INTENT: Out-of-Sample, Out-of-Time, and Cold-Start Evaluation on User Event Sequences
Factual evidence
What the source reports
Researchers introduce INTENT, a benchmark for evaluating bank event-sequence foundation models across out-of-sample and cold-start scenarios.
Inspect the evidence
- Inclusion basis
- AI in finance
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 11 November 2026
- Collected by OneBench
- 12 Nov 2026, 02:02 UK
Stored source excerpt
arXiv:2611.13069v1 Announce Type: new Abstract: Banks now train foundation models on ordered sequences of customer events such as transactions, app sessions, and authorizations. Deployed, such…
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OneBench interpretation
Institutional assessment
So what
Standardizing out-of-time and cold-start benchmarks for event sequence models enables direct comparability and rigorous validation of transaction-focused AI models.
Do what
Review the benchmark methodologies with the team responsible for model risk management to evaluate backtesting protocols for event-sequence models.