PERC, a “think and do tank” advancing financial inclusion through information services, has been effective in addressing credit invisibility by advocating the use of alternative data in credit reporting, including in Australia, Brazil, China, Kenya, and the U.S. We invited Michael Turner, PERC’s CEO, to submit an opinion piece, and are publishing the results in a three-part series. Part one can be found here; the following is part two.

While the jury may be out on M-Shwari (see here), the verdict is in on M-Pesa. M-Pesa offers real value to an estimated 14 million disenfranchised and financially excluded Kenyans. Indeed, for many lower-income Kenyans, M-Pesa is not only a payments service, but also a form of insurance. Think of it like an online strategy game. You donate units to members of your group in the belief that they will reciprocate when you request. This same norm operates in Kenya with M-Pesa users, who send spare shillings to friends and family every opportunity they get with the operating belief that if there is ever a need (say their tire pops and they need to pay for a repair) they can send out a request for funds to members of their group and have confidence that their needs will be met. This is a great contribution for a product that former Safaricom CEO Michael Joseph called “a gadget” to make phone service stickier.

Another unintended contribution stemming from M-Pesa is the gradual building of a non-financial payment transactions database at Safaricom. Practice and research from around the world proves that this data is highly predictive of consumer and small business credit risk. The collection and use of this data could be an extremely useful tool to drive meaningful financial inclusion in Kenya. Safaricom Financial Services fully realizes this, and like so many other mobile network operators around the world, moved to limit access to this data to themselves and their bank partners.

I attended the Center for Financial Inclusion’s FI2020 Global Forum in London in the Fall of 2013. Leading up to the event, I had the privilege of serving on their working group on credit information sharing along with an august group of subject matter experts. The group came up with a reasonable assessment of the state of credit information sharing in most emerging markets, recommendations for advancing the industry, and steps that governments, multi-lateral development agencies, and NGOs could take to facilitate the FI2020 objective of substantial global poverty reduction through prodigious gains in financial inclusion.

At the event, I participated in the break-out session on credit information sharing. There, I silently listened as executives from MNOs and payments systems all chimed in about how they were sitting on a gold mine of data, and that if they could figure out how to monetize it surely it would result in a revolution for financial services around the world. This is partly true—there is value in this data and it can be monetized—but all data assets are not created equal. Certain types of proven predictive data from MNOs, landlords, and energy utility firms are valuable, and can and should be sold by the data custodians at a price set by the market. In most cases, this price is unlikely to be anywhere near what the data guru consultants may have led executives at MNOs and other firms to believe.

I am not kidding about the pervasiveness of this belief among prospective non-financial data furnishers. One executive described his data asset as “rich,” and containing “predictive” credit payment data on millions of people worldwide. He was over the top when discussing the potential worth of this asset. I asked him what credit data he was referring to. He said that his firm granted loans to MNO subscribers when their prepaid credit was about to expire or was past the limit. Turns out the vast majority of these “loans” were for pennies, nickels, and dimes.

Encouraged by the euphoria that comes with innovations that have initial take-offs and by reinforcing messages from global consultancies, many large firms, but especially MNOs, have a grossly inflated sense of the value of their customer data. And some MNOs think that they should be banks. While some may ultimately succeed in becoming banks, none have yet received a banking license. There are serious questions about the wisdom of tethering access to finance to a telecoms monopoly or oligopoly—especially ones that are grossly underperforming in their core businesses, including network build-out and service reliability. More problematic still are data fiefdoms—whereby data base owners restrict access to their data (largely to foreclose competition as in Mexico) or price access so high that it becomes severely restricted. This is an emerging trend in advanced and developing countries alike, ironically fueled by the current obsession with big data.

Outlooks and advice that create data fiefdoms are likely to retard the speed and scale at which digital financial services drive financial inclusion, and perhaps more importantly, keep the data from being used to provide options such as loans to the financially excluded. This data could potentially underpin new products and services, helping to enable a financially inclusive system that addresses hurdles to alleviating poverty and building assets. How this can happen is a matter for another day.

Have you read?

New Research Highlights Increasing Use of Alternative Data in Credit Reporting

Financial Inclusion Trends and Innovators – 2015

The Great Equalizer: How Advances in “Big Data” Allow Tech-Savvy Start-Ups to Compete with the Major Players in East Africa