Machine-learning in Finance: Unshackled Algorithms
金融领域的机器学习：算法冲破疆界
More firms are experimenting with artificial intelligence
越来越多的公司在尝试人工智能
Machine-learning is beginning to shake up finance. A subset of artificial intelligence (AI) that excelsat finding patterns and making predictions, it used to be the preserve of technology firms. The financial industry has jumped on the bandwagon. To cite just a few examples, “heads of machine-learning” can be found at PwC, a consultancy and auditing firm, at JP Morgan Chase, a large bank, and at Man GLG, a hedge-fund manager. From 2019, anyone seeking to become a “chartered financial analyst”, a sought-after distinction in the industry, will need AI expertise to pass his exams.
机器学习已开始震撼金融界。作为人工智能的一个子集，机器学习擅长发现规律并作出预判，这在以前一直是科技公司的专属技术。如今金融业也赶上了这趟风潮。只举几个例子：咨询审计公司普华永道、大型银行、摩根大通和对冲基金管理公司Man GLG都设有“机器学习主管”。自2019年起，如果想要获得业内广受追捧的资质、成为“特许金融分析师”，必须掌握AI专业知识才能通过考试。
Despite the scepticism of many, including, surprisingly, some “quant” hedge funds that specialise in algorithm-based trading, machine-learning is poised to have a big impact. Innovative fintech firms and a few nimble incumbents have started applying the technique to everything from fraud protection to finding new trading strategies – promising to up-end not just the humdrum drudgery of the back-office, but the more glamorous stuff up-front.
尽管许多人表示怀疑，其中甚至包括一些专门根据算法来进行交易的“量化”对冲基金，但机器学习势必将产生巨大的影响。创新的金融科技公司和一些灵敏的传统金融企业已开始将这一技术应用到方方面面，例如防欺诈和寻找新的交易策略等。这不仅有望颠覆单调乏味的后台苦差，还将深刻影响更为风光的前台部门。
Machine-learning is already much used for tasks such as compliance, risk management and fraud prevention. Intelligent Voice, a British firm, sells its machine-learning-driven speech-transcription tool to large banks to monitor traders’ phone calls for signs of wrongdoing, such as insider trading. Other specialists, like Xcelerit or Kinetica, offer banks and investment firms near-real-time tracking of their risk exposures, allowing them to monitor their capital requirements at all times.
机器学习已经广泛应用于合规、风险管理和预防欺诈等工作。英国公司智能语音（Intelligent Voice）向大银行出售基于机器学习的语音转录工具，可用来监控交易员的电话，以发现内幕交易等不正当行为的迹象。其他的专业公司如Xcelerit或Kinetica向银行和投资公司提供接近实时的风险敞口跟踪，让它们能随时监控自己的资本要求。
Machine-learning excels in spotting unusual patterns of transactions, which can indicate fraud. Firms ranging from startups such as Feedzai (for payments) or Shift Technology (for insurance) to behemoths such as IBM are offering such services. Some are developing the skills in-house. Monzo, a British banking startup, built a model quick enough to stop would-be fraudsters from completing a transaction, bringing the fraud rate on its pre-paid cards down from 0.85% in June 2016 to less than 0.1% by January 2017.
机器学习擅于发现不寻常的交易模式，而这种异常背后可能存在欺诈。从Feedzai（面向支付业务）或Shift Technology（面向保险业务）这类创业公司，到IBM这样的巨头都在提供此类服务。有些企业正在自行研发这样的技术。英国银行业创业公司Monzo建立了一个模型，能够及时阻止诈骗嫌疑人完成交易，这令它的预付费卡欺诈率从2016年6月的0.85%降至2017年1月的不到0.1%。
Natural-language processing, where AI-based systems are unleashed on text, is starting to have a big impact in document-heavy parts of finance. In June 2016 JPMorgan Chase deployed software that can sift through 12,000 commercial-loan contracts in seconds, compared with the 360,000 hours it used to take lawyers and loan officers to review the contracts.
自然语言处理将人工智能系统的威力释放到文字上，开始对金融业繁重的文书工作产生巨大影响。2016年6月，摩根大通部署了每秒能筛查1.2万份商业贷款合同的软件。换在过去，审查这些合同要耗费律师和信贷员36万个小时。
Machine-learning is also good at automating financial decisions, whether assessing creditworthiness or eligibility for an insurance policy. Zest Finance has been in the business of automated credit-scoring since its founding in 2009. Earlier this year it rolled out a machine-learning underwriting tool to help lenders make credit decisions, even for people with little conventional credit-scoring information. It sifts through vast amounts of data, such as people’s payment history or how they interact with a lender’s website. Lemonade, a tech-savvy insurance startup, is using machine-learning both to sell insurance policies and to manage claims.
机器学习还擅长自动化金融决策，无论是评估信用还是保单的投保资格。Zest Finance自2009年成立后就从事自动化信用评分业务。今年早些时候，该公司推出了一款机器学习信贷审核工具，可帮助贷款方做出信贷决策，甚至可以决定是否贷款给那些几乎没有传统信用评分信息的人。该工具筛查大量的数据，如这些人的付款历史，或是他们与贷款方网站的互动。精通科技的保险创业公司Lemonade既利用机器学习来卖保险，也用它来管理索赔。
Perhaps the newest frontier for machine-learning is in trading, where it is used both to crunch market data and to select and trade portfolios of securities. The quantitative-investment strategies division at Goldman Sachs uses language processing driven by machine-learning to go through thousands of analysts’ reports on companies. It compiles an aggregate “sentiment score” based on the balance of positive to negative words. This score is then used to help pick stocks. Goldman has also invested in Kensho, a startup that uses machine-learning to predict how events like natural disasters will affect market prices, based on data on similar events.
机器学习的最新应用领域可能是在交易上，人们用它来分析市场数据，以及选择并交易证券投资组合。高盛的量化投资部门用机器学习驱动的语言处理技术来通读分析师撰写的成千上万份公司研报。该技术根据积极言论和消极言论的数量对比，编制出一个综合的“情绪评分”，然后用这一评分来帮助选择股票。高盛还投资了Kensho，这家创业公司根据自然灾害等事件的有关数据，用机器学习预测这些事件将如何影响市场价格。
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