Enterprises have grown dramatically in their ability to apply analytics to solve corporate ailments. But, as clichéd as it must sound, there is a far loftier purpose for analytics than just enabling businesses to make money, reduce costs, mitigate risks, engage in smarter purchases and so on. Yes, these are important. But, of equal importance ought to be the focus on how we can use data and our vast troves of analytics core competence in areas that correct societal wrongs and better people’s lives.
One of these areas is enabling better economic outcomes in regions of the world where bias or lack of information are holding a culture back.
Financial credit to the indigent and the entrepreneurial in Senegal
In Senegal, a West African nation of about 14 million people that is plagued by high unemployment rates, the drive to become economically prosperous is often unmatched by the ability to lubricate the credit system to serve the wellspring of entrepreneurship in Senegalese society. Traditional sources of data to determine creditworthiness of the poor are universally absent. Much of the economic activity is cash based and reliance on some of the modern-day credit instruments is often minimal to nonexistent. Under these circumstances, applying some of the standard credit evaluation methodologies is likely to be a nonstarter.
Enter the French digital finance group Microcred, which works to contribute to the growth of local economies in developing nations by offering simple, accessible financial services. The organization provides lending to nearly a half-million micro-entrepreneurs in eight African countries that either lack or have weak guarantees, and would otherwise be unable to access financial markets.
Microred, in partnership with Datakind (a pro bono group with the goal of offering data services through data scientists to nonprofit organizations), undertook primary data collection through highly personalized loan applications that considered the unique economic conditions of the population that they aimed to serve to develop credit risk models that would attempt to realistically assess the likelihood of loan repayment or default. The data from about 110,000 loan applications made over a seven-year period was used to build rich predictive models to assess default likelihoods. The idea behind developing these predictive models was not just to preselect the defaulters and provide loans that are likely to be safe but also ensure that drivers of loan default are identified and the borrower pools are significantly expanded in the process. The result is a far greater availability of sensible credit options, higher levels of economic activity and greater overall economic prosperity that significantly alleviates widespread poverty and desperation.
There are many such examples of advanced analytics having a positive impact on other areas, such as anticipating and protecting against environmental disasters, developing lifesaving medical interventions, and creating social programs for women and children in less developed economies.
What happens when data is sparse or missing?
While wanting to effect good outcomes with data is a worthy aspiration, it is not always easy. Sometimes crucial data are missing and that can hamper the best of us from making reasonable assessments of the available policy options. Take for example structured survey data on national income levels. Those of us living in the developed world are accustomed to interacting with entire institutions dedicated to collecting, processing and analyzing data at scale on all things associated with economic development. This data is often triangulated with other sources of information so the resulting conclusions hold up to rigorous scrutiny — biased electioneering slogans notwithstanding. This is not the case in less developed economies where data is often poorly collected and curated and can be malleable.
This is where alternate data sources can be used as effective proxies. Sendhil Mullainathan, an economist from Harvard, shows how several new techniques and unusual data can be used to accurately assess economic activity. In Uganda, satellite photos can be used to estimate harvest sizes so earnings from agriculture for the year can be accurately forecast. In North Korea, nighttime luminosity obtained from satellite data pinpoints the stark divergence in external estimates of rural electrification numbers from official figures.
Economic activity in North Korea is either a purely daytime affair or is not quite as robust as what their government leads us to believe. In Rwanda, cell phone metadata was used to determine concentrations of wealth. Richer people tended to make calls of longer durations at certain times of the day, while less economically fortunate people tended to make calls of shorter duration. All these assessments result in public, economic and political policy prescriptions, none of which are possible without an out-of-the-box analytic approach where unconventional data sources are included in the analyses.
Organizations like Flowminder have pioneered innovative data collection, ingestion and analytics practices to address intractable problems, such as precision epidemiology and understanding the effects of natural disasters on population displacement. The upshot is that the non-availability of good, structured data should be a wakeup call to search for other nontraditional sources that are available but hidden and on which multi-genre analytics can be applied to deliver critical insights that addresses serious socioeconomic challenges.