While domestic wire transfers between two U.S. banks typically take less than 24 hours, international trades can take between three to five days.¹ These delays in the receipt of payments can increase liquidity costs, borrowing costs, and extend the working capital cycle, potentially limiting clients’ ability to effectively manage their money.

The lengthy settlement time is due, in part, to needing to abide by the laws and regulations of multiple countries: that of the sending country, the beneficiaries’ country, and the home country of any banks involved, should those be distinct. As each country has its own requirements, each step adds processing time and cost.

These costs and others are not transparent, however. While there are stated transaction fees, transfers can also include a variety of hidden fees and pricing models, including FX rates, which are not stated upfront. All of this means that the resultant payment to beneficiaries may be significantly different than what was expected or originally remitted. These uncertainties can also cause difficulties in reconciliation and increase the need for investigations, further adding to costs and negatively impacting the customer experience.

Increasingly, companies are promising faster payments through account-to-account models. This next-gen model can reduce a multitude of fees imposed by the correspondent banks, improve the speed and predictability of settlement, and provide robust payment data. The growing adoption of real-time settlement is also increasing the opportunity to achieve straight-through processing.

Looking ahead, the opportunity for a centralized, permissioned network, where all participants are known, and which allows secure payments to be processed directly, could help further reduce costs and increase data access.

Download our white paper to learn how innovative new cross-border payment models can offer businesses, and the banks supporting them, a range of new choices when it comes to transacting globally.

1. Juniper Research, B2B Money Transfer – Deep Dive Data and Forecasting, accessed May 2019