Econ_May_05

The Economist

May 5th 2018

FINANCIAL INCLUSION

SPECIAL REPORT

Part of the problem is that microfinance is very hard to pro-

vide on a large scale. Reaching, assessing and helping borrowers

like Ms Parveen is time-consuming and labour-intensive, which

makes it hard to keep interest rates at a reasonable level. Typical

annualised percentage interest rates are in the region of 20-40%,

cheaper than the traditional local moneylender or pawnbroker

but hardlya snip. Digitalmoneyholds out the hope ofimproving

things in two ways: bymaking it cheaper and faster to grant, dis-

burse and repay loans and to provide other financial services,

notably savings and insurance; and by harvesting data that

should widen access to financial services for those with little or

no history in the formal financial sector.

In Kenya, for example, Safaricom in

2012 launched

-Shwari, a paperless

bank account offered by the Commercial

Bank of Africa (

CBA

) via

M-PESA

CBA

takes the risk but can use the know-your-

customer checks alreadydone digitallyby

M-PESA

to open the account, and the

M-

PESA

payment history to gauge creditworthiness. Like

M-PESA

it-

self, it has grown like Topsy (

CBA

’s customer base increased from

50,000 in 2010 to 22m today) and has beenmuch imitated across

Africa and beyond. In Pakistan,

FINCA

, the global microfinance

network, wants to use SimSim, its new mobile-money account,

to offer “nano loans” (the equivalent of $5 or $10, say), thereby es-

tablishing a data trail for assessing bigger loans later.

-Shwari and a few of its peers also offer services that pay

interest on mobile-money accounts in credit. Indeed, the num-

ber of financial services available to poor people with a mobile-

money account is exploding. Michael Schlein of Accion, a Mas-

sachusetts-based financial-inclusion non-profit, speaks of “a

golden age of fintech”. Take life insurance. In Ghana,

MTN

, a mo-

bile-network operator, offers a life-insurance product called Mi-

Life linked to its mobile-money accounts. For about $0.23 a

month users get cover of around $100. This is catching on across

the developing world. In March Telefónica, a Spanish multina-

tional network operator, announced a tie-up with Bima, a pro-

vider of mobile micro-insurance, to offer life insurance across

Latin America, starting in Nicaragua. Crop and livestock insur-

ance is also becoming available onmobile phones. Anumber of

firms, such as Econet in Zimbabwe andAcre Africa in east Africa,

offer farmers “index insurance” for their crops that will pay out

automatically to a mobile-phone account, without the need to

put in a claim, if, say, a rainfall index drops belowa certain level.

Ingenious pay-as-you-go schemes offer credit for pur-

chases. The most famous is

-Kopa’s solar-panel technology,

which has brought electricity to hundreds of thousands of

homes in Kenya, Tanzania andUganda. Buyers put down a small

deposit and thenmake a daily payment from their mobile-mon-

ey account until, after a year, they own the panel. If they miss a

payment the panel is automatically locked, so if they urgently

needmoney for something else, they have the choice of forgoing

a day’s electricity to give themextra cash in hand. In February

Kopa announced a partnership with MasterCard to help it ex-

pand through Africa, using the card firm’s

technology. Again,

good east African ideas travel: Easypaisa in Pakistan, for exam-

ple, now has a similar offering. SimSim would like to use the

model to finance smartphone purchases, but the technology to

lock the devices remotely is not yet robust enough to rebuff at-

tempts to outwit it.

The data generated by such accounts provide the nearest

thing many of the holders have to a credit score. They are an in-

valuable aid to lenders trying to decide if a borrower can afford a

loan. But a phone—and especially a smartphone—also provides

all sorts of other information that some lenders may find useful

for marketing or credit-assessment services. Positional data, for

example, can show if someone has a steady job and a perma-

nent address. Social-media activity can be highly informative.

And shopping data can let on, say, if the user is pregnant.

Some firms specifically try to generate credit judgments in

the absence of a conventional financial history. Lenddo

EFL

, a

merger of two fintech startups, claims to have facilitated more

than 7m assessments, allowing 50 financial institutions of all

sizes to lend more than $2bn to people with limited borrowing

histories. Lenddo relies on advanced

-driven analytics.

EFL

provides “psychometric testing”—online quizzes that have a sur-

prisingly good record in predicting a prospective borrower’s pro-

pensity to repay. Questions might be about how you are feeling;

your viewof the time value ofmoney (“Would you take $10,000

now, or $20,000 in sixmonths’ time? Howabout $17,000now?”);

how you spend your money; what you would do with a wind-

fall; and how you view your community. If the questions seem

easy to game, that is part of the point: the way that defaulters

game it goes into the data. The algorithmwill always be one step

ahead. Lara Zibarras, a seniorpsychology lecturer at City, Univer-

sityofLondon, isworkingon another set ofpsychometric tests to

be introduced by Oakam, a British subprime lender. They ask

people to choose between photos to reveal personality traits.

Early tests suggest they are as accurate in predicting missed first

payments as an experienced human loan-underwriter.

Digital money should make it cheaper and faster to

grant and repay loans, and widen access to financial

services for those without a formal credit history

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