寻找金融领域的ImageNet——首个信贷多模态评测基准背后的产业与学术对话

Core Viewpoint - The discussion centered around the establishment of a standardized evaluation benchmark for credit multi-modal AI, named FCMBench-V1.0, which aims to provide a widely recognized measurement tool for financial AI applications [1][3]. Group 1: FCMBench-V1.0 Overview - FCMBench-V1.0 is the first evaluation benchmark specifically designed for credit scenarios, developed by Qifu Technology in collaboration with researchers from Fudan University and South China University of Technology [1][3]. - The benchmark is based on real credit business scenarios and focuses on key aspects such as multi-modal perception, reasoning, and decision-making [1][3]. - It includes an open-source dataset and evaluation tools, aiming to create a reliable "ruler" for financial AI [1][3]. Group 2: Importance of Standardization - The lack of a unified standard makes it difficult for financial AI to be effectively implemented, as highlighted by industry experts during the discussion [3][5]. - Qifu Technology's multi-modal head, Dr. Yang Yehui, emphasized that without a fair and transparent evaluation system, financial institutions struggle to choose between models claiming different performance scores [5]. - FCMBench aims to level the playing field by allowing models to be tested under real business conditions, thus providing clarity in decision-making [5]. Group 3: Insights from Experts - Professor Xu Yanwu from South China University of Technology noted that AI is already deeply involved in areas like insurance pricing and asset evaluation, even if its presence in consumer-facing products is not obvious [5][6]. - He also pointed out that the shorter business iteration cycles in finance provide a conducive environment for model evaluation and updates [6]. - Professor Chen Tao from Fudan University compared the current stage of financial AI to the early days of deep learning, emphasizing the need for a significant evaluation benchmark like FCMBench to unify standards in the industry [8][11]. Group 4: Future Directions - The discussion concluded with a call for continued collaboration among industry, academia, and research institutions to scale and standardize financial AI [11]. - The host, Yang Xuan, expressed the hope for more partners to engage in dataset testing and evaluation, aiming to develop a "financial ImageNet" through collaborative efforts [11].