Machine learning in UK financial services
bankofengland.co.uk
bankofengland.co.uk
Fraud detection is one of the areas I've come across that are actually doing something interesting within retail banking. But agreed - plenty of opportunities for ML to make things more efficient.
The BoE prides itself on being "up" with the latest talking points amongst the chattering classes. But take a look at how the BoE is actually run and you will get an idea why British society struggles with change.
Incompetence and knowledge of irrelevant information is pride of place in British society. Example: Carney has been the most accident prone BoE governor since...the last one, who was trying to raise rates in 2008...the Chief Economist was head of Financial Stability in 2006...yes, seriously (incidentally, he recovered his reputation by being able to hit headlines by talking about new trendy topics like ML).
Where ML has been implemented in the UK it is often by managers who have almost no competence in any real-world skills. They have heard about ML, think it is magic, and want to look like they know what they are doing. This has resulted in very bad outcomes (a recent story - https://www.theguardian.com/society/2019/oct/15/councils-usi...).
I don't think this effect is at all well understood (economists, for example, tend to assume that the person in charge actually knows what they are doing...despite all the research suggesting the contrary in the UK). Insurance has done well because there is a good level of statistical knowledge amongst managers. But I suspect the UK will continue to lag the world (as it does in almost everything else) as the hype disappears, and the typical British suspicion of new technology and change returns.
Of course if the project lasts long enough the software being reasonably good starts to matter as you start finding out things you promised people don’t work anymore, your huge rats nest of code makes it too hard to add features like, etc. But until it hits the “power point bottom line” a non technically educated manager will not care at all.
This has changed recently, as investment returns have dropped, and may also be driving the adoption of better statistical techniques for modelling risk (as they can't rely on investment income to make up for insurance losses).
Also, having read this report, I'm very very sceptical about whether or not companies are using "ML". I suspect that most of them are just doing linear/logistic regression on larger data sizes, which isn't really the same.
But then, our disagreement here just highlights the difficulties involved in getting consensus on what ml vs statistics actually are.
Xiao-Li Meng has had some interesting talks/papers about this and related ideas: https://dash.harvard.edu/handle/1/10886849
But if you use GEE, it's probably great to know.
Many ML techniques are statistical techniques, not ML. If we go down this road.
But apparently, this is a controversial view.
Perhaps not all, but most of the distinctions between "statistical" techniques and "ML" techniques are fairly arbitrary.
Especially if they share the same trait of being so complex and dependent on Byzantine treatment of data that they need to be regulated and governed in a different way from something that can be groked via a single ppt slide?
edited for the second clause.
I hadn’t considered that. It sounds like another reason why insurance is broken. If you have two options, $100 premiums and $100 cost; versus $1000 premiums and $1000 cost; the net gain on premiums vs cost is the same, but the investment income over the interim will be higher when the total amount of money in the system is higher.
I would be interested to see the average premium price compared to total investment funds across different geographies and times
Searching for this gives articles about how the insurance companies actually have returned a fair bit of money, and none with hard numbers about this actually driving healthcare costs up, But there could be a lot of reasons other than "it hasn't driven costs up" for why it's not showing up in a quick search.
Whether or not this is a good idea is moot: this is in the bowels of the bank and not the bleeding edge of commerce.
These terms are meaningless in both ML and finance.
Perhaps, vetted and peer-reviewed on the one hand, and person on the commute bus with a pop-topic book on the other.. there has to be some awareness of that spectrum. With no indication of that awareness here, it just does not bode well.
It’s not clear where an ML expert who isn’t already familiar with the data and the business fits in or adds value, or even what an expert really means in industry.
But you've also succinctly described why a huge percentage of ML as practiced in industry under performs or flat out fails. To a first approximation the person who reads a few tutorials and plays with example data sets, then sets out to apply it to their own domain, has no real idea what they are doing - and it shows.
Agree with your point about domain knowledge being important.
[0] https://news.efinancialcareers.com/uk-en/3002418/machine-lea...
I have several friends who are machine learning practitioners. Many are actively looking to get out. When you strip away the hype and BS the demand in London is very thin.
Edited to add: this isn't a diss on them or this phenomenon in general -- it keeps me employed :)
From my experience, a large part of the demand also needs to be driven by the AI and ML execs' ability to sell to the business.
If you go to grad school in CS and work hard in the right subfield you might develop the understanding of someone with part of a stats BS. Again, the part you would actually know something about is computation, which as far as I can tell isn’t a real bottleneck for anything except very large automated systems. Otherwise some guy can just use R and his laptop.
