Machine Learning in Finance
How AI is used for trading, fraud detection, insurance & personalised banking
There is a lot to be gained for finance businesses in applying AI. And - in fact - plenty are already doing so successfully. To give you an idea - here’s a summary of the top applications of AI in Finance.
AI for trading
We can’t talk about Machine Learning in finance without talking about Machine Learning for trading. In fact, there is so much to talk about that we dedicated a whole article to Machine Learning for Trading
I summarised most important points here:
AI doesn’t replace traders – It helps them
Traders need to process a very large amount of information – to stay up to date with what’s happening and develop informed hunches about where things are going.
They also conduct rigorous statistical analyses to decipher the details of small-market inefficiencies.
All of this takes time – and since trading is a competitive game, the faster you are, the more of the pie you get.
AI helps make traders faster and more accurate in their analyses by summarising news (e.g., with sentiment analysis), improving predictions of external factors (such as rainfall, commodity supply volumes, or election outcomes), and automating the fine-tuning of high-frequency trading machines.
Automated & profitable AI trading is mostly a fantasy
AI is still much weaker than human brains when it comes to making decisions in complex systems where you compete against other human beings - like you do in stock markets – so be wary of anyone that promises a profit based on pure-play AI systems.
There are lots of common mistakes that make it seem like this is possible – when in fact it isn’t.
For more details about why this is the case – and learn more about the ways you can use AI instead – check out our in-depth article on Machine Learning for Trading
Making portfolio management better & cheaper
Portfolio managers tell you where to invest your money – depending on your financial goals and the market fluctuations you can deal with (your risk tolerance).
They pick different types of investments and arrange a portfolio (a list of assets plus the percentage of your money that should go into each one). If the market changes – which it constantly does – this portfolio needs to be recalibrated by moving your money around between assets.
For a human being, this is a time-intensive process – so banks charge a lot for this service and only offer it to their wealthy clients. But good investment advice helps everyone, no matter how large your savings are.
That’s why there’s been a rapid rise in “robo-advisors.” Not only are they very cheap – they actually do their job better than more expensive, human portfolio managers.
Why? Because many human advisors can’t stop themselves from promising their clients superior returns via “secret tips” – which, in an efficient market, is always an illusion, and hence a bad idea. These “tips” end up adding a lot of risk to your portfolio – and might wipe out a big chunk of your savings. (I’ve seen this happen a couple of times – and it’s not pretty.)
The secret of why AI is so useful in portfolio management is that its almost purely a statistical process. Every factor that goes into determining your ideal asset mix (portfolio) can easily be expressed in numbers: your age, income, family status, when you’ll need money (for a house, kids, education, etc.), and your risk tolerance – all easy to enter as numbers in a database.
Based on this data, AI can predict your preferences (like when you’re likely to need how much money). Once you have this information, then the correct allocation of your money is simply a matter of solving a mathematical formula – which is a simple task for any computer.
Want to learn more about the theory behind statistical asset allocation? – Check out Modern Portfolio Theory.
Strengthening fraud detection
With increasing digital commerce (through credit cards and online payments), the incentive to commit fraud is also constantly increasing.
Traditional fraud models rely on simple rule systems – checklists designed to catch fraudsters. The biggest drawback of these simple means of identifying fraud is that they produce a lot of false alarms. Which means legitimate customers are denied purchase transactions – and they’re rightfully upset, and will go and buy somewhere else.
Machine Learning can produce more accurate fraud-detection checklists while also reducing false alarms. Why are AI models better? These are the Top 3 Reasons:
- Data driven: Machine Learning models go through all the examples of fraudulent purchases and – free of human bias – find the exact patterns that differentiate fraudsters from customers.
- More complex: Machine Learning models can easily capture thousands of small patterns and use them to check a transaction – something that isn’t realistic if you’re writing a checklist by hand. This means the models are more accurate and produce fewer false alarms.
