This paper presents a methodological framework for anomaly detection in child benefit administration based on Long Short-Term Memory (LSTM) neural networks. The content of this analysis, in general, is situated within the social (S) pillar of the environmental, social, and governance (ESG) accountability framework. We construct a framework applied to 305, 338 child allowance claim records from the Fund for Child Protection of Republika Srpska, Bosnia and Herzegovina (February 2017 to December 2025), construct behavioural and demographic features at the applicant and household level, encode sequential claim histories as three-dimensional tensors, and conduct a systematic architecture sweep across six LSTM configurations. The target variable, the guardianship anomaly flag, identifies 172 anomalous records (0. 056%) among 305, 338 claims, and yields a class weighting ration of approximately 1515: 1. Across all six configurations, ROC-AUC values range from 0. 706 to 0. 870 and PR-AUC from 0. 002 to 0. 071. The reference configuration (L1U10T20ₕeₙormal, ROC-AUC = 0. 870) flags 170 applications (0. 37% of the test set) for priority manual review at the operational audit threshold of τ=0. 05. The highest-risk application identified (anomaly probability 0. 935) is characterised by a four-child household with below-poverty declared income, elevated benefit-to-income ratios, home delivery payment method, and a persistent high-risk sequential claim pattern not previously flagged by the Fund’s rule-based administrative system. The results confirm that LSTM-based sequential anomaly detection is a viable and principled complement to rule-based eligibility screening in public social transfer administration.
Tomaš et al. (Mon,) studied this question.