09/12/2025
A survey by Finantsinspektsioon has found that most of the financial institutions in Estonia are already using AI and that its importance in providing support for work processes, client service and risk management is increasing rapidly.
Finantsinspektsioon ran a questionnaire in the first half of 2025 asking financial companies to estimate their current use of AI and its role in the Estonian financial sector. There were 65 companies under financial supervision that responded to the survey, which is enough to give a good picture of the technological level and readiness of the whole sector. Most of the respondents had already looked at AI, and a lot of solutions are at least in the testing phase. A notable proportion of the companies are actively using AI, while those that are keeping clear of it are the exception. The most common uses are unsurprisingly language processing, machine learning models and automation of processes, and they are mainly encountered in data analysis and client service.
Member of the Finantsinspektsioon management board Siim Tammer said that artificial intelligence is one of the main trends of development in the financial sector.
“The widespread use of AI shows that the Estonian financial sector is ready to take innovation on board. Artificial intelligence will probably become one of the key factors in the coming years for competitiveness in the sector and for the quality of services”, he said.
He also stressed the need for robust risk management practice to be applied. “Artificial intelligence will create lots of new opportunities in the financial sector, but those opportunities will bring great responsibility with them. Companies will have to pay careful attention to the quality of their models and their risk management and make sure that it is not possible for technological error or lack of transparency to impact clients or the credibility of the market”, he added.
The advantages of AI that companies responding to the survey identified was that it increased efficiency, cut costs and improved the quality of decisions. The main risks they identified were data protection, the reliability of models and cyber security. They said they are strengthening their internal controls, human supervision and data protection measures to minimise the risks.