- Continually updating: Fraudsters are inventive and constantly try new things to trick the system. Once you’ve found fraud that your system hasn’t caught yet, it only takes a few minutes to update the model so it learns how to spot the new trick from now on. The great thing is: it doesn’t matter whether you’re fighting one inventive fraudster or thousands – an AI system can learn all their patterns just as easily, with no need to cut corners or generalise.
Fraud systems are mostly side-kicks, like algorithms in trading: a human being still needs to do the final check on all the transactions tagged as fraud. Why? Because 1-1, humans are still better at reading other humans’ behaviour – Machine Learning just makes our work easier.
Want to learn more? We wrote an article about using AI to better detect Mobile Click Fraud.
Making insurance underwriting more accurate & personal
Similar to portfolio management (above), insurance underwriting is a job with a clear set of quantified factors (inputs), quantified goals, and fixed outputs: the price of the insurance and its premiums over time.
This is the ideal environment for automated statistical decision-making* – meaning: Machine Learning.
For years, underwriters have already been using simpler statistical models to estimate the underwriting risk and decide on the right premiums.
Machine Learning models can go further and make almost the entire underwriting job cheap and scalable. The key is the insurance company’s data: the thousands of contracts they’ve already calculated – with human input – are the ideal basis on which to train a Machine Learning model to learn to write contracts itself.
Machin Learning models like this open the door to cheap, individualised underwriting – which is especially helpful in the B2C space (such as life insurance).
But that’s just the beginning – the insurance business is stastical through-and-through, so there are many more AI applications. Here are two examples:
Good drivers pay less for car insurance
Some insurance providers are already predicting your risk of having an accident based on the way you drive (your telematic signature). They use algorithms to learn which driving behaviours are signs of a safe driver – and then they offer those drivers cheaper rates.
This sets insurance companies apart from the competition and attracts safe drivers who don’t want to pay for risky drivers’ mistakes.
Predicting which insurance you’ll choose
Another way Machine Learning makes insurance companies’ lives easier is by predicting the exact insurance option you’ll prefer in the end. This makes your advisor’s job much easier, because they can propose the options that make the most sense to you right away.
And this is just the tip of the iceberg
There are many more applications, such as: predicting whether you’ll be a loyal customer, whether you’ll default on the insurance or file a claim in the future.
Targeted sales of banking services
Banks have a lot of very valuable data on you – besides knowing your age, income, and where you live, they also know your exact spending behaviour. To put it mildly, this reveals a lot about you. Maybe more than your online browsing behaviour.
If a bank used all the information hidden in this data, it could learn a lot about you – including which services you’re interested in right now.
Banks hire advisors to give some personal advice – but they only have time for the big clients, and an advisor needs a lot of experience to read the signs correctly.
Machine Learning can find the relevant information in your data and predict which bank products fit your current interests:
- A new credit card or a higher credit limit?
- A temporary loan?
- Or maybe a mortgage, because you’re behaving like someone who plans to buy a house soon?
Not only that, banks can also use Machine Learning to predict the likelihood that you’ll repay that credit, loan, or mortgage – and hence which interest rate they should charge you.
This will increasingly lead to proactive lending. Instead of waiting for you to apply for a loan, thanks to algorithmic profiling, banks will offer a tailored loan to you – and also to millions of other customers.
Churn prediction - Predicting whether you’ll switch banks
Once you decide to switch banks and let your advisor know, it’s usually too late to convince you to stay. It would be much better if they saw the warning signs early and had a chance to make you happier before you decide to leave.
Banks can use the same profiles as above – built from your data and transaction histories - to predict whether you’re likely to switch to another bank. This gives advisors early warning of who to focus on and helps the bank retain many customers they would otherwise lose.
So why is AI so useful in finance?
Simply put, because finance is a game played with data and statistics. Financial products are mostly mathematical bets: statistical equations that – on average – should return a profit.
In the simpler and more repetetive parts of this game, we can substitute Machine Learning for human brainpower and automate a lot of decisions.
That is, unless we’re directly competing against other human beings – as in trading or fraud detection, where human competition makes the game much more complicated. In those cases, we’re better off using our strongest weapon: our own brains.
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