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How Kiva uses data analytics to lend a hand (and money) to refugees

Jessica Twentyman Profile picture for user jtwentyman July 10, 2018
The micro-loans platform has demonstrated that, despite perceptions of being risky borrowers, refugees repay loans made on its site at the same rate as other recipients.

For most financial lending institutions worldwide, refugees are typically considered to be too high risk to make suitable borrowers. Their credit histories may be limited or inaccessible. They typically have few fixed assets to offer as collateral. Their current living situations can be temporary or uncertain, until they find somewhere to settle permanently.

At the same time, a loan can be a lifeline, helping a displaced person to move beyond emergency aid and start building for the future. And despite the perceived risks involved, according to executives at micro-lending platform Kiva, refugees who receive loans via their site are no more likely to default than other borrowers who receive funding on Kiva.

And they’ve got the data to prove it. That’s based on Kiva’s launch in 2017 of its World Refugee Fund (WRF), an effort to formalize its effort to scale refugee funding on the platform.

Following the launch of this fund, Kiva has provided $3.5 million in loans to almost 4,000 refugees and internally displaced peoples (IDPs). In 2018, it’s aiming to reach some 8,000 of them, with loans totalling $7 million. These include Syrian refugees in the Middle East (particularly Jordan), IDPs in Colombia, and refugees from Burundi and the Democratic Republic of Congo now based in neighbouring Rwanda.

According to Kiva’s recently released Refugee Impact Report, loans to refugees and IDPs have a repayment rate on Kiva of 96.6%, while loans to non-refugee populations have a repayment rate of 96.8%.

Crunching the numbers

A cloud-based data warehouse from Snowflake Computing sits at the heart of Kiva’s efforts to analyse the impact of loans made by the non-profit organization and its partners, explains Kiva CTO Kevin O’Brien:

The big question at the heart of our data strategy is this: Can we use evidence-backed research to look at our lending activities, the types of loans we make and the types of populations that we serve and see from that research what’s actually having the most impact on the lives of borrowers? Snowflake has really helped us in that regard, enabling us to use our findings to make changes to the website, and in how opportunities to lend are presented and sorted so that we are able to ensure that money flows to the potentially most impactful loans.

Implemented around two years ago, the Snowflake data warehouse was particularly useful in establishing that refugees are credit-worthy recipients for lending - and Kiva now has plans to take its use of that data further, he says:

It’s very much the case that a combination of Snowflake with Looker analytics as a front end is what has allowed us to do the necessary analysis and arrive at this interesting finding, which may seem counterintuitive to those who believe that the refugee population is a risky lending proposition.

Having the ability to run queries on very large volumes of repayment data is key to this. For example, the lenders who use our platform are lending at $25 at a time, so in the case of a $1,000 loan, that’s 40 lenders being paid back over, for example, two years - so that’s 24 repayments each.

Typically, there are around 900 data points per loan and since we doing large numbers of loans and need to take into consideration penny rounding issues and currency exchange issues, plus monthly settling of balances, there are millions of data points to deal with.

A bet on blockchain

The goal now, he continues, is to use the data that Kiva holds in Snowflake on these loans to refugees in order to help establish a credit history for each of them. It’s in talks about this idea with various international organizations, including the United Nations and World Bank, with the basic concept being a shared digital ledger, potentially based on blockchain technologies, and managed by various non-governmental organizations.

This would provide identity credentials and credit histories for people who, through no fault of their own, are unable to access more conventional financial lending systems, says O’Brien:

We want to be part of the effort to leverage the data we have, our partners’ data and other data from other NGOs, to create an environment where it becomes much easier for people who have needed to leave their homes, for various reasons, to prove that they are who they say they are and to get loans they can use to improve their circumstances. We believe that sharing our experience and taking this idea further will encourage other lenders to start serving displaced populations.

We see a future where refugees and those living in economically unstable communities can share their Kiva history with regional and national banks for credit lending decisions powered by Snowflake. As we help more people move up the ladder of financial inclusion, we can focus on reaching more of the communities who need our help most.


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