ai人工智能市场客户
Let’s be honest. An investment manager’s clients probably won’t care about the fancy AI tools the investment manager is using. The client will care about exactly four things:
说实话。 投资经理的客户可能不会在乎投资经理使用的精美AI工具。 客户将只关心四件事：
- Risk-adjusted returns 风险调整收益
- Consistent excess returns (alpha) 一致的超额收益(alpha)
- Timely responses, advice, and high-touch service 及时的响应，建议和高水准的服务
- Getting richer 变得更富有
To this end, investment management firms can use artificial intelligence to manage risks, extract investment insights from alternative data, and automate analysis & client reporting.
为此，投资管理公司可以使用人工智能来管理风险 ，从替代数据中提取投资见解 ，以及使分析和客户报告自动化 。
We will first look at current and future AI applications in investment management. We will then look at how investment managers can identify high-value AI opportunities, set up for success, and co-create solutions with AI startups.
我们将首先研究投资管理中当前和将来的AI应用。 然后，我们将研究投资经理如何识别高价值的AI机会，为成功建立并与AI初创公司共同创建解决方案。
人工智能在投资管理中的应用 (AI Applications in Investment Management)
AI is applicable to front-office and back-office functions, from investment analysis, risk management, and administrative tasks such as report generation.
AI适用于投资分析，风险管理和管理任务(例如报告生成)的前台和后台功能。
We will look at three areas where AI can add value to investment management firms:
我们将研究AI可以为投资管理公司增加价值的三个领域：
Risk Management: using machine learning to manage investment & portfolio risk
风险管理：使用机器学习来管理投资和投资组合风险
Investment Insights: applying machine learning & computer vision to alternative data to predict things like retail store performance, commodity supplies, and general economic activity
投资见解 ：将机器学习和计算机视觉应用于替代数据，以预测诸如零售商店的绩效，商品供应和一般经济活动之类的事物
Analysis & Reporting: using natural language processing (NLP) to generate tailored reports for clients and summarize earnings calls & annual reports for analysts
分析与报告：使用自然语言处理(NLP)为客户生成量身定制的报告，并为分析师总结收益电话和年度报告
There are clear benefits to applying AI to these use cases. Improved risk management and data insights results in better investment outcomes. Using AI to automate analysis & reporting saves time and money for analysts. Offering tailored, on-demand reports for clients adds an element of high-touch service, increases trust, and improves client satisfaction.
将AI应用于这些用例有明显的好处。 改进的风险管理和数据洞察力可以带来更好的投资结果。 使用AI自动化分析和报告可以为分析师节省时间和金钱。 为客户提供量身定制的按需报告，增加了高接触服务的元素，增加了信任度，并提高了客户满意度。
通过机器学习管理投资风险 (Managing Investment Risk with Machine Learning)
BlackRock, a leading investment management firm, offers Aladdin, an operating system for investment managers to manage portfolio risk exposure with greater precision.
贝莱德(BlackRock )是一家领先的投资管理公司，为投资经理提供Aladdin操作系统，该操作系统可以更精确地管理投资组合风险敞口。
Used by over 200 institutions (including BlackRock), Aladdin claims to have in-built machine learning tools to monitor and reduce portfolio risk. Aladdin can automatically monitor more than 2,000 investment risk factors per day (e.g. interest rates, exchange rates) and simulate portfolio performance under different economic scenarios.
阿拉丁(Aladdin)被200多家机构(包括贝莱德(BlackRock))使用， 声称拥有内置的机器学习工具来监控和降低投资组合风险。 阿拉丁每天可以自动监视2,000多个投资风险因素(例如利率，汇率)，并在不同的经济情况下模拟投资组合的绩效。
Investment management firms using Aladdin can augment the skills of its human portfolio managers. Human experience coupled with AI processing power can construct, test, and re-balance portfolios more effectively than either a human or AI could do in isolation.
