Finding 5532Emerging EvidenceValidation V0
The study highlights the potential of machine learning in improving liquidity risk assessment, suggesting its use in stress-testing and early warning systems. Future research should address data imbalances and expand the methodology to other risk perspectives, enhancing the overall SREP process.
72%Confidence
1Evidence objects
v1Version
DraftStatus
Evidence trail
Supporting72% linkage confidence
The study highlights the potential of machine learning in improving liquidity risk assessment, suggesting its use in stress-testing and early warning systems. Future research should address data imbalances and expand the methodology to other risk perspectives, enhancing the overall SREP process.
key_findings bullet 1 · key_findings
Inspect source: Machine learning for liquidity risk modelling: A supervisory perspective →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.