In January 2020, the Cambridge Centre for Alternative Finance (CCAF) released a study on the impact of AI in the finance industry. Known as one of the most comprehensive global surveys in this domain, it comprised around 151 respondents from 33 countries, including incumbent financial institutions and FinTech firms. The study came up with the following findings:

2020 年 1月，剑桥替代金融中心(CCAF)发布了一项有关人工智能对金融业影响的研究。 作为该领域最全面的全球调查之一，它包含来自33个国家/地区的151位受访者，其中包括现有的金融机构和金融科技公司。 该研究得出以下发现：

At least 77% of the respondents believe that AI bears high importance to their organization in the next couple of years.

至少77％的受访者认为，人工智能在未来几年将对其组织高度重视。

Almost 64% of the respondents intend to earn revenue through AI via client acquisition, customer service, risk management, process automation, and new products.

几乎64％的受访者打算通过客户获取，客户服务，风险管理，流程自动化和新产品通过AI赚钱。

At the moment, AI is widely used in risk management, having an implementation rate of 56% among firms.

目前，人工智能已广泛应用于风险管理中，在公司中的实现率为56％。

Traditionally, HFT firms and hedge funds were the primary AI practitioners in the finance sector, but lately, FinTech companies, insurance firms, banks, and regulators are also catching up.

传统上，HFT公司和对冲基金是金融领域的主要AI实施者，但是最近，FinTech公司，保险公司，银行和监管机构也正在追赶。

In this industry, some of the AI uses include Robo-advisors, backtesting, model validation, portfolio composition and optimization, stress testing, algorithmic trading, and regulatory compliance. Let’s find out more about AI applications in finance.

在这个行业中，一些AI用途包括机器人顾问，回测，模型验证，投资组合构成和优化，压力测试，算法交易和法规遵从性。 让我们进一步了解金融中的AI应用程序。

1.风险管理 (1. Risk management)

AI and machine learning algorithms are gradually revolutionizing financial risk management. AI-driven solutions are providing insights on:

人工智能和机器学习算法正在逐步革新金融风险管理。 人工智能驱动的解决方案可提供以下方面的见解：

Determining the loan amount to a customer.

确定对客户的贷款额。

Generating warning alerts to traders regarding position risk.

向交易员生成有关仓位风险的警告警报。

Enhancing compliance and limiting model risk.

增强合规性并限制模型风险。

To understand why the respondents in the CCAF study listed risk management as their primary focus in the implementation of AI, consider the case of Baidu.

要了解为什么CCAF研究中的受访者将风险管理作为实现AI的主要重点，请考虑百度的情况。

The most prominent search engine in China is Baidu. (since Google is banned there). In 2016, Baidu sought the assistance of ZestFinance — a US-based FinTech company specializing in AI products. Baidu’s objective was to provide small loan offers to retail customers who bought products from its platform.

中国最著名的搜索引擎是百度。 (因为Google在那里被禁止)。 2016年， 百度寻求ZestFinance的帮助-ZestFinance是一家总部位于美国的专注于AI产品的金融科技公司。 百度的目标是向从其平台购买产品的零售客户提供小额贷款优惠。

However, the Chinese lending landscape is in stark contrast to the Western markets — the lending risk in former is considerably high as more than 80% of people don’t have any credit profile or credit rating. Hence, there is no existing method to determine borrower reliability.

但是，中国的贷款形势与西方市场形成鲜明对比-前者的贷款风险相当高，因为超过80％的人没有任何信用状况或信用评级。 因此，没有确定借款人可靠性的现有方法。

ZestFinance tackled this issue by analyzing Baidu’s massive customer datasets, particularly the search and buying histories. In this way, they employed AI to assist Baidu in deciding whether to lend to a customer or not. By 2017, a survey found that Baidu experienced a 150% increase in small-item lending without any noticeable credit losses.

