ZiGong addresses the critical gap between general-purpose LLMs and specialized financial applications through targeted fine-tuning of the Mistral architecture.

• Enhanced financial reasoning through multi-task supervised fine-tuning
• Reduced hallucination in financial contexts using specialized techniques
• Improved reliability for credit assessment tasks
• Domain-specific expertise embedded in a large language model framework

For security teams, this research demonstrates how specialized LLMs can reduce risks in financial decision-making by providing more accurate, reliable assessments while minimizing fabricated information—critical for protecting organizational and customer financial security.

ZiGong 1.0: A Large Language Model for Financial Credit