The Best Uses for Machine Learning and AI in FinTech

by prostir
11 December 2017
The Best Uses for Machine Learning and AI in FinTech

Machine learning is a computer technology that allows resolving various problems without programming the actual solving algorithm. The machine is learning on the examples and then is able to mimic or predict the results with different inputs that weren’t presented during the learning process. The use of machine learning in finance is a comparably new area but it already shows a lot of implementations, including security, prediction, and analysis.


Machine learning algorithms can’t always get the perfect results for problems, but their prediction capabilities are just great. Machine learning in financial services allows FinTech institutions to predict a certain event in the future with lots of incoming complex information given. These machines, for example, become a part of the forecast systems in the investment companies, as they can analyze the market and output just a probability of a good investment as a simple number, easily interpreted by specialists. This way, if you extend the input information and include more capabilities in the system, a machine learning algorithm is able to perform various risk management tasks for different kinds of markets and investments.


Risk management partially welcomes the machine learning to a new category of security analysis. A neural network, or any similar machine, is able to analyze millions of events of one kind to detect suspicious actions. It may trigger a danger alert when it sees a lot of similar events, or an extraordinary one, or just spikes in activity with similar parameters. When analyzing a stream of transactions, a machine learning algorithm can work as a fraud prevention tool. If connected to a network interface, it can perfectly work for network security and Cyber Security as well.


Stock trading is a very complex thing. The computer algorithms that are any good in trading, cost tens and hundreds of thousands of US dollars, and give just a good probability of good trading, without any warranties. With them, a trader basically puts his own trading algorithm in code, but even the best traders make mistakes, and so do these algorithms. Machine learning for trading allows a computer to analyze and learn about previous price changes and trades to learn how does the market work and how bigger trades influence it. One other perk of machine learning stock trading is that a neural network can learn on the price history, so the learning process is usually much faster.


More usual FinTech applications may also use machine learning for their enhancement. For example, banks require lots of information to be processed during various business decision processes. Credit underwriting and portfolio monitoring already use machine learning during the crediting process. The computer analyzes lots of customer’s data and payment history to decide, whether or not should they receive a credit, and controls the bank’s portfolio afterward. Neural networks can predict the interest increase for credits and deposit withdrawals and also help balance the banks lending account to ensure that both creditors and depositors will be able to get their money at any time.

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