From informing banks how to give low risk credits to stock exchange through machine learning algorithms – data science is reinventing finance!
Ever since its genesis, Data Science has helped transform many industries. For decades financial analysts have relied on data to extract valuable insights, but the rise of Data Science and Machine Learning has brought upon a new era in the field. Now, more than ever, automated algorithms and complex analytical tools are being used hand-in-hand to get ahead of the curve.
Fraud prevention is a part of financial security that deals with fraudulent activities, such as identity theft and credit card schemes. Abnormally high transactions from conservative spenders, or out of region purchases often signal credit card fraud.
Whenever such are detected, the cards are usually automatically blocked, and a notification is sent out to the owner. That way, banks can protect their clients, as well as themselves and even insurance companies, from huge financial losses in a short period of time.
The opportunity costs far outweigh the small inconvenience of having to make a phone call or issue another card. The role data science plays here comes in the form of random forests and other methods that determine whether there are sufficient factors to indicate suspicion.
Unlike Fraud Prevention, the goal here is to detect the problem, rather than prevent it. The reason is that we can’t classify an event “anomalous” as it happens but can only do so in the aftermath. The main application of this anomaly detection in finance comes in the form of catching illegal insider trading…