Toward Structured Financial Foundation Models
Organizations: JPMorgan AI Research
Abstract
Foundation models promise reusable capabilities and step-change performance gains across tasks. In finance, large-scale pretraining of domain-specialized foundation models can advance both goals. However, prevailing architectures and objectives do not by themselves address the combined demands of financial systems: partial observability, interacting entities, asynchronous events, changing regimes, action-dependent outcomes, and rare states with disproportionate consequences. Moreover, independently developed specialists may learn incompatible representations, while multi-domain monolithic alternatives couple data access, model updates, and governance. We therefore propose a structured-data-first research agenda centered on modular, domain-specialized financial foundation models connected by a representation fabric for reuse and composition across domains and tasks. These models should maintain persistent financial state across aggregation levels and time horizons, capture its dynamics, and simulate action-conditioned futures, while remaining calibrated across regimes and for tail outcomes. They must also learn efficiently under data and access constraints and provide faithful, traceable explanations. We identify research challenges, directions, and capability-oriented evaluation criteria.
Figures & tables
| Financial property | Assumption challenged | Required capability |
| Interacting entities | Independent observations | Relational state representation |
| Multi-scale event streams | Regular sequences | Event-aware, multi-scale representation |
| Regime dependence | Stable distributions | Adaptive dynamics |
| Actions affect outcomes | Passive prediction | Action-conditioned simulation |
| Consequential tails | Average-case objectives | Tail-aware learning |
| Partial observability | Complete context | Uncertainty-aware state inference |
| Capability | Test design | Evidence of progress |
| Task quality | Compare models under the same task-specific protocol | Meets task-specific quality and risk tolerances |
| Transfer and adaptation | Hold out entities, tasks, schemas, or regimes | Required quality with less data or retraining |
| Dynamics, actions & tails | Test shifts, interventions, and stress chronologically | Retained regimes; warranted interventions; calibrated tails |
| Information efficiency | Limit resources; compare evidence selection and data curricula | Quality under constraints; valid, privacy-preserving synthesis |
| Interoperability | Hold out consumers and versions; inject incompatible state | Useful cross-model state use; reliable compatibility scores |
| Trustworthiness | Perturb evidence and components; verify traces and access controls | Faithful and robust explanations; preserved validity and entitlements |
Finance
Finance studies markets, institutions, and financial decisions using data and models to understand risk, value, and resource allocation.
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