Traditional Retail Banking - Rwanda

  • Rwanda
  • In Rwanda, the Traditional Retail Banking market market is expected to witness a significant increase in Net Interest Income.
  • By the year 2024, it is projected to reach US$0.35bn.
  • Looking ahead, this segment is anticipated to display a Compound Annual Growth Rate (CAGR 2024-2029) of 5.62%, culminating in a market volume of US$0.46bn by 2029.
  • However, it is important to note that in a global context, China is expected to generate the highest Net Interest Income, amounting to US$2,426.0bn in 2024.
  • Rwanda's traditional retail banking market is experiencing a shift towards digitalization, with mobile banking services gaining popularity among the unbanked population.

Key regions: France, Brazil, Germany, United Kingdom, United States

 
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Analyst Opinion

One of the key trends shaping the global retail banking market is the shift toward digital channels. With the proliferation of smartphones and other digital devices, consumers are increasingly turning to online and mobile banking platforms for their financial needs. This trend is expected to continue in the coming years, as banks invest in digital technologies to improve customer experience and reduce costs.

Another trend is the growing focus on customer experience. With the rise of digital channels, consumers have come to expect personalized and convenient banking services. As a result, banks are investing in new technologies such as artificial intelligence and machine learning to improve the customer experience. For example, chatbots and virtual assistants can provide customers with instant support and assistance, while personalized recommendations and offers can help banks build stronger relationships with their customers.

The global retail banking market is also characterized by intense competition, with established banks facing increasing competition from fintech startups and other new entrants. These players are leveraging digital technologies to offer innovative and disruptive products and services, such as digital-only banks and mobile payment platforms. As a result, traditional banks are investing in digital transformation initiatives to stay competitive and retain their market share.

Also, the market is affected by regulatory and compliance issues. Banks are subject to a range of regulations, including anti-money laundering (AML) and know your customer (KYC) regulations, which can be complex and costly to comply with. In addition, banks must also comply with data protection and privacy regulations, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Additionally, the peak of inflation in 2022 affected the market. For more details about the impacts of inflation on the financial industry read more here.

Methodology

Data coverage:

Data encompasses B2B and B2C enterprises. Figures are based on Net Interest Income, Bank Account Penetration rate, the value of Deposits, the number of depositors, the value of Loans, the number of borrowers, Credit Card Interest Income, the number of ATMs as well as the number of Bank Branches.

Modeling approach / Market size:

Market sizes are determined by a combined Top-Down and Bottom-Up approach, based on a specific rationale for each market segment. As a basis for evaluating markets, we use data provided by the IMF, World Bank and the annual reports of the top 1000 Banks by asset size. Next we use relevant key market indicators and data from country-specific associations such as GDP, deposit interest rates, lending interest rates or bank account penetration rates. This data helps us to estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the particular market. For example, the S-curve function and exponential trend smoothing are well suited to forecast financial services for digital as well as traditional products and services. The scenario analysis is based on a Monte Carlo simulation approach generating a range of possible outcomes by creating random variations in forecasted data points, based on assumptions about potential fluctuations in future values. By running numerous simulated scenarios, the model provides an estimated distribution of results, allowing for an analysis of likely ranges and confidence intervals around the forecast.

Additional Notes:

The market is updated twice per year in case market dynamics change.

Overview

  • Net Interest Income
  • Analyst Opinion
  • Deposits
  • Loans
  • Credit Card Interest Income
  • ATMs & Bank Branches
  • Methodology
  • Key Market Indicators
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