A new report from Juniper Research has found that nearly $55 billion in international remittances will be enabled via mobile devices in 2016, up from less than $12 billion this year.
Growth is currently being led by mobile remittances sent across established migration corridors such as the US-Mexico and intra-regional transfers across Africa and the Middle East, as migrant workers send money back home from foreign countries. However, substantial inter/intra-regional activity from and within Western Europe will see this region account for the largest remittance volumes by the end of the forecast period.
The report – Mobile Money Transfer & Remittances: Business Models & Monetisation Opportunities – also highlights the opportunity presented in the medium term by the “trickle-down” of smartphone features and functionality into mass market feature phones, such as touchscreen interfaces, apps and Internet access.
According to report author Dr Windsor Holden, “In markets with low literacy levels, money transfer applications on the handset based around easily recognisable icons may gain a far wider usage than services based around text-based menus.”
According to the report, service deployments are expected to gain impetus with the increased utilisation of multilateral hubs between MNOs and third parties, which connect both sending and receiving channels on a single platform. This in turn 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.
However, the report cautioned that the lack of regulatory engagement with service providers in many jurisdictions continues to inhibit service deployment and adoption of both domestic and international remittance services.
Other findings from the report include that service providers should introduce lower commission rates and flexible pricing structures to generate greater service uptake.Potential service users are being deterred in many markets by limited or poorly trained local agent networks.