Machine Learning 4 Good: Robots for a Better World

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Today’s post is the third of our blog post series authored by the speakers at the upcoming 2ML event. This article by David del Ser, Practice Director at Bankable Frontier Associates, presents the advantages and disadvantages of starting a new business in the emerging markets, and how Machine Learning helps alleviate the disadvantages. Join him at 2ML on May 8-9 to hear the complete talk, where David del Ser will showcase concrete applications of ML in financial inclusion.

Being poor in an emerging market like Tanzania or Mexico means a lot of hard work just to survive. Not only your income is low, it also tends to be very volatile. So it is tough to save money and invest in your small business or farm. In addition, you may not even have a physical address, let alone financial accounts for your business. This means the micro sizes of the transactions involved render standard developed country financial products unprofitable. As a result, low-income populations are not served by the traditional banking sector, making it more difficult for them to prosper and escape poverty. To give you a sense of the magnitude of the problem, there are two billion people worldwide without something as basic as a bank account!

At BFA, we believe these problems are addressable with the right approach and partners. Our prediction is that technology and innovation will one day ensure that anyone, anywhere can enjoy financial products that are accessible, affordable and appropriate for them. Since the best way to predict the future is to build it, we run initiatives like the Catalyst Fund, which selects and supports innovative fintech startups that are expanding the financial services industry in these markets. We also launch research collaborations like FIBR, which explores how the data created as the economy digitizes can be used to better understand and serve low-income consumers. BFA partners with organizations like MasterCard Foundation, JPMorganChase, and the Gates Foundation to make this progress happen.

We are also feeling optimistic as we have repeatedly seen the positive impact of technology. For instance, almost every adult in the world already owns a cell phone. With that infrastructure in place, mobile networks allow anyone in Tanzania to send money back home more easily than in Spain. At the rate mobile money networks like M-Pesa are expanding, soon everyone will at least have a mobile wallet and a digital record of transactions. Even better, smartphones are quickly being adopted in these markets and they create lots of data with all their sensors. Those new pools of user behavior data can feed Machine Learning systems to power financial services that are more inclusive and valuable.

In our work, we have repeatedly seen entrepreneurs create products that better fit the needs of the poor by cleverly deploying Machine Learning in a multitude of ways. For instance, Tala offers loans through an app that uses browsing history and social media to assess your credit risk. Smile Identity creates and validates identities across Africa by taking selfie pictures and comparing them with any ID card. World Cover offers ultra low-cost insurance for farmers in Ghana by using satellite images to completely automate drought claims.

These and many more examples are leveraging the power of old and new technologies to create financial services that can truly make a difference. During the talk at 2ML, we will provide an overview of the new and exciting applications of Machine Learning for financial inclusion, while reviewing a few use cases in more depth as exemplar concrete applications in the context of emerging markets.

Want to know more about how ML is used in several industries in emerging markets?

Join the #2ML18 event on May 8-9 in Madrid, Spain. Get your ticket today so you can meet all the speakers as well as the BigML and Barrabé teams, the co-organizers of 2ML. Looking forward to seeing you there!

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