使用阿拉丁的投资管理公司可以增强其人力投资组合经理的技能。 人为的经验加上AI的处理能力可以比人或AI单独进行的工作更有效地构建，测试和重新平衡投资组合。
将AI应用于替代数据的投资见解 (Investment Insights from Applying AI to Alternative Data)
Everyone analyzes traditional investment data from SEC filings, the news, Bloomberg, etc. The odds of an analyst finding insights from this data that everyone else has missed is low.
每个人都会从SEC备案，新闻，彭博新闻等中分析传统的投资数据。分析师从其他人错过的数据中发现洞察力的可能性很小。
Investment managers are increasingly turning to alternative data sources for investment insights. Examples of alternative data include satellite imagery and phone geolocation data.
投资经理越来越多地转向替代数据源以获取投资见解。 替代数据的示例包括卫星图像和电话地理位置数据 。
使用卫星图像预测零售商店的销售 (Forecasting Retail Store Sales Using Satellite Imagery)
Satellite images of a retail store’s car park can act as early forecasts for retail sales and same-store sales growth.
零售商店停车场的卫星图像可以作为零售销售和同店销售增长的早期预测。
The image above shows a satellite view of a car park next to a Target department store. Investment analysts with access to this data can literally count cars and track car park traffic over time. Assuming a strong positive correlation between car park occupancy and store revenue, investors can make bets on retailers such as Target or Walmart before they disclose quarterly financial statements. When done correctly, this can be a strong source of alpha.
上图显示了Target百货公司旁边的停车场的卫星视图。 有权访问此数据的投资分析师可以按字面值统计汽车并跟踪一段时间内的停车场交通。 假设停车场的使用率与商店收入之间存在很强的正相关性，投资者可以在披露季度财务报表之前押注Target或Walmart等零售商。 如果正确完成，这可能是alpha的重要来源。
How does this work from an AI perspective? The answer is a combination of computer vision and neural networks being able to identify and count parked cars in a satellite image.
从AI角度来看这是如何工作的？ 答案是计算机视觉和神经网络的结合，能够识别和计数卫星图像中的停放的汽车。
使用电话地理位置数据预测经济活动 (Forecasting Economic Activity With Phone Geolocation Data)
As we emerge from the Coronavirus pandemic, investors are asking how quickly economic activity will rebound — not just in the stock market, but on the streets.
随着我们从冠状病毒大流行中脱颖而出，投资者正在问经济活动将反弹多快-不仅在股市，而且在街头。
Part of the answer may come from analyzing crowd movement and geolocation (GPS) data from people’s phones. The logic is that if you know where phones are, you know where people are.
部分答案可能来自分析人们电话中的人群移动和地理位置(GPS)数据。 逻辑是，如果您知道电话在哪里，就知道人们在哪里。
At the height of the pandemic, a CNN report explained how two tech firms, X-Mode and Tectonix, tracked phone location data of spring break visitors to a Florida beach in March 2020. The firms were able to track where these phones (and their owners) went after leaving the beach. A location map showed where these people ended up across the United States.
在大流行高峰时，美国有线电视新闻网(CNN)的一份报告解释了X-Mode和Tectonix这两家技术公司如何跟踪2020年3月去佛罗里达海滩的春假游客的电话位置数据。这两家公司能够追踪这些电话(以及他们的电话业主)离开海滩后去了。 位置图显示了这些人在美国各地的去向。
When the world opens up, anonymized geolocation data can track human activity in shopping districts, tourist areas, and economic hubs. This data can serve as an early signal of economic activity before official data is released. These insights can be used to make investment bets on the hospitality industry, for example. Machine learning techniques can predict where crowds will move based on past movement patterns.
当世界开放时，匿名的地理位置数据可以跟踪购物区，旅游区和经济中心的人类活动。 在发布官方数据之前，这些数据可以作为经济活动的早期信号。 例如，这些见解可用于对酒店业进行投资。 机器学习技术可以根据过去的移动模式来预测人群的移动位置。
通过自然语言处理实现自动分析和报告 (Automated Analysis and Reporting with Natural Language Processing)
自动化投资分析 (Automating Investment Analysis)
In the good old days (a few short years ago), analysts would spend countless hours poring over annual reports, industry news, and earnings calls to understand how a company was doing.
