{"id":1042,"date":"2018-12-26T00:56:00","date_gmt":"2018-12-25T21:56:00","guid":{"rendered":"http:\/\/kusuaks7\/?p=647"},"modified":"2021-05-11T14:02:41","modified_gmt":"2021-05-11T14:02:41","slug":"machine-learning-in-finance","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/software-ux-ui\/machine-learning-in-finance\/","title":{"rendered":"Machine Learning in Finance"},"content":{"rendered":"

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How AI is used for trading, fraud detection, insurance & personalised banking<\/h4>\n

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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.<\/p>\n

AI for trading<\/h2>\n

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<\/a><\/p>\n

I summarised most important points here:<\/p>\n

AI doesn’t replace traders – It helps them<\/h3>\n

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.<\/p>\n

They also conduct rigorous statistical analyses to decipher the details of small-market inefficiencies.<\/p>\n

All of this takes time – and since trading is a competitive game, the faster you are, the more of the pie you get.<\/p>\n

AI helps make traders faster and more accurate in their analyses by summarising news<\/strong> (e.g., with sentiment analysis), improving predictions<\/strong> of external factors (such as rainfall, commodity supply volumes, or election outcomes), and automating the fine-tuning of high-frequency trading machines<\/strong>.<\/p>\n

Automated & profitable AI trading is mostly a fantasy<\/h3>\n

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.<\/p>\n

There are lots of common mistakes that make it seem like this is possible – when in fact it isn’t.<\/p>\n

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<\/a><\/p>\n

Making portfolio management better & cheaper<\/h2>\n

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).<\/p>\n

They pick different types of investments and arrange a portfolio<\/em> (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.<\/p>\n

For a human being, this is a time-intensive process<\/strong> – 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.<\/p>\n

That’s why there’s been a rapid rise in “robo-advisors.”<\/strong> Not only are they very cheap – they actually do their job better than more expensive, human portfolio managers.<\/p>\n

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.)<\/p>\n

The secret<\/strong> 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<\/strong>: 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.<\/p>\n

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.<\/p>\n

Want to learn more about the theory behind statistical asset allocation? – Check out Modern Portfolio Theory<\/a>.<\/p>\n

Strengthening fraud detection<\/h2>\n

With increasing digital commerce (through credit cards and online payments), the incentive to commit fraud is also constantly increasing.<\/p>\n

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<\/strong>. Which means legitimate customers are denied purchase transactions – and they’re rightfully upset, and will go and buy somewhere else.<\/p>\n

Machine Learning can produce more accurate fraud-detection checklists<\/strong> while also reducing false alarms. Why are AI models better? These are the Top 3 Reasons:<\/p>\n