金融已经使用了人工智能了一段时间，但是现在该技术的作用正在迅速发展。
Ten things you should know
1. AI in finance is not new
几十年前，金融部门开始使用智能算法。早在1980年代。在那个阶段，他们进行了统计和数学计算，在选项定价等领域进行了猜测。但是，改变的是，我们现在拥有更多的数据和更大的计算能力。这可以用于训练算法，以逗弄人类发现非常硬或昂贵的数据的微妙模式和微小的变化（就人力而言）识别。计算机可以同时在100个市场上交易模型，或者可以非常快速地分析数据并每隔几秒钟做出决定，当新信息到来时，它将再次更新。
2. AI主要增加人类决策；它不能取代它
Except in areas such as very high frequency trading – where relatively simple models are used to make thousands of deals in seconds – the main role of AI currently is to provide more knowledge that can help finance professionals make much better judgements. Algorithms can process large amounts of data at a scale that individual human beings, no matter how smart, cannot match. But they don’t have the same deep understanding of markets and human behaviour as people do: that’s why both are needed.
3. AI can help remove emotions from trading decisions
算法交易创建了一组规则，可以消除对突然市场转移的情感反应，或者其他可能吓到或激发人类的东西。只有在合适的机会展示自己时，这可能是一种纪律处分的方式。例如，某些算法交易所谓的“动量”。他们确定了强大的市场转移，然后推测将需要一些时间才能“波浪”中断。这意味着他们可以采取正确的位置，然后等到另一个数学指示器信号表明波浪即将降低。
4.新的和令人惊讶的数据源正在发挥作用
The remit of data acquisition is expanding beyond the financial sphere as people notice that, for example, there may be relationships (even if weak) between sentiments on social media and stock prices. Algorithms can be used to spot patterns and correlations in disparate datasets that can indicate directions in which the markets might travel. These indicators are then used to propose potential trade options to people who can implement them live in markets.
5. New AI-powered services are emerging from non-traditional suppliers
Following the financial crash in 2008-9, the UK regulator, the Financial Conduct Authority (FCA), was given a new brief to make the City of London competitive. The FCA interpreted that as a licence to work with a variety of new startups and to allow different types of transactions. As a result, many companies are now experimenting with AI in areas that are outside traditional banking practice, such as peer-to-peer lending.
6.个人和小型企业正在提供资金
Risk is often based on asymmetric information. For example, if someone applies for a loan they may be certain that they have the income and the will to pay it back. But the bank does not have the same knowledge and certainty, which is why young adults and people new to a country are assessed as bad risks and offered high interest rates. AI, however, can use and analyse data to build models for a variety of different types of people, creating opportunities around borrowing, lending, and measuring risk – making money available to more people on better terms.
7.个人数据的使用继续存在问题
A major insurance company was forced to scrap plans to use AI to analyse the social media posts of car owners to build personality profiles and use that information to set the price of their insurance. In theory, the plan could have helped conscientious younger drivers, for example, who often pay high premiums based solely on their age. However, using social media in this way also breached privacy rules. Companies must think carefully about how they use data.
8.算法很快 - 但有时太快
算法进行计算的速度也可能存在问题。价格的灾难性变化 - 导致闪存崩溃的情况可能如此迅速，以至于人类干预可能为时已晚。
9.算法不知道如果发生不寻常的事情该怎么办
算法训练非常large datasets. Even so, things can happen that are outside that data. At best, the algorithm will effectively say ‘I don’t know what to do about this’; at worst it will confidently make a prediction based on no evidence – and that could be disastrous.
10.我们仍然需要了解更多
We need to understand how algorithms can deal with unusual events. AI systems work by building models and making predictions based on past data. They do not necessarily understand the emergent dynamics of complex systems, such as finance. This is why people within the sector are still taking things slowly and, particularly in financial trading, trying to ensure that risk-management systems are built in. There is currently relatively little regulation and a lot of innovation in this field. It is exciting, but sooner or later there will be a need for regulators to intervene and take stock, to understand what the new risks are and regulate to mitigate them.
了解牛津算法交易计划，由Nir Vulkan教授领导，并与研究合作发展Oxford MAN Institute for Quantitative Finance.
