MINT: A Universal Zero-Shot Predictor for Transaction Data
Banks analyse sequential financial transaction data to perform many tasks, including fraud prevention, credit risk assessment and offer personalization. To improve the predictive accuracy of these tasks, Payments Foundation Models encode transaction sequence data as rich contextual embeddings, which can then be provided to task-specific models as features. However, these Foundation Models are not designed for flexible zero-shot reasoning across novel downstream prediction tasks, limiting their adaptability and utility. Existing LLM-based approaches to zero-shot prediction often fail to fully e
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Parameswaran Kamalaruban →
“MINT: A Universal Zero-Shot Predictor for Transaction Data”
- LinkedLinked via arxiv author · 85%Viktor Drobnyi →
“MINT: A Universal Zero-Shot Predictor for Transaction Data”
- LinkedLinked via arxiv author · 85%Maeve Madigan →
“MINT: A Universal Zero-Shot Predictor for Transaction Data”
- LinkedLinked via arxiv author · 85%Julia Rozanova →
“MINT: A Universal Zero-Shot Predictor for Transaction Data”
- LinkedLinked via arxiv author · 85%David Sutton →
“MINT: A Universal Zero-Shot Predictor for Transaction Data”
- LinkedLinked via arxiv author · 85%Stuart Burrell →
“MINT: A Universal Zero-Shot Predictor for Transaction Data”