Market Insights Financial Wealth Management Digital Investment

Robo-Advisors - Uzbekistan

Uzbekistan

In 2024, the projected assets under management in the Robo-Advisors market in Uzbekistan are expected to reach US$720.40m.
This market segment is anticipated to experience an annual growth rate of 20.83% from 2024 to 2027, resulting in a projected total amount of US$1,271.00m by 2027.
By that year, the number of users in the Robo-Advisors market is expected to reach 57.680k users.
The average assets under management per user in this market segment is projected to amount to US$13.26k in 2024.
When compared globally, it is worth noting that United States currently holds the highest assets under management in the Robo-Advisors market, with a value of US$1,459,000.00m projected for 2024.
"The adoption of robo-advisors in Uzbekistan is on the rise, as investors seek efficient and automated investment solutions in the country's emerging financial market."

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

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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.