ZestFinance通过分析百度的大量客户数据集，尤其是搜索和购买历史记录，解决了这一问题。 通过这种方式，他们利用AI协助百度决定是否借给客户。 到2017年， 一项调查发现 ，百度小额贷款增长了150％，而没有任何明显的信贷损失。

Since ZestFinance processes financial data through proprietary technology, the complete detail of their AI solution is unknown. However, it’s common knowledge that their process uses a blend of two machine learning algorithms: decision trees and clustering.

由于ZestFinance通过专有技术处理财务数据，因此未知其AI解决方案的完整细节。 但是，众所周知，他们的流程结合了两种机器学习算法：决策树和聚类。

For instance, if a customer’s search history indicates extensive visits to gambling websites, they would be grouped in a cluster associated with higher risk. On the other hand, if a borrower is responsible for online spending, they would be categorized as having low-risk borrowers. With automation, it would be quite easy for Baidu’s financial staff to review these applications and approve loans to people according to their risks.

例如，如果客户的搜索历史记录表明对赌博网站的广泛访问，则将他们分组到与较高风险相关的集群中。 另一方面，如果借款人负责在线支出，则将他们归类为低风险借款人。 借助自动化，百度的财务人员可以很容易地审查这些应用程序并根据人们的风险批准贷款。

2.算法交易 (2. Algorithmic trading)

For an extended period, investment firms used computers to make trades. A large number of hedge funds rely on data scientists to build statistical models. But, there’s a significant limitation with the approach — it only uses historical data, which is mostly static and depends on human intervention. Hence, these computations struggle as the market undergoes any change.

长期以来，投资公司使用计算机进行交易。 大量对冲基金依靠数据科学家来建立统计模型。 但是，该方法存在很大的局限性-它仅使用历史数据，该数据大部分是静态的，并取决于人工干预。 因此，随着市场发生任何变化，这些计算都很困难。

Luckily, modern AI models have made rapid strides through algorithmic trading. These models are different because they don’t only analyze large amounts of data, but are genuinely autonomous — they learn and improve over time, reaching a point where they can rival humans. This “smartness” derives from sophisticated machine learning techniques, such as evolutionary computation (based on genetics) and Bayesian networks.

幸运的是，现代的AI模型通过算法交易取得了长足的进步。 这些模型之所以不同，是因为它们不仅分析大量数据，而且具有真正的自主性-它们会随着时间的推移而学习和改进，从而达到可以与人类抗衡的地步。 这种“智能”源自复杂的机器学习技术，例如进化计算(基于遗传)和贝叶斯网络。

AI tools collect voluminous amounts of data from global sources, “learn” from it, and make predictions accordingly. This data consumption is exhaustive; it extracts information from financial exchanges, news reports, books, social media platforms (e.g., tweets), and even TV shows like Saturday Night Live.

人工智能工具从全球资源中收集大量数据，从中“学习”，并做出相应的预测。 这种数据消耗是详尽无遗的。 它从金融交易，新闻报道，书籍，社交媒体平台(例如，推文)甚至电视节目(如Saturday Night Live )中提取信息。

What’s important is to understand how AI has made deep inroads in this domain; unlike the traditional technological intervention that allows humans to decide the financial strategy, AI is now dictating the game.

重要的是要了解AI如何在这一领域取得长足的进步。 与传统的技术干预技术(它可以由人类决定财务策略)不同，人工智能现在可以指导游戏。

One example of these AI-powered trading systems is Aidiya — a Hong Kong-based AI hedge fund that allows users to make all stock trades via AI. It’s worth noting that startups aren’t the only ones interested in AI trading technology. Earlier, prominent names, such as Goldman Sachs, Wells Fargo, Citigroup, Morgan Stanley, Merrill Lynch, Bank of America, and JP Morgan Chase, took an active interest in Kensho — an AI trading platform.

