FinTech growth and the emerging financial regulatory transformation
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Sustainable Growth and Tech Trends
Publication | Update: Oct 2021
Recent technological changes in financial and banking services are seen as potentially disrupting the traditional actors in these markets (banks, asset managers, etc.). These technology shifts are driven by changes in both the demand and supply side. From a market structure and competition point of view, there are different types of players – some driving these changes, some seeing them as a threat and needing to react to them. Traditional financial institutions, in particular banks, need to adapt to a more competitive and fast-moving environment.
FinTech has received a lot of attention in recent years and has attracted increasing amounts of funding. On the supply side, while processing of large amounts of digital information has been central to financial institutions, the recent increasing ability of computing power, partly
through the use of cloud services, represents an important change in the cost but also the scalability of these tasks. On the demand side, the increasing use of new technologies, combined with the growing importance of digital platforms and ecosystems in our daily life, has changed both the profile of customers and their demand. The always online presence and the expectation of immediate outcomes has translated into demands for fast, efficient and costless financial transactions. The most obvious example here is payments, where expectations of seamless and instant transfers of money by clicking on a virtual button of a smartphone app are becoming universal.
There are many areas in the financial sector that are being affected by new technologies, and these areas are linked by significant interactions between them when it comes to both the customers’ side and the technologies being used. These technologies are affecting all financial and banking services. Payment technologies are being disrupted by a variety of new technologies that allow for the seamless execution of payments using mobile devices. Cross-border payments are being disrupted by new players that propose solutions that are much faster and cheaper than the traditional channels.
Digital money and payments have been around for many decades. The most common form of digital money has been the combination of a bank account (a digital record on the bank’s databases) and a payment technology. The payment technology here is usually a credit card or a debit card on the customer side and a point-of-sale terminal on the merchant side. Payment technology was evolving rapidly even before the FinTech revolution. From the original credit card imprinter, we had moved on to the use of the magnetic card, then to PIN codes, and more recently to contactless payment. On the merchant side, the ability to instantly query the customer’s bank for the existence of funds reduced the risk of the payment not going through. More recently we have seen the use of QR codes as an alternative form of contactless payments and biometric authentication as a replacement for the signature or the PIN code used before.
A new technology (Blockchain) has delivered the creation of both a digital asset and a payment technology in an environment that does not require intermediaries but instead runs on a new, decentralized governance structure. An interesting feature of Bitcoin is that it makes the payment technology inseparable from the asset itself, taking us back to a feature of physical money. There is a single database using distributed ledger technology (DLT) of records where the value of balances is kept. Payments require changes in those balances through communication with the database. To this end, electronic wallets can be seen as closed-loop payment systems, and are in that sense similar to Bitcoin. These wallets can be accessed easily and can be used to execute payments or transfers between individuals or companies.
The creation of additional closed-loop networks such as electronic wallets or even cryptocurrencies was initially the easiest way to create a competing payment infrastructure. But to be successful, these networks must meet three criteria. First, there must be an easy technology for individuals and merchants to connect to these networks. The foundation of this technology was created by the Internet and the spread of mobile telephony. Second, these networks need to reach a critical mass. In most cases, from alternative platforms. As our life has become more digital, the Internet, social media platforms and messaging platforms have become the natural networks where payments are developed. Finally, there needs to be an easy way in and out of the network, assuming that the network does not fully dominate the market. The ability of new entrants to make use of parts of the existing network will be fundamental for their success. Also, the creation of efficient and widely available standards will be a strong force behind adoption.
Without regulation that opens up access to those tools, it will be hard for banks or any FinTech entrant to compete with these new technologies. Regulators must ensure that standards remain open and that interoperability is possible so that no single player can become dominant when it comes to payments.
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:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
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.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
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:
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:
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.
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.