transfer your data more easily

Want platform flexibility or to switch providers altogether? You will now have the power to download all the data an organization has on you in a readily usable format, letting you check what companies have collected, as well as easily transfer your data between platforms.

7. Your data is safer

The new rules bring a new level of safety and protection for users. Organizations have to meet a higher level of security to ensure integrity and confidentiality of your data, using encryption and other cyber-resilience solutions.

8. You will be notified if there is a breach

Organizations can no longer leave users in the dark if they are attacked and data is compromised. While GDPR aims to keep your data safe and protected, cyberattacks and cybercrime still remain a risk. Should your personal data be compromised, organizations are required to notify the authorities or individuals within 72 hours of a security breach.

Companies are also strongly incentivized to comply or face fines up to 4% of annual global revenue – which could translate into billions of dollars for top global platforms.

Gold standard for privacy

In short, the GDPR was designed to empower individuals to know, understand and consent to the data that is collected about them. It turns the current data-business model on its head – according to which companies were incentivized to collect as much data from users as possible in order to monetize it in the future – towards one that is more balanced. Rather than having the burden of opting out, consumers will have the opportunity to opt in if they choose; this new paradigm rewards trust rather than taking advantage of vague implied acceptance.

Although there may be some companies who prefer to fragment and silo their data and treat it differently across different geographies – this runs the reputational risk of companies being seen as intentionally providing a lower “fool’s gold” standard of privacy and protection to some of its consumers and not others. At the end of the day, it may be better for businesses to view GDPR as the gold standard for how all personal data should be treated, regardless of where it comes from.

As the EU leads the charge as a global pioneer in strengthening the trustworthiness of data with GDPR, many people believe that these four letters could have ripple effects around the world, encouraging others to raise their privacy standards. While it’s likely varying interpretations and even strong opponents will remain, as norms change and consumers demand more control, the GDPR presents an initial set of policies that can enable an inclusive and trustworthy digital ecosystem to emerge.

*Community Lead, Digital Economy and Society System Initiative, World Economic Forum LLC
**This post was published on the European Business Review’s website.