How machine learning is affecting the 21st century financial industry

 

According to Yas Aloosy, before mobile banking apps or chat bots made their way into the financial scene, machine learning had already made its mark on the global industry. Artificial intelligence (AI) is obviously a great tool for the sector given the high volume of data and transactions, the need for accurate logging of historical records, and the quantitative nature of the industry. And with machine learning using statistical models to draw insights and make predictions, many aspects of finance has flourished in the past 20 years.

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The first example Yas Aloosy gives on the effectiveness of machine learning is with fighting fraud. The fact of the matter is that financial fraud alone costs Americans $59 billion every year, and traditional ways of keeping clients’ accounts safe and secure are no longer enough. It’s a good thing machine learning algorithms compare every transaction against account history, thus becoming able to assess the likelihood of a fraudulent transaction. Machine learning has the power to identify problems and raise flags in the system, helping delay the transaction until a human verifies and decides.

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Machine learning also helps in loan and insurance underwriting, especially among big companies that can hire data scientists and have the budget. In these companies, machine learning algorithms have millions of samples of consumer data and financial lending or insurance results, through which they assess the underlying trends and analyze factors influencing lending and insuring into the future.

Yas Aloosy mentions that these are just a few of the ways machine learning has made an impact on the global financial system. It has helped everyone out in terms of customer service, portfolio management, and a lot more other aspects.