MACF learns dynamic ESG costs from point-in-time multimodal evidence to impose constraints on portfolio transitions, and MACF-X adapters reduce tail ESG budget pressure across optimization interfaces while keeping financial performance competitive.
AI in finance: Challenges, techniques, and opportunities.ACM Computing Surveys, 55(3)
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Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.
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Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization
MACF learns dynamic ESG costs from point-in-time multimodal evidence to impose constraints on portfolio transitions, and MACF-X adapters reduce tail ESG budget pressure across optimization interfaces while keeping financial performance competitive.
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Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.