How Kiva Fights Global Poverty With the Power of Big Data

More than a third of the worlds population have no access to financing, but thanks to technology, that is changing fast

Even if you have the best idea in the world, starting a small business is hard. But if you’re one of the two billion people around the world who lack access to basic financial resources such as a bank account or credit, it’s just about impossible.

Karen Little, director of development at Kiva, a microfinancing web platform that helps global borrowers get small loans, agrees. “There are very smart people all over the world who just don’t have the opportunity because they don’t have access to capital,” she says.

Banks have long overlooked the poor largely because they had no record of their earnings, spending habits or history of repaying loans. Lacking financial data on more than a third of the world’s population affected more than individuals’ access to financing or the simple safety of deposit accounts, it also deprived policymakers of critical information to address the issue of global poverty itself. 

The Internet and mobile phones are changing that, with some 85 percent of the world’s population now using cell phones. This shift in basic information technology has put the poor on the map by generating huge volumes of information on their location, behaviors and needs. As the Big Data revolution spreads to the world’s most underserved populations, it’s making access to capital more equitable and accountable than ever, as well as providing the insights and tools needed for a deep understanding of poverty and the best approaches to reform.

“There are wide differences in data production between rich and poor, but the reality is that cell phone adoption is really high around the world,” says Emmanuel Letouzé, director of the Data-Pop Alliance, a global coalition focused on using Big Data as a tool for global development. “A lot of people in the developing world jumped the landline phase and went straight to mobile phones. The same thing is happening with computers—with mobile phones again filling that void—and a lot of data is being produced as a result.” 

The Hansel & Gretel effect

The data refers to all the digital, networked information that businesses and consumers generate with our mobile and connected devices all day, every day. This includes web activity, data collected from Internet of Things sensors and satellite-generated data, to name a few, which all power our 24/7 digital trail. “These are the breadcrumbs,” says Letouzé, “the hard, structured tiny pieces of data that we leave behind as users of these devices. And cell phone records are certainly a big piece of that.”

In the field of banking, for example, cell phone data is mined to determine the best locations for microfinance and telecommunications offices in underserved areas. More importantly, digital deposits and payments are creating credit histories for those that have none.

Digital payments to non-bank lenders will eventually become the proof of creditworthiness that conventional bank loans require. And, increasingly, small loans without that level of proof can be obtained online via sites like Kiva or through a booming number of mobile apps such as Tala, the most popular financing app in Kenya. Tala uses an algorithm that crunches up to 10,000 indicators of an individual’s level of responsibility, such as text volume and length of call time, to make small loans. 

“We use a lot of data to try and figure out what makes a good borrower and who has a high likelihood of repayment,” says David Pollak, vice president of engineering at Kiva. Beyond qualifying new borrowers, advanced analytics helps Kiva evaluate and track the performance of 300 separate microfinance institutions and social enterprises, over two million Kiva borrowers and one million Kiva loans from more than 1.4 million Kiva lenders.

People helping people

Data analytics is also the reason Kiva got a commitment of $7 million from the Hewlett Packard Enterprise Foundation for a campaign called “Matter to a Million,” which enables HPE employees across the globe to make $25 loans to entrepreneurs of their choice in over 80 countries. Since 2014, employees have lent over $11.5 million to Kiva borrowers, a risk that could not have been justified without Big Data. “Kiva wouldn’t exist if it weren’t for the power of analytics over gut-based choices,” Pollak adds. “Using data is why we’re able to connect lenders and borrowers across the world.”

The next phase for Big Data is using analytics to tackle poverty at its root. One research project at the State University of New York at Buffalo has used cell phones to precisely map poverty in Senegal. Traditional poverty maps require intensive on-the-ground surveys and can only be updated every three years, at best. Cell maps—thanks to the ubiquity of smartphones—are cheap and granular. They can be updated in real time and shared with local policymakers to create more effective poverty programs. “For poverty eradication, it is vital that the maps are available at the lowest spatial granularity of policy planning, and should be generated in a cost-effective manner and updated frequently,” explains SUNY researcher Neeti Pokhriyal.

Data driving disaster prevention

At Data-Pop Alliance, a large number of data streams are being mined to make precise predictions on everything from crime to natural disasters as an aid to development. And in partnership with the Qatar Computing Research Institute, the organization is analyzing millions of tweets to gather on-the-ground intelligence about poverty and general economic conditions in Egypt. Another of its projects is leveraging Google Earth to estimate communities’ vulnerability to flooding, which disproportionately affects the poor. 

Eventually, Letouzé hopes Big Data will support bottom-up decision-making in which poor people take part in shaping and exerting political pressure on behalf of policies and programs to improve their lives. “The question is not just how people in power are going to use this data,” as he puts it. “It’s also about how people who produce the data can actually make sense of what’s available and advocate for themselves.” 

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