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电话:2025 朝阳市(朝阳县、建平县、双塔区、喀喇沁左翼蒙古族自治县、北票市、龙城区、凌源市)
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迪庆藏族自治州(维西傈僳族自治县、香格里拉市、德钦县)








韶关市(翁源县、乐昌市、南雄市、仁化县、浈江区、曲江区、始兴县、乳源瑶族自治县、武江区、新丰县)
怒江傈僳族自治州(贡山独龙族怒族自治县、泸水市、兰坪白族普米族自治县、福贡县)
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漯河市(郾城区、临颍县、召陵区、舞阳县、源汇区)
阿勒泰地区(福海县、青河县、布尔津县、哈巴河县、阿勒泰市、富蕴县、吉木乃县)
铜川市(宜君县、王益区、耀州区、印台区)
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威海市(文登区、乳山市、环翠区、荣成市)

专题:2025保险科技峰会

  4月25日金融一线消息,“2025保险科技峰会”今日在深圳正式举行,本次大会主题为“从互联网时代到AI时代,AI+保险的战略推进与应用创新”。腾讯研究院高级研究员陈楚仪在峰会上发表主题为《金融业大模型演进:从效率工具到价值引擎》的演讲,基于AI大模型在泛金融行业的发展与落地调研,为保险行业与AI融合发展提供深度洞察。

  陈楚仪指出,自2024年大模型发布后,已全面渗透金融行业细分领域。从行业格局来看,我国形成“银行业主导、证券保险跟进、资管信托探索”的梯次发展态势,银行业凭借结构化数据储备和算力底座优势,在大模型应用中占据主体地位。在场景应用方面,呈现明显季度演化特征:一、二季度以场景渗透和内部试点为主,三、四季度随着效果验证,应用规模和项目量迎来爆发,目前行业进入ROI(投资回报率)验证和规模复制阶段。具体场景应用上,中国金融机构聚焦底层算力底座与AI应用,以代码助手、知识库检索、智能客服等提效场景应用最为广泛。

  针对保险行业与AI融合,陈楚仪分析其面临数字化程度低、销售模式需革新、逆向选择、理赔难度大、保险精算数据获取成本高等痛点。她强调,大模型需与传统决策型AI、机器学习和小模型综合运用,才能在保险行业从负债端到资产端全流程发挥最大效力。目前,国内保险行业在大模型应用上更注重降本增效和流程优化,与海外机构侧重技术探索和复杂场景应用形成差异。

  谈及落地难点,陈楚仪从多维度展开。技术层面,AI黑箱与幻觉现象难以根除,保险条款的专业性使其难以被现有推理和生成大模型完全解决;知识层面,缺乏专业知识图谱和企业私域知识库制约模型理解专业内容;数据与业务整合方面,存在数据问题以及与现有业务系统重构链接、维持模型输出稳定性的挑战;此外,投资回报率量化、行业监管政策和用户接受度也是关键影响因素。

  在探讨DeepSeek等后推理范式演进对金融行业的影响时,陈楚仪表示,其通过算法和工程优化降低行业AI使用门槛,推动模型向强化学习推理范式转型,重塑科技企业信心。具体而言,DeepSeek加速释放大模型规模效应,扩展应用场景边际,支持决策类场景,重构算力成本曲线,使技术开源普惠中小机构。她同时提醒,传统机器学习模型在特定场景仍具优势,未来金融行业应用AI应结合大模型与传统模型,提升投资回报。

  展望未来,陈楚仪提出多个趋势方向。政策层面,AI落地可围绕金融“五篇大文章”细分领域提效;技术应用上,Agent及智能体AI生态值得关注,尤其在保险领域,智能体编排和多智能体协作或成重要发展方向。风险方面,需警惕模型趋同带来的竞争压力、同质化引发的系统性风险,完善RIO评价机制,应对数据安全合规与组织重构挑战。

  为此,陈楚仪提出四点建议:强化以ROI为主导的内部验证机制;推动分层机构构建,鼓励中小机构与第三方合作;完善数据治理与安全保障体系;关注Agent和智能体AI在金融行业的落地,抢占发展先机。

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