I mentioned my status above because this is solely based on me evaluating things in an academic setting and not job experience, but this seems very clear to me once laid out like above.
ML is “hot” but it seems to me like an extraordinarily specialized area of expertise with little general applicability.
The point of view in the post here is from an individual, regarding the work of another (hypothetical) individual, while the paper linked spends quite a lot of effort to characterize in the real world, real activity by large and very large, organizations, mainly business, and mainly the business of money.
You conclude that the field has "little general applicability" and that might be true in some sense.. but large organizations, and particularly large finance organizations, are not general at all, but rather composed of a very large number of specialized and repetitive operations.. exactly what ML and AI perform, no?
Next, I'm reminded of when Elon Musk talked about integrated AI, and how we may one day end up in a situation where any type of intellectual or physical competition between humans, in the labor market or elsewhere, may just come down to how much money they have to spend on cybernetic, biological enhancement products.
My synthesis here is that from my perspective, the stock market has since the advent of digital trading tools, now vastly accelerated by expanded usage of machine learning in financial markets, completely undermined the original benefit of the stock market, where a pretty true democratization of the economy, almost an incredibly true one considering it was born in a capitalist society, has now been rolled back and undermined by "big capital" finding a tool that allows them to rig the game to their favor.
So if the assumption is correct that machine learning in financial markets essentially is just an interface for the already-rich to beat the never-rich by throwing more money at the problem, doesn't it stand to reason that these types of tools should be heavily regulated, if we want a stock market that has any resemblance of fairness and democratic influence on the economy?
I understand very well we are far away from this today, and I understand that rich people will not allow regulations that punish them, but if we go beyond the knee-jerk reaction of "cash rules baby", is this really the way things should be?
1. The prevalence if machine learning in trading. 2. That the game wasn't rigged against retail at a point in the past.
First, there is very little (almost none) sophisticated ML in trading. Sure, if you call linear regression or simple classification tools ML, then yes, there's plenty of ML in finance, and has been for decades. If you think that there are super sophisticated DNNs running the majority of trading today, you are very far from the truth. The vast majority of trading is based on fairly simple models. It happens mostly based on human insight and exploiting specific market flows. Sure, almost every firm has some token ML overlay for marketing purposes because it was the fad of the recent years. They all want the public to think that they have some super secret sophisticated AI that gives then an edge. It's all a smoke screen. Almost none of them mean it in earnest. In truth, it is incredibly difficult to build anything resembling a scalable and robust strategy with complex ML tools. The reasons are too numerous to describe here. a very small handful of firms have the capability and mandate to actually use these tools with success, but their market participation is minute, basically negligible.
Second, the game was always rigged in a sense. Big money could always buy privileged access to liquidity, information and tools. Nothing has changed here. If anything, the commoditization of all three of those things has lowered the barrier of entry. You can run a successful quant fund with a couple of friends now. That was much more difficult before.
Very rarely have individual investors from the "masses" done well out of the markets. If they did, they were usually upper-middle-class to start with. Until the 80s and the internet age there were distinct barriers to entry. What did happen was fund investment, at one remove - especially pensions, but also products like life assurance and annuities. The funds are one of the groups deploying machine learning now.
> if we want a stock market that has any resemblance of fairness and democratic influence on the economy?
I'm not sure that's the correct place to deploy the fairness levers against overall economic inequality. We come back to the need for a wealth tax to address dangerously large concentrations of capital destabilising the economy like cargo shifting in a plane.
But when speaking about very advanced tools that could, in theory, give unprecedented advantage to few rich people it would be in the best interest of governments to invent tax schemes that would redistribute their earnings more evenly. On the same note i think countries ought to implement digital taxes in the near future to not to become digital colonies where the internet companies have no employees and pay their taxes elsewhere, eg ireland. With margins as high as they are for digital goods where it's all ones and zeros traveling through cables, to support their own economy and skilled workforce something has to be done.
It's already a problem where the rich can make their money in some country but pay their taxes to tax havens. When the companies can employ the same thing without even investing anything in the countries they are operating it seems the playground is uneven for a lot of poorer players.
As for the super-rich, they have asset managers who pool family funds. Even a small family fund will have millions invested and a small asset manager will have billions under management (pick your own major currency). Every millionaire will be doing the same thing so none of them get an advantage and going against the herd would be unlikely to pay off, short to mid-term (again, my reading of "The Undercover Economist").
Of interest as well may be London Fintech week July 4 2020