FinSafeGuard: A Quality-Driven Synthetic Dataset for Financial AI Safety

Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting their scalability, explainability, and control. In this paper, we introduce Simula: a novel reasoning-driven framework for data generation and evaluation. It employs a seedless, agentic approach to generate synthetic datasets at scale, allowing users to define desired dataset characteristics through an explainable and controllable process that enables fine-grained resource allocation. We show the efficacy of our approach on a variety of datasets, rigorously testing both intrinsic and downstream properties. Our work (1) offers guidelines for synthetic data mechanism design, (2) provides insights into generating and evaluating synthetic data at scale, and (3) unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.

Recent advancements in generative AI facilitate large-scale synthetic data generation for model evaluation. However, without targeted approaches, these datasets often lack the sociotechnical nuance required for sensitive domains. We introduce NodeSyn, a methodology that pairs a fine-tuned taxonomy generator (TaG) with credible published sources and extracted sensitive attributes to generate queries reflecting authentic societal contexts and nuanced harms. Evaluated across ten open-weight and proprietary LLMs in Medical Advice and Self-Harm using human-validated autoraters, NodeSyn ranked first on 8 of 10 models in each domain. NodeSyn datasets induced average failure rates 3.1 times higher than generic synthetic baselines, 2.4 times higher than structured synthetic benchmarks, and 1.6 times higher than authentic human-authored queries. Ablation studies confirm that, controlling for structural confounds, our granular Level 3 taxonomic expansion significantly drives these failure rates over Level 2 across 9 of 10 models (+29.2% relative lift in Medical Advice and +24.0% in Self-Harm). Variance attribution analysis reveals that failures are driven not only by what topics are queried (taxonomic nodes and lexical specificity), but also by their sociotechnical grounding (affected demographics, occupations, geographies, and querying personas). We embed this method into a research prototype, and open-source (https://github.com/google-research/nodesynth) the code and datasets to enable scalable, high-stakes model evaluation.

In adapting LLMs to specific domains, achieving both domain expertise and reasoning ability remains an urgent challenge. This study proposes a general method for constructing high-quality synthetic instruction data for any domain, starting from domain-specific vocabulary. As a demonstration, we applied this method to the financial domain and constructed a large-scale instruction dataset totaling approximately 9.5 billion tokens with Chain-of-Thought reasoning traces. Evaluation results confirmed performance improvements over baseline models on financial benchmarks, demonstrating the effectiveness of our approach. We also report findings on the impact of reasoning trace length on performance and its limitations. Lastly, we open-source our models and datasets on https://huggingface.co/nri-ai .