How is Machine learning used in Finance?

Machine learning in Finance

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Machine learning is a part of Data Science and an application of AI. It’s been around for quite a while now. It focuses on building up algorithms to not only understand data but also to allow computers to develop a sense of data and analyze it on their own. Machine learning uses a lot of complex algorithms for calculating and analyzing the big data that it is fed with.

The advancement that machine learning has brought with itself in the technology industry is that it can not only analyze the given data using a particular algorithm but also it can with time evolve it’s learning abilities and adapt independently. Therefore Machine learning can adapt and analyze big data without being programmed explicitly. It is being used in every other place you’ll see around you.

You can read more about What is Machine learning at one of our previous blogs on Machine Learning

In this blog, you are going to see how Machine learning in finance is used widely. Finance is one of the many applications by Machine Learning and has made a huge impact on the finance industry providing the various purposes it is being used in.


The evolving technology and open-source software present in machine learning have helped it grow in the finance industry. The use cases are so many across different platforms of finance that it seems impossible to see Finance without machine learning applications. 

The fact that machine learning is fed with an enormous amount of datasets, it is only right to say that it fits perfectly in the Finances. As you know that when it comes to finances, it deals with a very big population of customers that end up giving a large amount of data to the industry daily. Therefore it is very convenient to learn and adapt to machine learning in this sector.


Here are 8 applications of machine learning in finances. These applications with their in-depth explanation of how they are being used will give you a clear view of how machine learning is used in finance.


Machine learning models are constantly being evolved for risk management. Evolving technologies have enabled markets to assess all the risks that get involved in Trading and other factors. Banks, Insurance companies, and Businesses have been using Machine learning technology provided by third-party companies to help minimize the risk factors. 

Traditional techniques like manual calculation or procedures based on FICO scores are not helpful now as they aren’t that efficient or accurate. Removing inefficient systems to an accurate and automated system is what the Finance industry is heading. 


Every person has some kind of finances to be handled. A never-ending amount of customer data is collected. From loans, savings accounts, fixed deposits, trading sectors, audits, etc. All these sectors have never-ending data being collected. Therefore such data cannot be handled manually. 

Hence using machine learning helps to understand the financial history of the customer, so that data can be divided into what is of no use and what would be of value in the future. This helps in the removal of manual address storage optimization. 


Banking and finances have been using machine learning for one mostly used purpose and that is to detect frauds. Fraud detection is done by using a customer history of finances and evaluating it to see if he/she is a reliable customer.

Previous methods for fraud detection were dependent on complex and robust rules. Whereas today a set of checklist rules to check risk factors are analyzed. The system learns and searches for new active threats.

A huge number of transactions are hence evaluated using real-time decision making and It helps in the detection of subtle or non-intuitive patterns which could lead to fraud.


Decision-making is one of the most important applications of machine learning in various industries. It is being widely and popularly being used in financial services.

Once the structured and unstructured data is analyzed using machine learning, a decision can be made for financial information. This helps in the detection of useful and potentially dangerous trends. 


Using deep learning neural networks, Machine learning can help a large population in predicting investments. It helps us to find out which companies will bolster out and simply not go with our self intuition.

However one cannot predict how a company will perform in the future but one can estimate by using the parameters. Long short-term memory and recurrent neural networks can make an approximate estimation of the future of stock for investment.  


Since cyber threats have increased in 2020, we need financial security more than ever. Machine learning is playing the biggest factor in making sure that our finances stay safe.

Through machine learning our cash flow patterns are kept in record and the systems learn how one usually spends his/her money. If any kind of suspicious activity is seen through the accounts, The account owner is informed automatically.

2FA is a new way to keep a check on the security of accounts. An account holder needs to pass two steps for the authentication of transactions which makes the whole transaction a lot safer.


When a trade transaction is failed, it is pushed into a long queue of failed transactions. Where the failed trade settlement is made through a long process.

This is where machine learning comes into the picture. Machine learning helps us understand where the trade settlement went wrong. It not only makes the process a lot faster but also results in cost savings and revenue opportunities. The financial sector is adapting to this technology at a very fast pace because of such results. 


Machine learning reduces the false positive numbers that are generated by old Anti-money laundering systems. Machine learning has changed the whole perspective of how anti-money laundering systems were used.

The two ways in which Machine learning is being used to prevent money laundering are:

  1. Monitoring of transactions
  2. Customer knowledge (KYC)

Only 2% of false-positive cases were coming under the radar of declaration of suspicion. But now using Machine learning the rest 98% will also have a much better chance of coming under the Declaration of suspicion. 

By all these ways one can see how Machine learning is playing a very crucial part in the Finances. Important issues like security, trade settlements, and money laundering are getting efficient operations to be worked with. 


At the rate at which machine learning is getting the boost to grow in the finance sector, one can certainly see a bright and promising future for this technology in finance. These are some aspects where Machine Learning will grow immersively.

Security: The 2FA might no more be present in the future. Things like passwords, security questions or OTP might not be there in the coming years. These can be replaced using Technology like facial recognition, voice recognition, or another type of biometric technology.

Customer service has taken a step up in the chatbox and virtual assistants area. But these areas might also get a top-notch in the coming years when the assistants will be able to answer more personal questions to the customer.

There are a lot of many aspects that are growing with machine learning. It has been proved that businesses that use Machine learning in finance with their algorithms have a direct increase in profits. And hence the propitious future we see in the sector. 

If you want to learn more about machine learning, check out Verzeo for Machine Learning Certification and Internship programs that will help you become industry ready in just 2 months. 

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