在过去几年(几年前)的美好时光中，分析师将花费大量时间研究年度报告，行业新闻和财报电话，以了解公司的运作情况。
Nowadays, a branch of AI called Natural Language Processing (NLP) is capable of ‘reading’ these reports, articles, and call transcripts. These tools can then extract insights from annual reports and summarize key findings. Sentiment analysis tools can analyze earnings call transcripts and determine the extent to which management feels positive or negative about the company’s prospects. AI startups such as Alpha Sense provide these tools to institutional investors.
如今，人工智能的一个分支，称为自然语言处理(NLP)，能够“读取”这些报告，文章和通话记录。 然后，这些工具可以从年度报告中提取见解并总结关键发现。 情绪分析工具可以分析收益电话记录，并确定管理层对公司前景的正面或负面程度。 诸如Alpha Sense之类的AI初创公司为机构投资者提供了这些工具。
Similar NLP tools can also be applied to news and social media data, processing massive volumes of data that human analysts can’t hope to match. The good news for analysts is that they are now free to focus on more value added, alpha generating analysis.
类似的NLP工具也可以应用于新闻和社交媒体数据，处理人类分析人员无法匹配的大量数据。 对于分析师而言，好消息是，他们现在可以自由地专注于更多的增值，alpha生成分析。
量身定制的客户报告和按需信息 (Tailored Client Reports & On-Demand Information)
Natural Language Generation (NLG), a technique related to NLP, can automatically generate text-based content from underlying data. Investment managers can use this technique to automate periodic client reports and even serve clients market insights on-demand.
自然语言生成(NLG)是一种与NLP相关的技术，可以根据基础数据自动生成基于文本的内容。 投资经理可以使用这种技术来自动执行定期的客户报告，甚至按需提供客户市场见解。
Bloomberg has been using automated reporting to write up to one-third of its news stories, according to a 2019 New York Times report. Bloomberg is not alone — the NYT report points out that hedge funds also use automated reporting to serve their clients market info.
根据2019年《纽约时报》的报道，彭博社一直在使用自动报告来撰写多达三分之一的新闻报道。 彭博并不孤单-纽约时报(NYT)报告指出，对冲基金还使用自动报告为客户提供市场信息。
Investment managers can automated reporting to cut costs and save time internally. More importantly, providing timely reporting and value added insights to clients will improve client satisfaction and the firm’s reputation.
投资经理可以自动生成报告，以削减成本并节省内部时间。 更重要的是，向客户提供及时的报告和增值见解将提高客户满意度和公司声誉。
识别高价值的AI用例并部署AI解决方案 (Identifying High-Value AI Use Cases & Deploying AI Solutions)
Investment management firms stand to gain considerably from AI adoption. In order to maximize benefits from AI, firms must consider the following:
投资管理公司将从采用AI中获得可观的收益。 为了从AI中获得最大收益，公司必须考虑以下几点：
AI Use Case Identification: What are the most rewarding business opportunities that we can solve with AI? Which AI techniques can we use?
人工智能用例识别：我们可以利用人工智能解决的最有意义的商机是什么？ 我们可以使用哪些AI技术？
AI Prioritization: How should prioritize our AI projects, by use case and time horizon?
AI优先级划分 ：如何根据用例和时间范围划分我们的AI项目优先级？
Acquiring Data: What types of data do we need? Where can we get it?
获取数据 ：我们需要什么类型的数据？ 在哪里可以得到它？
AI Vendor Partnerships: How can we partner with AI startups to co-create AI solutions that are designed for our unique needs?
