How Mobile Data Could Enhance Microfinance

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By Titus Mbithi
A report by Ericsson has shown that mobile data analytics could bring microfinance to hundreds of millions of individuals and businesses in need of short-term credit.
The report stated that globally, the microfinance sector reaches less than 20 percent of its potential market, according to the International Finance Corporation. In affluent markets, such as North America and Western Europe, most people have generated a credit history that banks can use to decide whether to lend them money and on what terms. However, in low and middle income markets, there has been a dearth of such data.
A New Data Driven Era
But the growing use of mobile phones, digital services and mobile money accounts is beginning to fill that vacuum. Mobile operators are teaming up with banks, financial tech (fin-tech) companies, and data analytics specialists to use the data they have on customers to gauge their credit risk and offer microfinance products to some who would otherwise lack any proof of their capacity to repay a loan.
Since they know how much consumers are spending on airtime and are able to infer other relevant information, such as whether a subscriber has a job, mobile operators can gauge how affluent an individual is and what size of loan they can afford. If the customer is a regular user of a mobile money transfer service, the operator may also be able to assess how much disposable income they have.
In fact, mobile operators’ data can be good enough to lower the lender’s risk significantly, enabling interest rates to fall and making microfinance a more attractive proposition for small businesses and individuals alike.
In Kenya, mobile operator data appears to be successfully reducing credit risks. Mobile operator Safaricom reported the non-performing loan ratio for its M-Shwari joint venture with the Commercial Bank of Africa down to just 2 percent, compared with an average of 5.5 percent for Kenya as a whole.
Although the sums involved are small right now, the market for data-driven microfinance is growing quickly. M-Shwari had 2.1 billion Kenyan shillings (almost $20 million) out on loan to customers as of March 31, 2015, up 75 percent from 1.2 billion shillings a year earlier. A younger, but similar joint venture between Safaricom and KCB Bank had 950 million shillings ($8.5 million) of loans on its books at the end of the first quarter of 2015.
Safaricom and KCB use an individual’s M-Pesa transaction activity to determine how much they can borrow (the minimum loan amount is about $5 and the maximum loan amount is about $9,000).
Scoring Customers for Credit
Ericsson pointed out that as post-paid mobile contracts are effectively a form of credit; some operators have already developed sophisticated risk management systems. For example, Oslo-based Telenor Group, which has operations in Europe and Asia, has built predictive credit-scoring models in-house to enable it to offer emergency airtime top ups and handset financing, as well as mobile money loans.
Telenor also uses these credit score cards to increase credit limits for post-paid customers, or to convert a prepaid customer to a post-paid customer. In view of this, Mr. Catrin Bekker Dahle, senior analyst, Telenorb Financial Services, said “the frequency of top ups can give indicators of income level, while location data could reveal job stability, how long a customer have had their phone, and how often they change, can provide insight to the likelihood of default.’
As their mobile money services generate more transaction data, some mobile operators are looking to team up with financial services specialists. At the Mobile World Congress 2015 in Barcelona, Voyager Innovation, a digital financial and commerce unit of Smart Communications of the Philippines, announced a strategic partnership with Cash Credit, a Sofia-based fin-tech company, to roll out a mobile-based microfinance programme.
The two companies plan to apply big data analytics to offer microcredit loans, airtime top-up credit, utility bill payments and credit for airtime resellers to Smart’s subscriber base of more than 75 million people in the Philippines. Cash Credit, which has a similar partnership with mobile operators in Bulgaria, says its decision-making model for credit scoring harnesses subscriber call and texting usage, payment and behavioural data.
New era, new competition
In some segments of the microfinance market, mobile operators and banks will face competition from Internet players running online marketplaces. In China, for example, Alibaba, and its affiliate Alipay, offers loans to small businesses that sell on its marketplaces. Again, this is a big data play:
Alibaba uses real-time records of borrowers’ cash flows and counterparties to aid lending decisions. In India, CapitalFloat is partnering with digital marketplaces, including Amazon and eBay India, to both access small merchant vendors. IndiaLends of New Dehli is an online marketplace that connects consumers looking for low-rate loans with institutional lenders. IndiaLends says it uses a combination of analytics and judgmental oversight to screen potential borrowers, drawing on a large number of data sources, such as a credit bureau, the borrower’s application form and social networks.
Better data analytics in developed markets
There are also opportunities to expand the financial services market in middle-income markets and developed markets. In the U.S., for example, there are almost ten million households without bank accounts, 11 while millions more are under banked, in that they can’t get access to the financial services they need.
However, a growing number of digital peer-to-peer lending platforms are using data analytics tools to help assess the risk of extending credit to people who don’t have the traditional collateral, such as pay slips or property deeds required of a borrower. Such tools can help distinguish affluent students and successful self-employed workers from people with few employment prospects.

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