这些以人工智能为动力的交易系统的一个例子是Aidiya ，这是一家总部位于香港的AI对冲基金，允许用户通过AI进行所有股票交易。 值得注意的是，并非只有初创公司对AI交易技术感兴趣。 此前，著名的名字 ，如高盛，富国银行，花旗集团，摩根士丹利，美林，美国银行和摩根大通，采取了积极关注Kensho计划-一个AI的交易平台。

3.欺诈检测 (3. Fraud detection)

Another application of AI in finance that is rapidly advancing is fraud detection, which is understandable considering the vast sums of money. The cybercrime industry steals around $600 billion or 0.8% of the global GDP from businesses around the world. Cybercriminals have become more sophisticated and smart, leveraging modern technology for nefarious purposes. According to Statista, the comprehensive fraud detection and prevention market expects to grow by more than $40 billion by 2022.

人工智能在金融领域的另一个快速发展的应用是欺诈检测，考虑到巨额资金，这是可以理解的。 网络犯罪行业从世界各地的企业中窃取了约6000亿美元 ，占全球GDP的0.8％。 利用犯罪分子利用现代技术，网络犯罪分子变得更加精明和聪明。 Statista的数据显示，到2022年，综合欺诈检测和预防市场预计将增长超过400亿美元。

So, how can AI help? Tackling the skill, modern machine learning cybercriminals can use a blend of supervised and independent techniques to build a model with predictive accuracy and capability.

那么，人工智能如何提供帮助？ 为了掌握这项技能，现代机器学习网络犯罪分子可以结合使用监督和独立技术来构建具有预测准确性和能力的模型。

Supervised learning utilizes annotated data — which humans assess and identify as fraud activity — and learn intricate patterns from corporate datasets. Meanwhile, unsupervised learning processes deal with those datasets that are not identified before and infer data structure by itself. Other fraud detection techniques include regression and classification. They can analyze data and determine whether the transaction is fraudulent or not.

监督学习利用带注释的数据(人类将其评估并识别为欺诈活动)，并从公司数据集中学习复杂的模式。 同时，无监督的学习过程将处理那些以前未识别的数据集，并自行推断数据结构。 其他欺诈检测技术包括回归和分类。 他们可以分析数据并确定交易是否欺诈。

The standard supervised algorithms used for addressing these issues include the following:

用于解决这些问题的标准监督算法包括以下内容：

· Decision Trees help to introduce a set of rules that learn normal customer behavior while being trained with fraud instances so that they can identify anomalies and alert authorities.

· 决策树有助于引入一组规则，这些规则可以在受欺诈实例训练的同时学习正常的客户行为，以便它们可以识别异常并警告权限。

· Neural Networks based on the human brain can learn and adapt to customer behavior to detect real-time fraud.

·基于人脑的神经网络可以学习并适应客户的行为，以检测实时欺诈。

Examples of unsupervised learning algorithms include the following:

无监督学习算法的示例包括：

· K-means Clustering splits a dataset into a batch of similar data points, known as a cluster, for anomaly detection.

· K-均值聚类将数据集拆分为一批类似的数据点(称为聚类)，用于异常检测。

· Local Outlier Factor determines the local density of data points, identifying areas where similar density exists. Data scientists can use locality concept to mark the end having unusually lower density, known as outliers. This application can come in handy to detect fraudulent transactions.

· 局部异常值因子确定数据点的局部密度，从而确定存在相似密度的区域。 数据科学家可以使用局部性概念来标记具有异常低密度的末端，称为离群值。 此应用程序可用于检测欺诈性交易。

4. RegTech (4. RegTech)

Regulatory compliance is a vital function in finance, particularly during an economic crisis like the current one. Compliance is associated with enterprise risk management and deals with risk functions, such as operational, market, and credit risks.

