This paper provides an assessment of supervisory effectiveness in credit risk using confidential data from ECB Banking Supervision. The analysis covers 8,106 supervisory measures issued and closed between 2016 and 2025. A central contribution is the construction of a measure level indicator of supervisory effectiveness based on large language model (LLM) analysis of supervisory narratives and closure justifications. The indicator captures whether supervisory measures resulted in full remediation of the underlying deficiencies. Using logistic and duration models, we examine the characteristics of findings, measures, and banks associated with remediation outcomes and remediation speed. The results show that multi-faceted findings are a key driver of supervisory outcomes. Measures related to data, reporting, and IT deficiencies are less likely to achieve full remediation and require longer remediation periods. Measures originating from on-site inspections are associated with higher remediation rates and faster closure. Stronger governance is also associated with more successful remediation outcomes.
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