AI供应商合作伙伴关系 ：我们如何与AI初创公司合作，共同创建满足我们独特需求的AI解决方案？
用例识别 (Use Case Identification)
Identifying AI use cases comes from gaining AI awareness. Investment management senior executives will understand their industry and their client’s needs, but may need an introduction to what AI is capable of. This foundational AI awareness will help executives conceptualize how AI will help the firm and its clients.
识别AI用例来自获得AI意识。 投资管理高级主管将了解他们的行业和客户的需求，但可能需要介绍AI的功能。 这种基本的AI意识将帮助高管概念化AI将如何帮助公司及其客户。
AI awareness & education can come from online courses, corporate training, AI consultants, or AI vendors that have a close relationship with the firm.
AI意识和教育可以来自与公司有密切关系的在线课程，公司培训，AI顾问或AI供应商。
It is especially effective when a trusted consultant or AI vendor comes in and works with employees to identify use cases. By working with the firm’s management and domain experts, AI vendors and consultants can help identify AI use cases for the firm’s most rewarding business opportunities, whether it is risk management, alternative data insights, or automated analysis.
当值得信赖的顾问或AI供应商加入并与员工一起确定用例时，此功能特别有效。 通过与公司的管理和领域专家合作， AI供应商和顾问可以帮助确定公司最有价值的商机的AI用例 ，无论是风险管理，替代数据见解还是自动化分析。
Another benefit to leveraging AI vendors and consultants for use case identification is that they can explain (and maybe provide) the AI methods and tools required to deploy a solution.
利用AI供应商和顾问进行用例识别的另一个好处是，他们可以解释(并可能提供)部署解决方案所需的AI方法和工具。
AI优先级 (AI Prioritization)
Companies can’t roll out multiple AI solutions at once due to time and resource constraints.
由于时间和资源的限制，公司无法一次推出多个AI解决方案。
A good rule of thumb is to knock out ‘quick win’ projects first — smaller projects that will quickly have a measurable impact. For instance, an investment management firm can acquire and deploy an intelligent Robotic Process Automation (RPA) tool that uses AI to automate a routine administrative workflow.
一个好的经验法则是首先淘汰“快速获胜”的项目-较小的项目将很快产生可衡量的影响。 例如，一家投资管理公司可以获取并部署一个智能机器人流程自动化 (RPA)工具，该工具使用AI来使例行管理工作流程自动化。
After gaining momentum and progressing on the AI learning curve, firms can shift to the higher-value AI use cases they identified. It makes sense to split an AI initiative into short/medium/long term projects. That way, costs are controlled, benefits are stacked, and the AI initiative is continuously validated over time.
在获得动力并在AI学习曲线上取得进步后，企业可以转向他们确定的更高价值的AI用例。 将AI计划分为短期/中期/长期项目是有意义的。 这样一来，成本得到控制，收益得以累积，并且AI计划会随着时间的推移不断得到验证。
采集数据 (Acquiring Data)
Much of the data for an investment management firm’s AI projects will be internal data. Still, there is the question of unifying data sources and data cleaning — a non-trivial activity to say the least. Other data will come from financial data vendors such as Bloomberg and Reuters.
投资管理公司的AI项目的许多数据将是内部数据。 尽管如此，仍然存在统一数据源和数据清理的问题，至少可以这样说，这是一项不平凡的活动。 其他数据将来自彭博社和路透社等金融数据供应商。
Alternative data, such as satellite imagery and anonymized geolocation data mentioned above, comes from specialized alternative data vendors. When vetting these vendors, firms must:
替代数据，例如上面提到的卫星图像和匿名地理位置数据，来自专门的替代数据供应商。 审核这些供应商时，公司必须：
- Ensure that the vendor does not use material non-public information. This could put investment managers at risk of insider trading accusations. 确保供应商不使用重要的非公开信息。 这可能会使投资经理人面临内幕交易指控的风险。
- Ensure that you can easily integrate vendor datasets into your AI models 确保您可以轻松地将供应商数据集集成到您的AI模型中
- Check whether the vendor data is tagged using machine learning only, or if humans perform a secondary check to improve tagging accuracy 检查是否仅使用机器学习来标记供应商数据，或者人工进行二次检查以提高标记准确性
- Consider whether the data vendor will still be in business next year (competition is tough)! 考虑一下数据供应商明年是否仍会营业(竞争很艰难)！
AI供应商合作伙伴 (AI Vendor Partnerships)
Building AI is not easy. Only the biggest investment managers can afford to build everything in-house using an internal AI team. While firms should aim for this in the long run (greater knowledge effects, data security, IP protection), many firms still have to get to this level of AI maturity.
