The Africa Soil Information Service (AfSIS) makes one wonder how people coped before it existed. Africa Soil offers an enormous abundance of peer-to-peer information and services, namely data and maps that are georeferenced. The site fills a much needed gap because knowledge about the condition of African soils because it tends to be fragmented and outdated. AfSIS aims at giving the tools needed to maintain the health of the soil resource base as science and technological developments in remote sensing are providing new opportunities for low cost and efficient applications such as digital soil mapping, infrared spectroscopy, remote sensing, statistics, and integrated soil fertility management. Through such efforts areas of risk can be predicted and monitored.The Globally Integrated Africa Soil Information Service (AfSIS) is a “large-scale, research-based project to  develop a practical, timely, and cost-effective soil health surveillance service to map soil conditions, set a baseline for monitoring changes, and provide options for improved soil and land management in Africa.”

AfSIS’s efforts of dissemination and training allow access to farm communities, public and private extension services, national agricultural research and soil survey organizations, the fertilizer sector, project and local planners, national and regional policymakers,and scientists. It is used in Kenya, Tanzania, Malawi, Mali, Nigeria, Ethiopia, Mozambique, and Ghana in partnership with several academic institutions. Through the success of the project , a global mapping effort has emerged.

Some impressive activities include:

• producing digital soil maps and environmental covariates
• developing, implementing and maintaining the cyber-infrastructure to operate this effort
• developing a spatial database of soil management experiments
• linking the soil management info to the digital maps
• developing information dissemination mechanisms including websites, method manuals and guidelines, policy brief and a digital atlas
• mainstreaming the soil health information system

Image: MMUBlog

A customers’ pattern of airtime top ups is being used to determine the credit-worthiness of a prospective borrower and approve/deny loans.  Will this technique facilitate the development of innovative micro-loans via the mobile channel?  Is the data truly an adequate predictor of customers’ ability to repay?  What’s the upside for the players involved?

Airtime based credit scoring in a nutshell

Most emerging markets have little to no infrastructure that adequately collects customers’ credit history: Credit bureaus either don’t exist, or exist on a limited number of individuals and with very thin financial data. For individuals without credit history, the result is stringent borrowing terms such as high collateral coverage, months of demonstrated savings, and/or individual or group guarantors.

The idea behind airtime based credit scoring is to use an individual’s history of airtime top up as a proxy indicator of what amount they can afford to borrow and their credit-worthiness.  The precise calculations and algorithms employed to do this is the “secret sauce” of Experian MicroAnalytics and Cignifi, two companies working in this space.

What does it take for this to work and what is the upside for those involved?

Models for Collaboration & Benefits

• MNOsare the owners of the customer data, so for starters, they have to allow analytics firms access to build the predictive models.  (It is of course possible that in some countries, airtime top up aggregators have enough customer data for this business, or that an MNO chooses to build their own predictive models, but both of these seem less likely.) The MNOs presumably would only do so if they believed they could gain the following:
  • Increased customer loyalty – Depending on the exact terms of the loan, credit is typically a fairly attractive product offering and one with the potential to keep customers loyal.
  • Drive mobile money usage –Disbursing loans and collecting weekly loan repayments, can drive increased usage of mobile money platforms, leading to increased revenue.
  • Topline revenues – MNO data is typically used for internal analytics, but monetising the data for credit scoring produces topline revenue by leveraging an existing asset.
• Financial institutions – The risk of the credit will always need to be underwritten by a financial institution i.e. bank or credit card company.  While there is certainly effort and risk involved to getting the model right, they have three things to gain if it works:  1) ability to reach a customer segment not previously accessible 2) improved ability to target the right product at the target customer i.e. loan size appropriate to the clients ability to repay and 3) revenues from loans
• Data analytics company / credit agencies – The models take time to develop before they are truly good predictors of credit worthiness, but if these firms get it right, they earn money on the valuable models they have built and hold a key position sitting between MNOs and financial institutions, who otherwise may not want to share data.
• Customer – It is the customer and the customer alone who applies for a loan and gives the ok for a company to use their airtime patterns in the approval process.  The client benefits from access to “instant decision” low value loans, which can be used to smooth monthly cash flows.

What do you think? Will MNOs use this technique to develop and offer innovative mobile money micro-loans? Will stakeholders work together to offer this service?  Is there demand from the unbanked?

Check back next week for guest posts from Experian MicroAnalytics and Cignifi on their work to date in this area.

