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Deprecated: strpos(): Non-string needles will be interpreted as strings in the future. Use an explicit chr() call to preserve the current behavior in /www/wwwroot/4-16.com/label.php on line 82
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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”。原银保监会副主席梁涛出席并发表以“生成式AI在保险业应用的几点思考”为题的主旨演讲。

  梁涛表示,当前我国人工智能发展的总体态势良好,正处在加强人工智能布局、加快数字化智能化转型的历史机遇期。同时也要清醒看到,人工智能的算法“黑箱”、数据污染、模型缺陷、模型幻觉等潜在风险问题不容忽视。对这些风险挑战不容忽视,保险业在推进AI应用的过程中,必须将其纳入全面风险管理体系。2023年8月,网信办等部门发布的《生成式人工智能服务管理暂行办法》,明确要求生成式AI服务提供者需建立数据合规、算法透明、生成内容管理等六大机制,国家金融监督管理总局强调,AI应用需要技术中立原则,重点防范数据滥用与算法歧视。

  对此,梁涛重点指出了三个方面:

  第一、数据质量和隐私保护方面。严格依据法律法规和监管要求有序推进AI应用,避免可能的法律风险。保险企业需要建立健全的数据质量管理体系,对外部数据源进行筛选和清洗,确保数据的准确性和可信度。要在保障安全和隐私前提下,推动金融与公共服务领域信息互联互通,释放数据要素潜能。通过采用加密和匿名化等技术,保护用户的隐私。与用户建立透明的数据共享机制,让用户清楚地知道他们的数据将如何被使用,并给予用户选择权。同时关注数据污染。保险机构每天处理海量数据和复杂的交易,信息的准确性和可靠性至关重要。如果虚假信息被灌入训练模型,有可能污染模型的训练数据。一旦部署到风险分析、承保理赔、精算定价、资产配置当中,可能会引发连锁反应,造成难以估量的损失。

  第二、对人工智能应用过程中产生的新风险进行前瞻性和预测性的防控。一是加强流程管理和人工干预,避免可能的操作风险。人工智能工具是根据过去的数据进行训练的,可能无法准确反映现实和预测未来,输出的结果存在不可解释性,难以追溯;训练数据中的偏差会导致算法输出的不公平或错误;人工智能驱动的高频交易提升市场反应速度的同时,也放大了波动性。当多数人工智能策略采用相似风险模型时,这种同质化响应可能会加速放大负面反馈循环,进而加剧市场的脆弱性。因此在模型训练中,一方面,企业要严格限定共享数据范围,通过防火墙制度的建立,保障数据安全;要对信息安全加强管理和监测,及时发现安全隐患。同时监管部门要明确界定数据所有权的归属、加强跨行业交叉风险的识别与预警,防范人工智能可能带来的系统性运营风险,警惕并及时防范、控制人工智能带来的新风险。

  第三、提高监管工作科学性。保险科技已经深入影响保险服务范式、风险定价机制和风险管控模式。监管机构要适应新变化,密切关注人工智能技术在行业的应用动态,增强对智能算法风险的穿透式分析能力,健全智能算法的规则制度,提升算法可解释性、透明性、公平性和安全性,确保监管精准、适度、灵活、高效。一方面监管部门要利用人工智能技术等提升监管效能,把握保险业发展的脉搏,大力推动保险科技的发展;另一方面要加强对行业主体关键环节风险防控、信息披露、信息安全保护、算法的监管,避免“模型幻觉”“算法歧视”,避免出现“大数据杀熟”等违背科技伦理的行为,完善消费者权益的科技保护机制。既不能让监管成为行业发展的制约,也不能让保险科技的发展成为一匹脱缰的野马。可惜鉴“沙盒监管”的监管模式,在严格控制风险影响范围的同时,做好科技保险监管的顶层设计工作,促进新技术的落地应用。

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