The Försäkringskassan ML Risk Scoring system is a machine-learning-based risk profiling tool deployed by Sweden's Social Insurance Agency (Försäkringskassan) since 2013 to assign risk scores to social security benefit applicants and automatically flag high-scoring individuals for fraud investigations. The system was used primarily in the context of tillfällig föräldrapenning (temporary parental benefit), known colloquially as VAB (vård av barn), which provides income replacement at approximately 80 percent of estimated earnings to parents who stay home from work to care for a sick child (Lighthouse Reports, 2024; Amnesty International, 2024). Applicants assigned risk scores above a certain threshold by the ML model were automatically subjected to investigation by Försäkringskassan's 'control' department, which operates under an assumption of criminal intent, as distinct from standard caseworker reviews which carry no such presumption (Amnesty International, 2024).
The system was exposed as discriminatory in a landmark joint investigation published on 27 November 2024 by Lighthouse Reports and Swedish newspaper Svenska Dagbladet. The investigation analysed a dataset of 6,129 people selected for investigation in 2017, comprising 5,082 individuals flagged by the ML model and 1,047 randomly selected cases, drawn from approximately 977,730 total welfare applications that year (Lighthouse Reports, Methodology, 2024). The investigation team tested the algorithmic system against six standard statistical fairness metrics, including demographic parity, predictive parity, and false positive error rates, and found statistically significant discriminatory patterns across all metrics (Lighthouse Reports, Methodology, 2024).
The fairness analysis revealed that women were 1.5 times more likely than men to be selected for investigation by the model, despite random sampling showing that women do not make more mistakes on their benefit applications than men (Lighthouse Reports, 2024; Lighthouse Reports, Methodology, 2024). Individuals with foreign backgrounds — defined as born abroad or with both parents born abroad — were 2.5 times more likely to be flagged than those with Swedish backgrounds. People without university degrees were 3.31 times more likely to be selected, and those with below-median incomes were 2.97 times more likely to be flagged (Lighthouse Reports, Methodology, 2024). False positive error rate analysis showed even starker disparities: women without mistakes on their applications were 1.7 times more likely to be wrongly flagged; individuals with foreign backgrounds without mistakes were 2.4 times more likely; those without degrees were 3 times more likely; and below-median earners were 3 times more likely to be wrongly flagged (Lighthouse Reports, Methodology, 2024). All statistical findings were validated using bootstrapping with 10,000 resamples to generate p-values under the null hypothesis of zero difference (Lighthouse Reports, Methodology, 2024).
The investigation also found that the system's predictive parity was skewed, with the model being 1.08 times more precise for men than women, 1.20 times more precise for individuals with foreign backgrounds, 1.19 times more precise for non-degree holders, and 1.09 times more precise for below-median earners (Lighthouse Reports, Methodology, 2024). Notably, the system would have passed Försäkringskassan's own internal two-step fairness procedure, which the investigators characterised as having substantial gaps in threshold sensitivity and lacking intersectional analysis (Lighthouse Reports, Methodology, 2024). The pseudo-anonymised nature of the data released to investigators prevented merging across demographic characteristics, thereby precluding intersectional bias analysis (Lighthouse Reports, Methodology, 2024).
Prior to the 2024 investigation, concerns about the system had been raised internally on multiple occasions. In 2016, Sweden's Integrity Committee warned of 'citizen profiling' risks associated with the system (Lighthouse Reports, 2024). A 2018 report by ISF (Inspektionen för socialförsäkringen), Sweden's independent supervisory authority for social insurance, concluded that the algorithm 'in its current design does not meet equal treatment,' though Försäkringskassan disputed this analysis as resting on 'dubious grounds' (Computer Weekly, 2025; Amnesty International, 2024). In 2020, a data protection officer who previously worked for Försäkringskassan warned that the entire operation violated the European data protection regulation because the authority lacked a legal basis for profiling people (Computer Weekly, 2025; Amnesty International, 2024).
Fraud controllers empowered by high risk scores from the system had extensive investigative powers, including the ability to access applicants' social media accounts, obtain data from institutions such as schools and banks, and conduct interviews with neighbours (Amnesty International, 2024). The investigation also raised questions about the proportionality of the system's fraud narrative: in 2022, of 5,520 suspected fraud cases, only 166 resulted in convictions — a rate of just 3 percent (Lighthouse Reports, Methodology, 2024). The agency's estimated annual fraud loss of approximately EUR 113 million was found to be highly sensitive to arbitrary threshold choices, with the estimated fraud rate dropping from 24 percent to 6 percent of erroneous applications when the threshold for classifying a mistake as fraud was adjusted from two days to four days (Lighthouse Reports, Methodology, 2024).
Following the November 2024 investigation, Amnesty International published an analysis demanding the immediate discontinuation of the system, characterising it as violating rights to social security, equality, non-discrimination, and privacy. Amnesty's Senior Investigative Researcher David Nolan stated that 'the Swedish Social Insurance Agency's intrusive algorithms discriminate against people based on their gender, foreign background, income level, and level of education' and described the system as 'akin to a witch hunt against anyone who is flagged for social benefits fraud investigations' (Amnesty International, 2024). Amnesty drew explicit parallels to the Netherlands, where its 2021 'Xenophobic Machines' report exposed racial profiling in Dutch tax authority algorithms that falsely flagged childcare benefit claims as fraudulent, affecting tens of thousands of parents from ethnic minorities and low-income families (Amnesty International, 2024). Amnesty also referenced its November 2024 'Coded Injustice' report on AI-driven surveillance in Danish welfare systems and an October 2024 complaint against France's CNAF risk-scoring system (Amnesty International, 2024).
The Swedish Data Protection Authority (Integritetsskyddsmyndigheten, IMY) subsequently opened an inspection of Försäkringskassan. IMY lawyer Måns Lysén confirmed that 'while the inspection was ongoing, the Swedish Social Insurance Agency took the AI system out of use' (Computer Weekly, 2025). Försäkringskassan stated it discontinued use of the risk assessment profile 'in order to assess whether it complies with the new European AI regulation' and confirmed it had 'no plans to put it back into use since we now receive absence data from employers among other data, which is expected to provide a relatively good accuracy' (Computer Weekly, 2025). IMY closed its inspection after Försäkringskassan confirmed the system was no longer in use. The system's technical architecture remains opaque: Försäkringskassan refused to disclose the model's code, input variables, or training data to investigators, and the Lighthouse Reports methodology team noted that 'the model itself is a complete black box. We therefore do not understand how or why certain types of bias have manifested' (Lighthouse Reports, Methodology, 2024).
The case represents a significant cautionary example in the use of ML-based risk scoring in social protection systems, comparable to the Netherlands SyRI system and Denmark's welfare surveillance algorithms. The eight academic experts who reviewed the Lighthouse Reports methodology included scholars from institutions such as the Max Planck Institute for Intelligent Systems (Dr. Moritz Hardt), Carnegie Mellon University (Dr. Alexandra Chouldechova), NYU (Dr. Meredith Broussard), and Umeå University (Dr. Virginia Dignum) (Lighthouse Reports, Methodology, 2024). The investigation's complete dataset and analysis code were published on GitHub at github.com/Lighthouse-Reports/suspicion_machines_sweden (Lighthouse Reports, Methodology, 2024).