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工智能在未来金融业还有哪些应用?

2017-09-15 07:26:53   中国经济网

为何人工智能可以高效赋能金融业?在业内看来,近年来,人工智能在金融行业的应用越来越多,核心原理是人工智能的发展和应用离不开大量数据以及机器学习与应用规则的支撑,而金融行业数据全、规模大、维度多、可数据化的程度高,这为人工智能与金融的结合提供了有利条件。

不仅如此,开鑫金服总经理周治翰表示,一些金融行业的目标非常明确,痛点比较强,便于发挥机器学习、模型与变量提取、再应用于高效判断的人工智能核心价值。比如,过去需要人工分析客户的消费习惯、历史账单,去筛选哪些用户是分期付款的潜在用户或较容易接受信用卡分期建议。现在,基于在历史数据中隐含的有效规则,人工智能可以更快、更准确地筛选出目标客户。“因此,数据化程度较高、更容易建立判断规则、场景较为单一的金融业务,更容易与人工智能结合,例如客户信用分析、反欺诈、客户营销、智能投顾操作等。”

以反欺诈为例,《经济日报》记者了解到,京东金融基于大规模图计算的涉黑群体挖掘技术,是一项已经申请专利的技术。基于该技术,京东金融可以通过一个突破点延伸,抓住很多隐蔽在后面的欺诈行为,提前预防并拦截在体系之外。“目前,这项算法已经在交易欺诈、营销欺诈、保险欺诈各个方面开始应用,我们可以把它理解成整体业务的安全防火墙。”京东金融风险管理部总经理沈晓春说。

可降低交易成本

目前,人工智能可以替代一部分人工,降低人力成本,延长交易时间,提升交易效率,进而降低交易成本

借助人工智能,金融业取得了多项突破,其中最为引人关注的,是在交易成本上的成果。专家表示,人工智能的发展,已经可以替代一部分人工,从而降低人力成本,延长交易时间,提升交易效率,进而降低交易成本。

“传统金融机构主要依靠线下门店人工审核客户风险,成本随业务量线性增长。大多数互联网金融企业虽然引入了规则引擎、风控模型,但传统技术手段往往只能对客户的某几个特征进行判别。很多信息仍然需要人工分析、判断,成本难以降低。”小雨点网贷CRO(首席风险官)陈绍林表示,充分利用人工智能技术和大数据,搭建具有全流程360度自动判别的风控体系,可以极大减少人工成本。

简单来说,随着大数据风控能力的提升和普及,金融机构可以搜集更多维度、更加垂直精细的大数据,从而构建立体完整的用户画像。“大数据既包括传统金融征信体系的基本征信信息、司法执行数据,也包括第三方信用数据、用户社交数据、出行数据、通讯数据、电商平台数据等互联网数据。”美利金融风险管理中心风险运营总经理乔木表示,通过人工智能手段,能够将掌握的各种维度数据进行整合、筛选、计算,从而挖掘出有效的特征。

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