In the previous piece we explored the definition of Machine Learning and this piece will further explore the relationship between Machine Learning and finance. 

Even before the advent of mobile banking applications, chatbots, search engines, machine learning already had some very productive applications in Finance. The use of machine learning in finance has been growing since its launching in this sector and all the statistics show that it will continue to grow, considering how dynamically this sector is evolving and how rapidly people’s requirements are changing. 

Today, Machine Learning has become an indispensable part of finance. It is well embedded in the sector and plays a very significant role in the financial processes like approving loans, managing credit scores, assessing risks and managing assets to name a few. All in all it is also helping companies drive value and provide better services to their clients.

Current Applications of Machine Learning in Finance 

Let’s explore the day to day applications of machine learning in the finance sector. We might not have observed this, but Machine Learning is being used quite actively. Let us take a look at some of the major applications of Machine Learning in finance at present. 

1. Portfolio Management

The term “robo advisor” was absolutely unknown to people about five years back. But today, it is a very common term in finance. The term is a little misleading because there is absolutely no use of robots involved. Robo advisors are actually algorithms which are built to assess a financial portfolio of the goals and risk tolerance of an individual. For this, the user has to give details including their goals, age, income and current financial status. The advisor or the allocator then lays out investments across the asset classes and financial instruments to reach the goals of the user. The system then works out to find the best fit for the goals of the user.

Robo advisors have gained a lot of popularity overtime because they have eliminated the use of a physical advisor and made the whole process more economical. 

2. Detection of Frauds

Fraud Detection is another very important function that is facilitated by Machine Learning. The modern fraud detection process works beyond following a checklist of the risk factors. With the help of machine learning, the current fraud detection process actively learns and assesses if there is any possibility of the occurrence of any new potential threats. Machine learning helps in detecting any unique or suspicious activities and flags them for security teams.

3. Algorithmic trading

Algorithmic trading makes use of complex AI systems which enable it to make fast decisions. Machine learning plays a very significant role in assessing trade decisions in real time. 

4. Loan or Insurance underwriting

Underwriting is one of the most appropriate jobs for Machine Learning in finance. Big companies are making use of Machine Learning algorithms to be trained on several examples of consumer data. These algorithms are used to detect the underlying trends which influence lending and ensuring in the coming times. 

Future Applications of Machine Learning in Finance

The future of Machine Learning is very promising in the finance sector. Here are the applications of machine learning in finance which we are going to see in the coming future and which indicate a very bright future for Machine Learning in the financial industry. 

1. Customer Service 

We have already seen some banking companies providing the facility of chatbots which allows customers to ask queries via chat. This also lessens manual labor as the automated messages are usually sufficient to clear the queries of people. This is something which is still not very common in the finance sector as of now, but this shall soon become a trend in the coming future. 

2. News Analysis 

In the future, Machine Learning is expected to play a fundamental role in understanding and analyzing the stock market. It will be used to provide some very productive insights into the world of AI hedge funds as well. 

3. Security

User security is expected to take a massive turn in the coming 5 years. Things like usernames, security questions, passwords might not be a necessity in the future. In the current scenario everything is dependent on these factors but this is going to change in the coming times. Apart from the development of anomaly-detection applications, the security measures in future might require voice recognition, facial recognition and other biometric data. Machine learning will contribute in strengthening the safety and security measures and making them more convenient, leaving no scope for fraud or exposure of personal data to anyone who might try to misuse it. 

4. Sales of financial products

Applications of the automated financial products’ sales exist today as well but some of them might not involve the use of Machine Learning. The future will see more extensive use of machine learning in this sector sooner than one might think. For instance, we see Netflix recommending us movies and series on the basis of what we watched recently or what we have watched the most, and these recommendations turn out to be relatively better than the ones we receive from humans. We might see something like this happening in the financial sector as well. The financial personal assistants will to do the same job for financial products. This trend has already begun in the insurance sector.


Keeping the above-mentioned points in mind, it can not be wrong to say that the financial sector is going to witness some very significant transformations in the coming five years and Machine Learning is definitely going to be a central and a very important part of it. Although the contribution of Machine Learning is not very tangible as of now, in the coming years, the contributions made to the financial sector by Machine Learning will become apparent to the world at large.

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