Robo-Advisors - Kazakhstan

Kazakhstan

Highlights

Assets under management in the Robo-Advisors market are projected to reach US$1.26bn in 2024.
Assets under management are expected to show an annual growth rate (CAGR 2024-2028) of 7.16% resulting in a projected total amount of US$1.66bn by 2028.
In the Robo-Advisors market, the number of users is expected to amount to 30.790k users by 2028.
The average assets under management per user in the Robo-Advisors market is expected to amount to US$43.64k in 2024.
From a global comparison perspective it is shown that the highest assets under management is reached United States (US$1.46tn in 2024).

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Currently, the robo-advisor market is experiencing significant growth, with an increasing number of consumers turning to automated investment advice platforms. One trend that has emerged in the market is the integration of artificial intelligence and machine learning technologies, which enable robo-advisors to provide more personalized investment recommendations to clients. Another trend is the expansion of robo-advisory services to encompass a broader range of financial services, such as retirement planning, tax optimization, and debt management.
Several factors are driving the growth of the robo-advisor market. One key factor is the increasing demand for low-cost investment advice, as traditional financial advisors can be expensive and often require high minimum investment amounts. Another factor is the rise of tech-savvy millennials, who prefer digital platforms for financial services. Additionally, the COVID-19 pandemic has accelerated the shift to online financial services, including robo-advisors, as more consumers have turned to digital channels for their financial needs.
The robo-advisor market is expected to continue to grow in the coming years. This growth is expected to be driven by factors such as increasing demand for low-cost investment management solutions, the rising popularity of passive investing, and the continued development of artificial intelligence and machine learning technologies.

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.