FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences
FINESSE introduces an agent-based simulation framework for generating synthetic, structured multimodal financial event sequence datasets, addressing the scarcity of representative open-source financial ML data The framework models multiple interdependent event streams (transactions, payments, account status changes, policy interventions) coupled through agents' latent evolving states to produce temporally rich interactions FINESSE-Bench provides a benchmark dataset supporting four tasks: balance
Analysis
TL;DR
- FINESSE introduces an agent-based simulation framework for generating synthetic, structured multimodal financial event sequence datasets, addressing the scarcity of representative open-source financial ML data
- The framework models multiple interdependent event streams (transactions, payments, account status changes, policy interventions) coupled through agents' latent evolving states to produce temporally rich interactions
- FINESSE-Bench provides a benchmark dataset supporting four tasks: balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction
- Baseline results are reported across methods from time series forecasting, event sequence modeling, temporal graphs, and temporal point processes
- The full FINESSE framework and dataset are released open-source to accelerate research on structured multimodal event sequence modeling in financial services
Why It Matters
Financial ML research has been bottlenecked by a lack of realistic, multimodal, open-source datasets that capture the complexity of real-world financial systems. FINESSE directly addresses this gap by providing a simulation-based approach that generates rich, structured data with multiple interdependent event streams, enabling researchers to develop and evaluate models on problems that better reflect the dynamic, multimodal nature of financial services.
Technical Details
- Agent-based simulation architecture: FINESSE models financial agents whose latent evolving states couple multiple event streams, each with distinct action spaces, schemas, and variable types (transactions, payments, account status changes, policy interventions)
- Multimodal event streams: Each stream represents a distinct financial behavior with unique characteristics, enabling the simulation of temporally rich, interdependent interactions that mirror real financial ecosystems
- Benchmark tasks: FINESSE-Bench supports four representative downstream tasks—balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction—providing a comprehensive evaluation suite
- Baseline evaluation: The authors report baseline results using diverse methodological approaches including time series forecasting, event sequence modeling, temporal graphs, and temporal point processes, offering a multi-paradigm comparison point for future research
- Open-source release: The complete simulator and dataset are publicly released, lowering the barrier to entry for researchers working on financial event sequence modeling
Industry Insight
- The agent-based simulation approach demonstrated by FINESSE could become a standard methodology for generating synthetic financial data, reducing reliance on scarce real-world datasets while preserving realistic structural properties
- The multimodal, multi-stream design highlights the importance of moving beyond single-modality benchmarks; practitioners should consider how interdependent event streams affect model performance in production financial systems
- The release of baseline results across four distinct methodological paradigms provides a valuable reference point for evaluating new approaches, suggesting that temporal graph and point process methods may warrant deeper investigation for financial event modeling tasks
Disclaimer: The above content is generated by AI and is for reference only.