OTT (Otsustustugi) — AI Decision-Support Tool for Unemployment Service Tailoring

Overview

OTT (Otsustustugi, meaning 'decision support' in Estonian) is a machine-learning-based decision-support tool deployed by the Estonian Unemployment Insurance Fund (EUIF) to assist employment counsellors in configuring and tailoring service packages for unemployed clients. The system was developed through a collaboration between the EUIF, the Centre for IT Impact Studies (CITIS) and the Estonian Centre for Applied Research (ECePS) at the University of Tartu, software implementation partner Nortal, and data warehouse provider Resta. Development began in 2018, with the system integrated into EUIF's IT infrastructure in 2019, pilot testing conducted across five branch offices from mid-2020, and full operational deployment across all EUIF offices achieved by October 2020.

The core function of OTT is to calculate two key predictions for each registered unemployed person: the probability of finding employment within 180 days, and the estimated risk of becoming unemployed again after re-employment. Based on these predictions, the system segments clients into risk categories corresponding to recommended service intensity levels — high, medium, or low support — and suggests appropriate service types such as retraining, reskilling, language courses, digital skills training, or work capacity re-evaluation. The tool also advises on the appropriate service channel, recommending either in-person counselling or digital self-service depending on the client's risk profile and circumstances. This segmentation enables counsellors to prioritise clients who are furthest from the labour market and require the most intensive intervention packages.

Technically, OTT employs supervised machine learning, initially using a Random Forest model which was subsequently switched to gradient-boosted trees in 2022. The model analyses approximately 45 indicators for each unemployed person, drawing on five years of historical data aggregated from multiple national registries via Estonia's X-Road secure data exchange layer. These indicators include employment and unemployment histories, socio-demographic characteristics (age, gender, family status, residence location), education level and qualifications, ICT and digital literacy indicators, benefit receipt history (unemployment insurance, social assistance), risk-group flags (disability, long-term unemployment, youth, persons aged 50+), regional labour market indicators (vacancy rates, sector composition), and prior EUIF service participation and outcomes. Data sources include the Tax Office (salary records), the Board of Education (qualifications), the Social Welfare Board (subsidy information), and the Health Insurance Fund, among others. The model is retrained quarterly to reflect changing labour market conditions, and individual risk scores are refreshed nightly to incorporate new information. The system processes records from over 100,000 clients.

OTT is integrated into EUIF's primary case management system, EMPIS-2, through which counsellors access the tool's recommendations. The system provides counsellors with visibility into which factors most strongly influence each individual's risk score, supporting a degree of interpretability. Crucially, OTT operates in an advisory capacity only: employment counsellors (referred to as 'specialists' within EUIF) retain full decision-making authority over service allocation. Counsellors must review OTT advisory scores, can override recommendations based on professional judgement and qualitative information not captured in administrative data, and are required to record accuracy feedback on whether OTT recommendations were appropriate. This feedback loop enables ongoing model improvement. Management dashboards aggregate risk distributions across the client population for workload planning purposes but do not automate individual service decisions.

Research by Vihalemm et al. (2025) documented the implementation experience of specialists and middle managers at EUIF, finding a mixed reception. While EUIF leadership and some counsellors reported that OTT improved workload planning and helped identify appropriate interventions more quickly, other specialists reported that they seldom used the tool in daily practice, citing difficulties understanding the cause-effect relationships in the model and requesting more comprehensive training. Some counsellors expressed scepticism about the tool's recommendations and found certain features unclear. The study highlighted tensions between the formal obligation to provide feedback on OTT scores and specialists' actual engagement with the system.

The Nortal case study reports a 95% forecast accuracy rate and notes that reducing unemployment by a single day could save 3.8% of Estonia's yearly unemployment expenditures. In 2021, OTT was awarded 'The Best Data-Based Digital Service' in the Estonian public sector. Feedback statistics indicate that 93% of welfare specialists consider OTT scoring to be accurate. The system has been recognised internationally as a public-sector AI exemplar through platforms including the ITU AI for Good initiative and e-Estonia briefings. However, no published causal impact study has quantified employment outcomes directly attributable to OTT.

OTT operates under the EU General Data Protection Regulation (GDPR) framework and Estonian national data protection law, including the Estonian Administrative Procedure Act requirements for reasoned decisions. The Unemployment Insurance Act authorises EUIF's use of automated decision-making processes. The system does not make automated eligibility, payment, or sanctioning decisions — all such decisions require human counsellor approval. A 2021 maintenance contract with CITIS at the University of Tartu provides for ongoing model development and retraining. The EUIF has also explored extending predictive capabilities to identify employed persons at risk of future unemployment, with pilot plans for preventive services targeting the currently employed population.

The system runs on Estonian government infrastructure, with data retrieved from national registries via X-Road. There is no evidence of external or non-sovereign hosting arrangements. The development involved a mix of in-house EUIF capacity and third-party partners (University of Tartu for model development, Nortal for software integration, and Resta for data warehousing).

Classification

Programme Details

AI-enabled decision-support tool within the Estonian Unemployment Insurance Fund that uses supervised machine learning to predict employment probabilities and unemployment recurrence risk, segment clients by risk level, and recommend tailored service packages to employment counsellors.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-005-EST-003 AI-FORA (n.d.). 'Country Case Study Estonia.' Mainz: Johannes Gutenberg University Mainz. Available at: https://www.ai-fora.de/estonia/
   https://www.ai-fora.de/estonia/
2. SRC-003-EST-003 AlgorithmWatch (2020). 'Estonia — Automating Society Report 2020.' Berlin: AlgorithmWatch. Available at: https://automatingsociety.algorithmwatch.org/report2020/estonia/
   https://automatingsociety.algorithmwatch.org/report2020/estonia/
3. SRC-006-EST-003 Bushell, J. (2021). 'How Estonia is using AI to tackle unemployment.' Tech Monitor. Available at: https://techmonitor.ai/technology/ai-and-automation/how-estonia-using-ai-tackle-unemployment
   https://techmonitor.ai/technology/ai-and-automation/how-estonia-using-ai-tackle-unemployment
4. SRC-002-EST-003 Nortal (2022). 'Estonian Unemployment Insurance Fund Prevents Unemployment with Artificial Intelligence.' Tallinn: Nortal AS. Available at: https://nortal.com/insights/estonian-unemployment-insurance-fund-prevents-unemployment-with-artificial-intelligence
   https://nortal.com/insights/estonian-unemployment-insurance-fund-prevents-unemployment-with-artificial-intelligence
5. SRC-001-EST-003 Vihalemm, T., Manniste, M., Trumm, A. and Solvak, M. (2025). 'Specialists and Algorithms: Implementation of AI in the Delivery of Unemployment Services in Estonia.' In: Participatory AI in Public Social Services. Cham: Springer. Available at: https://link.springer.com/chapter/10.1007/978-3-031-71678-2_5
   https://link.springer.com/chapter/10.1007/978-3-031-71678-2_5
6. SRC-004-EST-003 e-Estonia (2021). 'AI to Help Serve the Estonian Unemployed.' Tallinn: e-Estonia Briefing Centre. Available at: https://e-estonia.com/ai-to-help-serve-the-estonian-unemployed/
   https://e-estonia.com/ai-to-help-serve-the-estonian-unemployed/