遵守法规是金融的一项重要职能，特别是在像当前这样的经济危机中。 法规遵从与企业风险管理相关联，并处理风险功能，例如运营，市场和信用风险。

RegTech is an advanced function of the FinTech domain focused on compliance. Here, AI’s advantage taken when used for continuous monitoring of a firm’s activities. This way, it offers valuable real-time insights and prevents compliance breaches from incurring in the first place. Moreover, this form of monitoring allows firms to free up regulatory capital and leverage automation for decreasing the excessive compliance costs — major financial firms spend $70 billion on compliance every year.

RegTech是金融科技领域的高级功能，专注于合规性。 在这里，人工智能在持续监控公司活动中的优势。 这样，它可以提供宝贵的实时见解，并从一开始就防止违反法规的行为。 此外，这种形式的监控使公司可以释放监管资本，并利用自动化来降低过多的合规成本，大型金融公司每年在合规方面的支出为700亿美元。

A well-known player in this field is IBM. A while back, IBM acquired Promontory — a RegTech startup consisting of 600 employees. This acquisition has led IBM to promote a multitude of AI-powered solutions for managing financial compliance. For instance, IBM is using its proprietary AI tool, Watson AI, with Promontory’s RegTech expertise to deploy real-time voice conversation analysis for ensuring compliance. Part of this includes the translation of voice-based conversations to text and then using natural language processing for text classification. The aftermath of this process is the formation of categories that detects potential non-compliance.

该领域的知名公司是IBM。 不久前，IBM收购了Promontory，这是一家由600名员工组成的RegTech创业公司。 此次收购使IBM推动了众多基于AI的解决方案来管理财务合规性。 例如，IBM正在将其专有的AI工具Watson AI与Promontory的RegTech专业知识配合使用，以部署实时语音对话分析以确保合规性。 其中一部分包括将基于语音的对话翻译为文本，然后使用自然语言处理进行文本分类。 此过程的后果是形成了检测潜在违规的类别。

Other AI applications include the automated reading and interpretation of the regulatory documentation, especially for determining implications. Waymark, a London-based company, is already providing this service to financial firms.

其他AI应用程序包括自动阅读和解释法规文件，特别是用于确定含义。 总部位于伦敦的Waymark公司已经在向金融公司提供这项服务。

最后的想法 (Final thoughts)

Although there are numerous other applications of AI in finance, there’s a flip side as well. The industry needs to rectify practical issues to enhance AI implementation.

尽管人工智能在金融领域还有许多其他应用，但也有另一面。 业界需要纠正实际问题以增强AI实施。

One of the biggest concerns remains the availability of suitable data. Even though R and Python can read any form of data from Excel spreadsheets to SQL/NoSQL datasets, the pace at which AI-driven solutions function is slower than the organizations’ ability to organize their internal data accurately. Typically, data is stored in separate silos in various departments, and often in different systems, where regulatory and internal political dilemmas limit information sharing.

最大的担忧之一仍然是合适数据的可用性。 尽管R和Python可以从Excel电子表格到SQL / NoSQL数据集读取任何形式的数据，但AI驱动的解决方案运行的速度比组织准确组织其内部数据的能力要慢。 通常，数据存储在各个部门的不同仓库中，通常存储在不同的系统中，在这些系统中，监管和内部政治困境限制了信息共享。

On a similar note, another predicament is the lack of skilled staff who not only commands an expert-level knowledge of AI, machine learning, and data science, but also have experience in building and implementing AI-centric solutions in the finance industry.

同样，另一个困境是缺乏熟练的员工，他们不仅具有AI，机器学习和数据科学方面的专家级知识，而且在金融行业中构建和实施以AI为中心的解决方案方面具有丰富的经验。

“Artificial Intelligence will reach human levels by around 2029. Follow that out further to say, 2045, we will have multiplied the intelligence , the human biological machine intelligence of our civilization a billion-fold.”

“人工智能将在2029年左右达到人类的水平。再进一步说，到2045年，我们将把我们的文明的智能(人类生物机器智能)增加十亿倍。”

-Ray Kurzweil-

雷·库兹韦尔