> Posted by Anita Gardeva, Senior Analyst, CFI

The Financial Inclusion 2020 campaign at the Center for Financial Inclusion at Accion is building a movement toward full financial inclusion by 2020. Accordingly, this blog series will spotlight financial inclusion efforts around the globe, share insights coming out of the creation of a roadmap to full financial inclusion, and highlight findings from research on the “invisible market.”  

“Big data.” This is the buzzword for the use of the unprecedented amounts of information about individuals, their actions, and their preferences that is now becoming available through electronic transactions. This information has the potential to unlock powerful transformations in both the business and policy worlds.

At the Center, many members of our Financial Inclusion 2020 campaign point to the potential of “big data” throughout our consultative process to build a roadmap to inclusion. There is a general consensus that data analytics will change the face of financial inclusion in far-reaching ways. Analysis of big data can improve the operations of financial services providers to expand access, decrease costs, and improve products. It can also result in smarter policies. Below we break out four ways data analytics can play a role in advancing financial inclusion:

1. Financial access for excluded populations

Big data can enable financial services providers to reach client segments that were previously excluded, especially for credit and insurance. Access to alternative information such as utility bills or mobile phone bills can facilitate the creation of credit scores for “thin-file” clients—individuals for whom there is not sufficient information to build a traditional credit score. Cambridge-based Ciginify uses mobile phone usage data to understand and predict client behavior and then translates that into a credit score. Other companies, like Lenddo in the Philippines, are allowing users of social media, such as Facebook and Twitter, to use their “online” reputations to qualify for loans.

Use of data and analytics can also make it possible to insure people who previously could not be insured. AllLife in South Africa is the only insurance provider to offer coverage for HIV-positive individuals. Its model depends on the use of data to change the behavior of clients. AllLife uses medical data to promote adherence to a healthy lifestyle by its HIV-positive insurance clients. If the client has not gone in for his check up or taken his medications, his life insurance will be suspended. The result is a program that educates and encourages HIV-positive individuals to take the steps they need to take to live a longer life, while also making it possible for AllLife’s insurance model to succeed.

2. Risk management, increased operational efficiency, and greater affordability

Data analytics can help financial services providers manage risk, improve operations, and reduce costs. Data on behavior can improve risk management by enabling providers to better predict borrower’s willingness to repay (the Entrepreneurial Finance Lab has been looking at doing just this). Data can also help financial services providers better detect fraud (see, for example, Palantir).

Data can also help in establishing the identity of individuals. This is especially important in financial inclusion where the need to comply with Know Your Customer (KYC) requirements generates an important barrier for opening accounts and a cost for financial services providers. Innovations that make it easier for banks to comply with KYC by using public domain data to confirm an individual’s identity (in the absence of a national identification document) can decrease the costs of providing services, such as savings.

3. Improved design and marketing

Big data has the potential to transform the way financial services are designed and marketed. Data mining can unlock insights about what kinds of financial services might be useful for different clients (Grameen’s App Lab). Others use it to more effectively target and market services to relevant client segments. DemystData aggregates public domain data about individuals and then, through statistical analysis, segments clients by different criteria, allowing financial services providers to target their communications and interactions with clients.

4. Financial inclusion policy and strategy

Data is central for strengthening financial inclusion policy. The Alliance for Financial Inclusion, a network of policymakers and regulators from over 80 countries in the global south, has formed a working group on data and measurement that is exploring how countries can better gather financial inclusion data, and more specifically demand-side and usage information, which can support policy, regulation, and national strategies for inclusion. The World Bank’s Financial Inclusion Index (FINDEX), is the first of its kind to provide demand-side data on how people in nearly 150 countries engage with financial services. At the Center we are creating a website that will allow users to explore data that is relevant for financial inclusion, including FINDEX data, demographic data, urbanization trends, and economic data. While these initiatives are not building on “big data,” there is recognition that it can come to play an important role for the public sector as well. Just today, the Guardian’s DataBlog wrote about a recent study that estimated that the public sector in the UK could save between £16 billion and £33 billion a year by “making better use of big data.”

As exciting as all this is, important client protection issues arise with the use of big data—namely around privacy of client information. Where do we draw the line on which information is appropriate for providers to use? How do we ensure that information remains secure and is only used for the intended and approved purposes? These questions need to be addressed as financial sectors begin to use big data to improve operations and services.

For more information on Financial Inclusion 2020, sign up for campaign updates.

Image Credit: Grameen’s App Lab

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