Smart Finance - Lithuania

The finance industry has been using an early version of the Internet of Things in the form of automated teller machines (ATMs) for decades, but the applications have since been increasing. Point of sale (POS) have been connected to the internet protocol (IP) for a while, and mobile POS devices are widespread and available virtually everywhere.

Utilizing IoT technologies for risk management has been on the rise as financial institutions and insurance companies can make more informed decisions based on the data collected via the IoT devices. With the rise of transactions being performed via connected devices, the provided data gives a better insight into how the customers buy and spend their money. The demand is being fueled by the need for faster and real-time data that the devices are constantly collecting, which results in more personalized customer experience.

With the development of edge computing, analyzing and monitoring large amounts of business operations data makes cost and performance decision-making a faster process. Companies can gain big advantage with this by setting up predictive and prescriptive analytics which have possibility to result in both time and cost savings.

Data coverage

The data encompasses B2B, B2C and B2G revenues. The revenue only refers to the spending share of the Internet of Things components.

Modeling approach/ Market size:

The market size is determined through a combination of top-down and bottom-up approaches. We use annual financial reports of the market-leading companies and industry associations, as well as third-party studies and reports to analyze the markets. To estimate the segment size for each country individually, we use relevant key market indicators and data from country-specific industry associations, such as consumer spending, internet penetration, 4G coverage, and current and historical developments. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques but primarily exponential smoothing. The selection of forecasting techniques is based on the behavior of the relevant market. The main drivers are the GDP and the level of digitization.

Additional notes:

The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.