Machine Learning And Finances : The Truth About 3 Myths

Machine Learning And Finances : The Truth About 3 Myths

Machine Learning And Finances : The Truth About 3 Myths : Nowadays, financial planning and analysis takes a lot of studying and improving. Even with the rise of technology, financial institutions must still be on top of their game. That’s where machine learning (ML) comes in. 

Although ML may still have its pitfalls to this day, it’s still important to see what it can do for businesses in the meantime – banks and institutions included.

However, as companies envy the improved forecasting accuracy and efficiency that ML can bring, there are still people who still see the implications of adapting to such technology. 

You might still have questions yourself:

  • Is machine learning safe?
  • Is machine learning better for financial institutions? Is it bad for such institutions?
  • Can anyone trust that ML won’t leak personal financial information to would-be hackers?

But not to worry!

This article will demystify 3 common myths about ML and finances, and will attempt to shine light on the truth about each. 

  1. ML Can’t Make Financial Decisions

“It’s understandable that many financial institutions are still wary about machine learning, and what it can bring, since it’s still being tested by various industries,” says Martin Leakey, a business writer at 1 Day 2 write and Write my X.

“But the truth is, whether people realize it or not, they’re already using ML and AI-based technology in their daily lives. From asking Siri or Google questions, or online stores providing product recommendations to users, machine learning is behind all of that.”

Plus, ML has already served in the following areas, when it comes to finances:

  • Helping banks and other financial institutions fight fraud
  • Being an important asset in affordability modeling
  • Coming up with ways to prevent credit risk
  • Analyzing existing data in institutions’ databases
  • Helping lenders support their decisions to both consumers and regulators
  • Helping flag default applications 
  1. ML Is Hard To Learn And Time-Consuming

While ML is still fairly new to many institutions, that doesn’t mean that businesses will need to start from scratch to learn and implement it. In addition, while it’s still justifiable to some that ML is a technology that only a handful of people know, there’s still room for anyone to learn about the technology themselves.

In other words, ANYONE can learn and implement this technology, including banks and institutions. Even if an institution only implements ML to have it work alongside existing systems, then that’s more than enough “learning” and “time consumption” that you’ll need.

As a result, institutions will see plenty of results, whenever they choose to implement ML – either partially or fully.

  1. ML Leads To Risky Financial Situations

Finally, the ever-present issue of trust tends to make people wary about whether ML is good for financial institutions or not. 

“Trust comes from trial and error, especially in the financial industry,” says Samuel Edison, a writer at Brit Student and Next Coursework. “For example, fraud is something that makes people think twice about who they trust their money with.

Therefore, instead of embracing newer technologies, they may opt for the traditional methods. However, with scam artists and thieves growing more sophisticated with their fraudulent schemes, machine learning will need to be implemented to be one step ahead of these criminals.”

So, how exactly can ML help financial institutions prevent fraud, theft, etc.?

“Machine learning will look into tons of data to detect and prevent suspicious activity,” adds Edison. “This lets the institution know what’s legitimate, or what’s fraudulent, when it comes to accounts, applications, other data, etc. Therefore, machine learning is your ally, when it comes to fraud prevention.”


As you can see, it’s easy to get caught up in the misconceptions that revolve around machine learning in the financial realm. However, as time has proven, technology has evolved to ensure better handling of finances, of which has been a challenge for traditional methods. So, the only way to go, in this case, is forward!

So, now that you know the truth about the 3 myths described in this article, you can trust that machine learning is not only here to stay, but it’s the right step towards transparent and safer practices in the world of finances. 

So, financial institutions are free to experiment with ML without worrying too much about cost or risk. As long as you do plenty of research, and implement the right practices, ML will be sure to work for you and your financial institution!

Michael Dehoyos is a writer and editor at PhD Kingdom and Research paper writing services. He is also a contributing writer at Origin Writings. As a business writer, he writes articles about financial growth, banking, and cryptocurrency. 

Machine Learning And Finances : The Truth About 3 Myths