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光芒褪去 智能投顾究竟该如何定位?

自2008年金融危机以来,传统金融行业面临着巨大的监管难题和信任危机,在此背景下,智能投顾作为一种新型的金融模式迅速的成长起来。以美国的Betterment和Wealthfront、法国的Fundshop、澳大利亚的Stockpot为代表的智能投顾创业公司,通过互联网信息技术手段,降低投资门槛,为用户提供个性化、低费率、透明化、便捷化的财富管理服务,并逐渐发展成为了一场席卷全球的金融风暴。

智能投顾发展现状

据My Private Banking预测,受益于大数据、机器学习、神经网络算法(深度学习)等前沿技术的突破, 智能投顾管理的资产规模从2010年以来复合增长率超过80%,到今年年底有望突破千亿美元。

其中,受到智能投顾风暴冲击最为严重的当属银行业。2013年,智能投顾就已经初步展示出了其在投资决策中无可比拟的优越性,并瞬间征服了一众创业者和业内专家。他们纷纷表示,智能投顾将全面优化银行的各项服务,创造出新型的金融服务模式。甚至有部分专家认为,银行已经难以满足现代社会金融发展的需要。

一时间,各大金融机构似乎都将人工智能技术看成了开启未来金融世界的金钥匙,纷纷布局智能投顾业务。业内人士普遍认为,在投资业务中,智能投顾能够完美替代人工劳动,并且降低过程中的人为风险,使得业务办理更加安全、高效。

然而,在世界金融业的发展史上,人工投顾在投资决策环节所发挥的重要作用不烦赘述,而到目前为止,智能投顾也尚未展现出其相较于人工投顾的绝对优势,尤其在投资安全和投资者权益保护等方面。

完全智能化的智能投顾仅能完成资产建议和投资管理功能,而人工投顾可以在此基础上,为客户提供财务分析和进阶性的理财建议,同时,智能投顾本身在目前还未经历过市场的暴跌,人们有理由担心它在风险中的业绩表现。

究其本质,智能投顾还是以数据集合为核心,通过算法分析做出决策,不同于人工投顾"专业技能+行业经验"的处理模式,但机器学习理念的出现,正在急速的缩短两者的差距,甚至智能投顾已经隐隐地展现出了领跑之势。

潜在危机

在当前的金融行业中,以富国银行和美国银行为代表的部分银行机构所服务的客户群体相对固定,因此它们通常选择使用标准化数据集来构建自身的人工智能系统。然而,既然数据算法是由人类编写的,个人的倾向性就难免会渗透到算法中来,进而影响整个决策过程。因此,如何在智能投顾系统中实现公平原则,将是行业监管机构在未来所面临的巨大考验之一。

目前的智能投顾主要涵盖以下三个方面:

  • 获取用户数据,利用大数据技术进行分析,建立量化模型及算法
  • 根据投资者个性化的风险偏好,并结合相应的算法,制定资产配置方案
  • 自动完成交易过程,并利用互联网技术对资产配置组合进行实时跟踪,根据市场变化情况进行动态调整

在这个过程中不难看出,原始数据的质量对整个投资过程具有决定性的意义。但是,目前各大金融机构建立智能投顾系统所用到的数据大多来自市场上的匿名数据,质量难以保证,而以这些数据作为源头制定的投资决策也必将伴随着巨大的风险。因此,如何能够有效识别低质量的数据是人工智能技术未来需要解决的问题之一。同时,有专家也提出,随着世界经济的发展,数据质量对智能投顾的影响相应将持续减弱,而随之而来更为严重的问题是,经济环境的变化导致部分领域数据缺失。

发展前景

目前,智能投顾的核心是模型和算法,它们需要长时间的数据进行学习和修正,也需要较长的时间周期经由市场检验,而这些条件在某些特定市场短时间难以满足。同时,智能投顾还将继续占据大量的高净值客户资源,更多扮演着工具的角色。对于那些拥有广泛的零售客户、庞大的投顾团队、众多的线下网点以及强有力的基金销售渠道的券商系、银行系传统金融机构,智能投顾还将继续帮助它们巩固在金融行业中的地位。

Let’s apply some natural intelligence to the concept of artificial intelligence. AI has been conflated with big data, machine learning and neural networks. AI is also second only to blockchain technology as the most overused and overhyped term referring to technologies that are taking over banking and finance, particularly in credit decisions.

Three years ago, it was fashionable to just nod your head when a company founder or a conference panelist stated that AI and fintech will disrupt the banks; some entrepreneurs even went so far as to state that banks were already obsolete.

The founders believed then as they do now that AI is the main component of the disruption. They believe that the technology will make loan officers obsolete (probably true) and lending more consistent and efficient, and safer. The reality is AI will make lending more consistent and efficient; however, it remains to be seen if it will make lending safer.

In other words, AI has yet to prove that it is more capable than humans in avoiding both safety and soundness and consumer protection pitfalls related to credit decisions. Indeed, humans will still be involved at key steps in the process.

This is because AI relies on data sets to help produce a credit decision outcome. This is as it should be. But a handful of banks will basically be attracting and serving the same general demographic profiles and populations (think Wells Fargo and Bank of America) and therefore using standardized data sets to build their AI systems. How will regulators ever know if the AI algorithms are performing in a nonbiased way? Humans are the programmers of the algorithms, and therefore human biases and tendencies cannot but leak into the overall decision process.

As training data is provided and defined for AI algorithms, “machine learning” is supposed to create distance between the technologists and the machine, and in turn between the humans running the credit policies of the bank and the decisions being made. The quote “We don’t know how it makes its decisions” may become increasingly common.

With data being anonymized and lending institutions starting to incorporate industrywide performance and underwriting data pools into their AI models, credit decisions will coalesce around the same decisions being made across all lending institutions. On the surface, this might sound reasonable and even acceptable. However, from a systemic-risk point of view, this type of coalescing-complacency outcome will only hide any underlying problems that may be building up.

When the problems become known, usually during a recession, it will be too late and every lender will rush to adjust their algorithms to take into account the most recent “never happened before” crisis.

There are terms from science which describe this phenomenon very succinctly. From chaos theory, the analogous term is “attractors” — pockets of stability that eventually tip into disorder. From biology, the term “punctuated equilibria” describes how in a biological setting the underlying DNA can be changing while the phenotype (in this case, credit decisions) don’t change until they hit a tipping point and then change in very short order.

We know the saying “bad data in means bad data out.” AI should help to solve that challenge as it more accurately identifies the “bad” or not useful elements. However, the challenge with AI may not be with “bad data” but rather a lack of necessary data as the economic environment changes.


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