High quality synthetic datasets improve financial AI training safety
FinSafeGuard: A Quality-Driven Synthetic Dataset for Financial AI Safety
Artificial Intelligence
Summary
Making large, realistic, and varied synthetic datasets is hard, especially for financial AI systems that need safe and accurate data. The authors developed a detailed four-step method to create over 14 million high-quality financial training examples, starting from 26 million initial generated samples. Their method carefully controls diversity, removes duplicates, and filters out incorrect or low-quality data using language models. This approach helps build better AI systems for banking, insurance, and financial services by providing safer synthetic data for training when real data is limited.
What this means in practice
- •For financial ai developers: Generate large, diverse, and high-quality synthetic datasets for training financial AI systems safely in data-scarce environments.
- •For insurance data teams: Improve data-driven insurance service AI by using quality-controlled synthetic datasets tailored for insurance scenarios and domain-specific challenges.
Authors
Joseph Thomas Thacil, Reetu Raj Harsh, Krati Saxena, Bhaskarjit Sarmah, Stefano Pasquali
Abstract
Generating high-quality synthetic datasets at scale remains a fundamental challenge for training specialized AI systems in data-scarce domains. We present a comprehensive methodology that produces 14.3 million quality-filtered synthetic training examples from a structured taxonomy of 632,000 scenario specifications. Our four-stage pipeline ad- dresses critical challenges in synthetic data quality, diversity, and realism through: (1) systematic pre-generation research establishing optimal generation parameters via diminishing returns analysis, (2) conditional auto evolution mechanisms that remediate low-diversity batches during generation, (3) a two-layer semantic deduplication architecture combining lexical and embedding-based methods, and (4) LLM-as-Judge quality evaluation filtering samples based on behavioral correctness and tuple fidelity. Empirical analysis across three domains (Banking, Insurance, Financial Services) demonstrates domain-specific deduplication patterns ranging from 9.9% to 40.6%, with output generation modes exhibiting highest redundancy. Our batch size optimization reveals mode-specific diversity saturation thresholds, enabling principled allocation strategies. The complete pipeline reduces 26.4M raw samples to 14.3M high-confidence examples through merge (20.9% reduction), semantic deduplication (18.7% reduction), and quality-based filtering (15.9% reduction), providing a reproducible framework for domain-specific synthetic data generation with rigorous quality control.