Mobile devices are making it easier for people to send money to friends and relatives who desperately need it, but live across international borders.
A new report from Juniper Research says nearly $55 billion in international remittances will be send via mobile devices in 2016. That's up more than fourfold, from $12 billion this year.
The most common transfers are across migration routes like the U.S. and Mexico and intra-regional transfers across Africa and the Middle East, as migrant workers send money back home from foreign countries. But Juniper says inter/intra-regional activity from and within Western Europe will make that region account for the largest remittance volumes by the end of the forecast period.
The report, "Mobile Money Transfer & Remittances: Business Models & Monetisation Opportunities," shows the important impact of the so-called “trickle-down" of smartphone features and functionality into mass-market feature phones. Touchscreens, apps and Internet access are examples.
“In markets with low literacy levels, money transfer applications on the handset based around easily recognizable icons may gain a far wider usage than services based around text-based menus," said the report's author, Dr. Windsor Holden.
The report says service deployments are expected to gain momentum with the increased use of multilateral hubs between MNOs and third parties, which connect both sending and receiving channels on a single platform. This reduces the time to deployment, as each MNO is required to connect to a hub only once to send/receive remittances and does not have to spend additional time on agreements. There is a problem, however. Holden says the lack of communication between regulators and service providers in many countries inhibits deployment and adoption of remittance services.
The Juniper report also finds that service providers should introduce lower commission rates and flexible pricing structures to generate greater service uptake. And potential service users are being deterred in many markets by limited or poorly trained local agent networks.
