在2016年大选前夕, 一些投票网站, 包括《纽约时报, gave Donald Trump less than a 1% chance of beating Hillary Clinton.
就英国脱欧而言, it was a hard-learned lesson that “surely not” isn’t an entirely accurate way to forecast whether something will occur or not.
还有谁厌倦了等待中国完成崛起?
预测的问题在于，总的来说， 人类是 可怕的 在这. 在任何情况下, 变化和偶然性的绝对程度, the volume of variables and the blindness to our own biases prevent us from making objective forecasts. We fail to see that every second of our lives is Schrödinger’s cat playing out a million and one ways.
Experts have been condemned for making predictions at similar accuracy levels as “玩飞镖的黑猩猩”. 在很大程度上, predictions are harmless and usually provide good dinner table fodder when we really mess up. The danger arises in when we use our flawed predictions to inform decision making.
The Global Financial Crisis evinces the most prominent example of financial institutions impacted by short-sighted predictions, namely on the invincibility of the real estate bubble and the belief that banks were “大到不能倒”. 雷曼兄弟为此付出了最终的代价, and served as a pertinent warning that the experts can be wrong.
被我们的偏见蒙蔽
So why do we think we are better at guessing what’s going to happen than we really are?
当我们猜对某件事时, 我们拍拍自己的背, cherry pick our examples and declare it was always ‘obvious’ it would occur. Yet, tweak any of the variables and a completely different scenario will take place. 诺贝尔奖得主丹尼尔·卡尼曼在书中探讨了这个问题 思考快与慢 how 人类是 excellent at pinpointing random incidents from the past, 连接他们, and then declaring that whatever eventuated was certain and pre-determined.
We are susceptible to a number of biases and heuristics that prevent us from objectivity and strongly influence our behaviour, 如:
- 确认偏误 – our pesky little habit of selectively choosing information that reinforces our already-formed opinion and ignoring views that argue the opposite;
- 后见之明偏见 – we think we remember the past as more predictable than it was;
- 锚定效应 – a cognitive bias where we assume an unknown quantity is known to us based on something shown to us before;
- 乐观偏见 -我们对某些结果更有希望
- 近因效应”(或近期偏差) – we more easily remember and place greater significance on events that happened more recently;
- 突出的偏见 – being influenced by more information that is more obvious or important to us.
Essentially the problem with human predictions boils down to this: 我们只看到我们想看到的.
That is why the ‘experts’ are shocked when Donald Trump wins his way to the White House, Britain turns its back on its European neighbors and guesses for when China will eclipse the United States as the dominant global power are routinely pushed back.
Computer-powered预测
我们可以, 然而, 开始关注机器, whose deep learning is heralded as a game changer in its ability to forecast future events. 人类在哪里摇摇欲坠, 计算机可以处理大量的数据, process it in almost real-time with an objective* set of rules and pop out patterns and insights that significantly reduce the human biases that have plagued our previous predictions.
在过去的五年里，增长 数量驱动型投资呈指数级增长, reaching almost USD$1 trillion (with a ‘t’) worth of assets last year. Algorithmic-powered investment strategies are proving to be both more reliable and significantly faster than traditional market analysis. Even Bloomberg news outlet is moving towards utilising machine learning to deliver more accurate and nuanced market predictions with its new 羊驼预测AI预测矩阵.
在更微观的层面上, machine learning offers banks the ability to predict in far more nuanced ways that can 保护金融机构 防范传统贷款风险. 通过处理大量的数据来识别模式, algorithms can be used to spot an account closure before it happens, 预测客户拖欠贷款的可能性, and even determine the habits of a customer to make accurate guesses at where they will spend in the future. 添加, machine learning used to decipher payment patterns and anomalies could revolutionise financial crime protection.
Removing human interference and relying on quantitative evidence to make better informed decisions is critical to mitigate the traditional risks faced by FIs. When it comes to transforming the financial services industry, the application of machine learning and AI technology is only starting to come to fruition.
I won’t be factitious enough to predict how it will look in 10 years, but I will say I am excited to explore the myriad applications of machine learning in posts to come.
*The term ‘objective’ here comes with a caveat – while machines can apply rules without many of the biases that plague humans, the data used does not speak for itself but is given meaning by humans and programmed by humans. 在以后的文章中会有更多相关内容!