Photo: MSF

By Médecins Sans Frontières

As a project manager for MSF (Médecins Sans Frontières/Doctors Without Borders), a medical emergency humanitarian agency, I attended this year’s South by Southwest (SXSW) festival in Austin, Texas, in the company of a friend and collaborator from Google who is involved in crisis mapping. We gave a presentation on some mapping work we had done together, and inevitably we discussed the differences and similarities in our geek (high-technology) and primitive fieldworker (humanitarian) cultures.

The two cultures are, on the face of it, rather distinct.

Geeks live in highly connected environments, usually urban, surrounded by technological infrastructure straight from the most outrageous science fiction of decades past. They inhabit an intellectual world of abstractions: math, code, logic, creativity, and business; while their physical existence is supported by a seamless layer of luxury and connectivity. The food, and the coffee, is good.

Humanitarian fieldworkers live a variety of environments from luxury hotels or palatial mansions to mud huts or tents, with variable connectivity, but almost always with constraints. Even in the most modern of African or South Asian capitals the technological infrastructure is often wobbly; there may be high-speed internet but it usually features random periods of downtime. In the mud hut scenario, internet and telephone use generally involves a satellite rig, great expense, and substantial cursing at the fiddly configurations and on-again-off-again unreliability. Our lives are never far abstracted from the constant effort required to enable working conditions, and the intensely pragmatic work of solving immediate problems. That patient is in danger of dying NOW, that refugee camp needs clean water NOW, and if we don’t get the car fixed in the next few hours we’ll be spending the night by the riverbank eating dry emergency ration biscuits. Most of us have at one time or another shared a single broken-down laptop with several people, a leaky thatched roof over a mud floor, and a plate of sandy, oily rice that is as good as we’re going to get for dinner.

Despite these disparate viewpoints (abstraction vs. immediate physical pragmatism) and differing ecological niches (urban café vs. mud hut) there is an unexpected similarity; primitive fieldworkers and geeks share a culture of problem-solving. Nothing makes a geek happier than a tough problem that gets all the neurons firing, and nothing makes a fieldworker happier than a serious emergency that gets the adrenaline flowing (if that sounds callous, in our defence it’s not the suffering that we enjoy, but the chance to make a real difference with our work).

At SXSW we had the chance to share that cultural crossover with a broad audience of geeks, fieldworkers, and an assortment of others, all of whom shared an interest in the intersection of humanitarian work and technology.

My main take-home message was: we are not alone.

We humanitarians tend to take pride in our ability to deal with problems by stretching our ingenuity and using only what is available in the field. My friend from Google was astounded at what we do with spreadsheets, saying “I didn’t think that this could be done without software coding capacity”. We use spreadsheets as databases, stock management systems, maps, payroll systems, and sketchpads. This is, perhaps a strength but I am beginning to realize that we take it too far. There is an enormous community out there in the world, with increasingly robust electronic links to even the most remote field locations, who can help us. Open source code can be written to address problems that we would normally tackle with tortuous repurposing of spreadsheets; there are incredibly talented programmers delighted to donate their efforts, especially to interesting problems. Informal slums can be mapped by volunteers, either people who once lived in the country or even by people who have never been there simply hand-tracing satellite imagery. The astounding success of the Ushahidi project (follow the link to read about an open-source crisis mapping project that started in Kenya to assist people during electoral violence and has since been used around the world, including Washington DC, to map emergencies) shows the power of crowdsourcing or distributed voluntarism to assist people in crisis.

Humanitarians need tools and information, particularly during crises. The tech world is bursting with possibilities to provide just that, often free of charge and with an astonishing level of professionalism. I hope that this meeting of cultures continues to deepen and that the early promise of these innovations translates to real benefit to the populations in crisis that we serve.

Ivan Gayton

Ivan Gayton is a project manager with Médecins Sans Frontières, currently working in Nigeria. During the Haiti cholera outbreak in 2010, he worked together with a team from Google to develop tools to map the outbreak using freely available software (Google Earth). He and Google’s Pablo Mayrgundter continue to work on an open-source epidemiological mapping tool in their scant spare time. Other than an interest in seeing further cultural cross-pollination between humanitarians and techies, Ivan has no conflict of interest, and no financial interests whatsoever in the matter.

This and many other MSF blogs are available at PLoS – Speaking of Medicine https://blogs.plos.org/speakingofmedicine/category/msf-2/