Economic Benefits of Real-Time Payments Remain Largely Untapped
Hungary recorded 166 million real-time transactions in 2021, which resulted in an estimated cost savings of $39 million for businesses and consumers. This in turn – which helped to unlock $153 million of additional economic output, representing 0.08 % of the country’s GDP.
With real-time transactions set to rise to 471 million in 2026, – net savings for consumers and businesses are forecasted to climb to $131 million in 2026. That would, helping to generate an additional $415 million of economic output, equivalent to 0.19% of the country’s forecasted GDP.
Hungary is one of the countries for which real-time payments provide the biggest economic growth opportunities. According to the Cebr, the theoretical impact of all payments being real-time could add 4.4% to formal GDP by 2026. However, these are theoretically modelled modeled benefits; they, this does not suggest that there is no longer place for non-instant electronic payments or paper-based payments.
Azonnali fizetési rendszer (AFR) is Hungary’s domestic real-time credit transfer scheme launched in March 2020 in compliance with SCT Inst standards. Despite being less than two years old, the share of real-time payments of the total payments transaction volume stood at 4.2% in 2021. This can be attributed to AFR supporting different kinds of payments, including individual P2P, bulk, and recurring payments, and proving the convenience of making payments using mobile phone numbers, email addresses or tax identification numbers.
The central bank requires all Hungarian banks to offer real-time payments via AFR, which also supported this growth. The rally in both real-time payments volume and value will continue at the expense of cash, registering respective CAGRs of 23.1% and 35.7% from 2021-2026. The increased preference for electronic payments amid COVID-19 will also support this trend.
Real-Time Payment Types
Year of Real-Time
A proactive government continues to drive growth in the Hungarian real-time payments market. Participation in the HCT Inst scheme (branded as AFR and based on SCT Inst) has been mandated since 2020 and payments under the value of HUF 10m must execute within five seconds. At the start of 2021, a further mandate compelled all brick-and-mortar stores to accept electronic payments.
Still, while adoption is healthy, a critical mass is yet to be achieved, meaning real-time payments are not yet generating significant revenues for most players. However, it is encouraging that banks remain willing to invest in new use cases. Confident that the demand is there to be tapped, they are putting the infrastructure in place to capitalize on increased demand. Take OTP Bank, for example. The largest card payments provider with a 50% footprint, OTP has launched a real-time payments solution featuring a digital wallet that enables customers to pay merchants who also have OTP accounts.
We anticipate that a big enabler of increased adoption will be the consolidation of the currently fragmented API landscape, in which there is no common standard shared by the banks. One solution looking to plug this gap is Mastercard’s Open Banking Connect — of which most banks are part — which provides third parties with a universal connection to financial institutions’ open banking functionality.
In the near term, then, this dramatic lowering of the barriers to entry should prompt a renewed influx of fintechs bringing new services to market. Banks should ready themselves for increased competition as a result. Across the board, payment modernization roadmaps need to move beyond the previously noted minimum-level mandates to added-value services. Among the options available is Request to Pay, which is supported by AFR today, but only three banks currently support receiving these payments (OTP, Erste Bank and Raiffesen). And there remains ample scope for increased innovation in the user experience around proxy look-ups through phone numbers and email addresses, among other identifiers.
Real-Time Total Participants
All Domestic Banks
Population Banking Level
Number of debit, credit and
charge cards per adult
Index to global average
F5 Yr CAGR
Share of Volumes by Payments Instrument
- Paper-based payments
- Electronic payments
- Real-time payments
Real-Time Payments Volume and Its Share in Overall Non-Paper-Based Transactions, 2015-26f
Hungary, a high-income country, ranked as the 56th largest global economy in 2021 (Cebr World Economic League Table, 2022).
With its current share of real-time adoption, Hungarian consumers and businesses gained estimated net efficiency savings of $39 million in 2021, which was predominantly driven by a reduction in the costs associated with failed transactions. In Hungary, we estimate the total cost of failed transactions to be $329 million in 2021. However, through reducing the probability of failure, real-time payments saved stakeholders from an additional $14.4 million of payment failure costs.
In 2021, economy-wide efficiency gains were estimated to facilitate $153 million of economic output (0.08% of formal Hungarian GDP). The country has a relatively young real-time infrastructure with the first scheme launching in 2020, but by 2026, the share of volumes for real-time payments is estimated to more than double to 9.7%.
This robust real-time uptake will result in business and consumer level benefits reaching $131 million in 2026. This is forecasted to facilitate 0.19% of formal GDP in 2026. Compared to other European countries in relative terms, the scale of this impact is above average and is equivalent to $415 million of economic output annually.
For Businesses and Consumers
Net savings stimulated by real-time payments
Projected net savings stimulated by real-time payments
of economic output
of GDP facilitated by real-time payments
Projected of economic output
of GDP facilitated by real-time payments
Choose a Country
The Need for Speed to Market in Consumer Payments - Payments modernization as a response to customer demand
Fraud Management Insights
Expanding the Horizons of Fraud Detection - The Network Intelligence Approach to Machine Learning