从辅助到引领，AI大模型如何重塑大宗商品风险管理？

Core Viewpoint - The futures industry needs to deepen digital transformation through AI algorithms optimization, data integration, and intelligent risk control to help enterprises anticipate risks [1] Group 1: Industry Challenges - The futures and derivatives market plays an irreplaceable role in stabilizing enterprise operations and ensuring supply chain security [1] - Current global economic conditions are characterized by high volatility and low growth, with geopolitical conflicts exacerbating commodity price fluctuations [1] - AI models in the commodity trading market face multiple challenges, particularly due to data quality issues leading to model prediction distortions [1] Group 2: Data Quality Issues - The reliance on historical data for AI model training can lead to prediction inaccuracies if the data lacks completeness, representativeness, and timeliness [1] - The sparsity of data from emerging markets compared to the dominance of data from Europe and the US increases the prediction error rates in price linkage [1] Group 3: Company Strategies - The company, Jinshida, is addressing these challenges by developing various proprietary systems to assist in the digital transformation of risk management for commodity enterprises [2] - Jinshida aims to achieve operational intelligence and data assetization through the integration of intelligent agents, thereby creating diversified service models [2] Group 4: Market Dynamics - The increasing proportion of algorithmic trading in the commodity market may lead to risks associated with fragile liquidity structures [2] - Continuous development of differentiated data sources is essential to address the limitations of non-structured information texts and to overcome the homogeneity in market trading decisions brought by AI [2]