Contactless Payments & the Omni-directional Flow of Value

Contactless Payments & the Omni-directional Flow of Value

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Economic Growth
  • Competitive Differentiation
  • Digital Payments

Publication | Update: Sep 2020

The payments industry was built to handle large transactions, with banks developing infrastructure, policies, and revenue models to support batch processing. Over time, transaction sizes began to shrink while volumes rose, forcing payment providers to manage more transactions on thinner margins.

According to Agustín Carstens, General Manager of the BIS, information technology is permeating every corner of financial services, whether through the entry of so-called "big tech" firms or the use of mobile payment applications, disrupting existing business models and challenging traditional financial infrastructures.

These game-changing developments require central banks to step up and play a more significant part in improving payment security and efficiency. The Bank for International Settlements (BIS) is taking a leading role in driving these efforts, spearheaded by the establishment of the BIS Innovation Hub across international locations with a mandate to develop insights into critical financial technology trends, to engineer public goods to enhance the global financial system and to act as a focal point on change for central banks, while ensuring the stability of the global financial and monetary system.

According to Vanessa Colella, Citi’s Chief Innovation Officer & Head of Citi Ventures, a global explosion of real-time payments and the prospect of machine-to-machine (M2M) transactions via the Internet of Things (IoT) is causing a state change in the space. Rather than executing individual transactions of any size, payment providers will soon oversee a continuous, omni-directional flow of value.

This revolution in payments -driven by changes in consumer behavior, technology, and industry practices- is being led by mobile transactions. Using payment gateways from Venmo to Zelle to WeChat Pay, consumers around the world are buying everything, and industry experts expect transaction volumes to skyrocket to an estimated 40 trillion annually in 2020.

Consumers have long valued convenience and flexibility in payments. Companies such as Amazon and Uber responded to this customer demand for payment processes into one click. Other mobile payment players followed suit, setting an expectation of seamless, real-time transactions that is unlikely to slow down or change course. 

The next big area of change is the use of mobile wallets for business expenses.

Many have already embraced mobile wallet solutions on the consumer side, and companies are following closely behind. According to David Voss, head of Commercial Cards, Global Transaction Services EMEA, at Bank of America: “Mobile devices have the potential to replace the physical payment card in the coming years.” Moreover, payment via mobile wallet also adds security to the list of gains from technological development. Beyond the ease of not needing a physical card, they entail great possibilities when it comes to authentication and tokenization. Using biometric data like fingerprints or facial recognition, payments can integrate with, and enable better security in, the eCommerce environment. In addition, mobile wallet transactions do not have a contactless limit and offer increased security over traditional card payments by using a tokenized account number, meaning the actual account number is never stored within the mobile device nor presented to a merchant to complete the transaction. Currently the highest usage country is China at 36%, more than 6 times the nearest rivals but the global adoption is expected to continue.

How can companies adapt their infrastructure, policies, and business models to the changing nature of transactions?

One way is to make payment processes ‘smarter,’ encoding multilateral transactions that encompass several complex steps, rules, and interdependencies all at once. Daimler Chrysler did just that, leveraging blockchain technology to devise a scalable, repeatable bond issuance process, facilitating efficient transactions.

The shift in payments to smart provides a wealth of opportunities for payment providers to enable seamless transactions and offer more-integrated customer experiences.

Contactless Transactions

Contactless payment methods rely on embedded near-field communication (NFC) sensors in a card or smart device, which send signals directly to POS hardware, avoiding any need to make direct contact with surfaces in a specific shop, or pass cash notes to an employee -- greatly reducing the risk of spreading contaminants. Each individual transaction creates a unique code, each of which is protected by layers of authentication, monitoring and data encryption to keep an individual’s finances safe – instilling trust in the consumer and the retailer the transaction is safe and secure.

Amid concerns about COVID-19, many countries are already moving towards a digital payment ecosystem and away from cash payments. In some respects, the pandemic has accelerated trends that were already under way. Both contactless payments, and remote payments for online transactions, were rising before the pandemic, but the pandemic has given them further impetus.


  • Mastercard Consumer Research suggests contactless transactions growing at a rate of 15 x over the last 12 months.
  • According to ABI Research’s Assessing the Impact of COVID-19 on the Smart Card and Secure ICs Market application analysis report, 110 million contactless payment cards expected to be issued in 2020, when compared to pre-COVID-19 forecast expectations.
  • Overall usage of contactless payments in America has risen 150% since March 2019.
  • Contactless transaction limit is increasing across 49 countries, ranging from 25% to 400%, with an average of 131%.
  • 451 Research’s data demonstrates that cardholders in the 18-24- and 25-44-year-old age brackets show the highest propensity to begin using a contactless card.

According to Mastercard Global Transaction Data and Consumer Research:

(Online interviews with 17.000 consumers in 19 countries worldwide, conducted in 2020.)

  • %79 of respondents worldwide say they are now using contactless payments.
  • %46 of respondents moved contactless cards to the top of the wallet.
  • %82 of respondents view contactless as the cleaner way to pay.
  • %74 of respondents state they will continue to use contactless payment post-pandemic.

Benefits of Contactless Payments

According to Linga research, the pandemic has driven a significant shift in customer purchasing behaviors. Contactless payments offer a variety of innovative solutions, speed and security to its users even as it shapes the future of payment technology.

Contactless technology provides numerous benefits for customers for a seamless money transaction.

  • Safe, hygienic and socially distant transactions: Now that hygiene is the number one concern on everyone’s minds, the first thing businesses should do is take measures to protect its customers and staff, thus helping to prevent the spread of viruses and other infections.
  • More speed for customers and restaurants: Contactless payment technology enables shorter lines and reduced wait time and money spent on cash-handling. This allows restaurant operators to serve their customers faster and offer a much better customer experience.
  • Ease-of-use: Contactless payment options offer an easy-to-use alternative to other payment methods such as cash, checks, swipe-cards, inserted cards.
  • Data Security: Contactless NFC technology, which has the highest level of encryption and offers the most secure way of paying to protect customer information, is also called “effective at reducing fraud” by the U.S. Payments Forum.
  • Profitability: One of the key benefits of contactless technology is that it can help businesses boost revenue and profitability. While it lowers cash-handling costs, it also increases customer loyalty and customer satisfaction by maintaining faster service.
  • Environmentally friendly: Cashless technology offers a greener and more sustainable life by moving the transactions to a paperless digital world.



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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology


We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

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  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

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  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

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  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

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  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

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Global Business Reviews, Research Papers, Commentary & Strategy Reports

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M&A and Risk Management | Regulation

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.


Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.


Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.


The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.