Market Insights Financial Wealth Management Digital Investment

Robo-Advisors - Sierra Leone

Sierra Leone

The projected assets under management in the Robo-Advisors market for Sierra Leone are expected to reach US$79.95m by 2024.
This market segment is also projected to show an annual growth rate (CAGR 2024-2027) of 13.34%, resulting in a total projected amount of US$116.40m by 2027.
Furthermore, the number of users in Sierra Leone's Robo-Advisors market is expected to amount to 12.360k users by 2027.
In 2024, the average assets under management per user in Sierra Leone's Robo-Advisors market is estimated to be US$7.78k.
When comparing in Sierra Leone to other countries, it is worth noting that United States has the highest assets under management in the Robo-Advisors market, with a projected amount of US$1,459,000.00m in 2024.
Despite the growing popularity of robo-advisors globally, Sierra Leone's financial market remains largely untapped by this technology.

Key regions: Asia, Canada, Hong Kong, Singapore, Germany

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Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on transaction values / revenues / assets under management and user data of relevant services and products offered within the FinTech market.

Modeling approach / Market size:

Market sizes are determined through a combined top-down and bottom-up approach, building on a specific rationale for each market segment. As a basis for evaluating markets, we use annual financial reports of key players, industry reports, third-party reports, publicly available databases, and survey results from primary research (e.g., the Statista Global Consumer Survey). In addition, we use relevant key market indicators and data from country-specific associations, such as GDP, consumer spending, population, internet penetration, smartphone penetration, credit card penetration, and online banking penetration. This data helps us 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 relevant market. For example, the S-curve function and exponential trend smoothing are well suited for forecasting digital products and services due to the non-linear growth of technology adoption.

Additional notes:

The market is updated twice a year in case market dynamics change. The impact of the COVID-19 pandemic and the Russia-Ukraine war is considered at a country-specific level.