构建AI并不容易。 只有最大的投资经理才能负担得起使用内部AI团队进行内部构建的一切。 尽管公司应该从长远来看(更大的知识效果，数据安全性，IP保护)，但许多公司仍然必须达到AI成熟度的水平。
At the same time, just going out any buying a bunch of AI tools won’t work in the long run. Off-the-shelf AI products may not be tailored to your business needs and may not integrate well with your data.
同时，从长远来看，任何花钱买一堆AI工具都是行不通的。 现成的AI产品可能无法满足您的业务需求，并且可能无法与您的数据很好地集成。
A more robust strategy is to partner with trusted AI startups and vendors to co-create AI solutions. Vendors can work with your employees (end users) and your technical & data team to develop a suite of AI tools that work well together, both with each other and with the company’s data.
一个更强大的策略是与受信任的AI初创公司和供应商合作，共同创建AI解决方案。 供应商可以与您的员工(最终用户)以及您的技术和数据团队合作，开发一套可以很好地协同工作的AI工具，这些工具可以相互配合，也可以与公司的数据配合使用。
This strategic partnership helps ensure that AI solutions are built to last. A vendor that is a partner will understand your business objectives over the long term and can upgrade your AI tools as objectives change.
这种战略伙伴关系有助于确保AI解决方案持久耐用。 作为合作伙伴的供应商将长期了解您的业务目标，并可以随着目标的变化而升级您的AI工具。
Finding high-quality AI startups and vendors to partner with is not easy. A prospective startup or vendor should have:
寻找高品质的AI初创公司和供应商来合作并不容易。 潜在的初创公司或供应商应具有：
- A diverse team of machine learning developers, product managers, software engineers, data specialists, and business specialists 机器学习开发人员，产品经理，软件工程师，数据专家和业务专家的多元化团队
- Deep knowledge about your industry 对您的行业的深入了解
- A track record of deploying AI tools across functions and value chains 跨职能和价值链部署AI工具的往绩记录
最后的想法：瞄准规模！ (Final Thoughts: Aim for Scale!)
Deploying AI at scale — so that it improves all major corporate functions — can lead to sustained competitive advantage and investment out-performance.
大规模部署AI(以便改善所有主要公司功能)可以带来持续的竞争优势和投资表现不佳。
Scale matters. Companies don’t invest in AI to look cool. They invest in AI to improve business outcomes and solve problems. These problems, such as investment risk management, are large scale. Therefore, AI solutions must scale accordingly.
规模很重要。 公司不会在AI上投资看起来很酷。 他们投资于AI以改善业务成果并解决问题。 这些问题，例如投资风险管理，规模很大。 因此，AI解决方案必须相应地扩展。
Partnerships will help companies scale their AI initiatives. Partnering with competent AI startups and vendors will help shorten the learning and development curve and result in faster implementation.
合作关系将帮助公司扩展其AI计划。 与有能力的AI初创公司和供应商合作将有助于缩短学习和开发曲线，并加快实施速度。
In the end, each firm’s AI journey is unique. Identify high-value use cases, lay out a plan, pick your partners, and build something that lasts.
最后，每个公司的AI旅程都是独一无二的。 确定高价值的用例，制定计划，选择您的合作伙伴，并建立持久的业务。
翻译自: https://medium.com/swlh/artificial-intelligence-in-investment-management-elevating-client-relationships-and-returns-c0106f7606e8
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