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How Machine Learning can be Useful in the Finance Industry?

How Machine Learning can be Useful in the Finance Industry?

Technology signifies no industry but an application that benefits all industries. After all, new technologies like machine learning and data science can provide new paths in many different industries like the finance industry.  However, machine learning plays an important role in finance. After all, finance just covers your banking or even share trading. But, what is the relevance of machine learning here? Machine learning has many different benefits of the finance industry.

Top software companies in Toronto can enhance their performance and cost-efficiency. This is done by training machine learning models using a large amount of data that is available from customers and markets. This is how technologies like machine learning can be combined with financial institutions to obtain good results. So, let’s understand the meaning of Fintech that machine learning plays in enhancing these companies.

Fintech Overview

FinTech is an innovative achievement that is combined using two words. i.e, Financial technology. However, this is the combination of finance and technology that merge various tech sectors into financial services. There are different types of Fintech companies in multiple sectors such as banking, insurance etc. These companies can reach out to a larger number of customers and provide them secure and easy transactions.

And emerging technologies such as machine learning have made these companies easier. This makes it easier to access for their customers. They provide more applications in various domains like financial trading, fraud prevention and customer support. So, let’s explore these benefits that machine learning provides to the finance industry.

Benefits Of Machine Learning in Finance Industry

  • Financial Trend Forecasting

Machine learning algorithms play an important role in forecasting financial trends. Finance companies can use ML algorithms to predict market risk, reduce fraud etc. Companies are focusing on training their machine learning models on huge amounts of data. Such as financial interactions, company stocks, customer interactions etc. This ensures that they are useful in predicting future trends. This, however, relates to lending, insurance, stocks etc.

Companies can also use these ML algorithms that can predict risk scenarios, changes in portfolios etc. Another application ML is forecasting consumer trends for these companies. 

  • Algorithmic trading

Algorithmic trading is becoming popular these days. In fact, around 70 percent of all daily trading worldwide is algorithmic trading. This is an application of machine learning. Algorithmic trading involves execution of  trading orders. Thus keeping in mind pre-programmed trading instructions. These are created using machine learning algorithms in conjugation with financial formulae. 

However, there are no human emotions involved in this. Because the algorithm is automatic and keeps in mind changing market variables such as price, timing and volume. Another advantage is that humans don’t need to monitor the market consistently. All these factors combine to create much larger profits from algorithmic trading that’s not possible by human traders.

How Machine Learning can be Useful in the Finance Industry?

Other Benefits

  • Advanced Customer Support

Machine learning is also helpful in offering  advanced customer support to all their clients. Now, there’s no need for customers to stand in long lines just to have some basic queries answered. An example of this is chatbots. These chatbots can provide instant support and solutions for their problems.

And this saves time for both big software companies in Toronto and their customers. This takes place when agents are not required to solve basic problems. For example, the Bank of America has its chatbot that can provide balance sheets, past transactions, to customers with no hassle. 

  • Fraud Prevention and Detection

High chances of fraud occur in finance industries. This is especially true with the increase in technical innovations as there are more chances to do fraudulent transactions. In such cases, an ML based approach to fraud detection and prevention is useful along with the traditional methods. The ML based approach manages real time processing and automatic detection of the irregularities using ML algorithms. The common examples are credit card fraud and investment fraud.

Meanwhile, finance industries can catch the irregularities in credit card history and financial purchases using ML algorithms. This also freezes the account to minimize their losses as much as possible. 

  • Advanced Underwriting Services

Underwriting services are those where large Finance industries guarantee payment for financial losses. This also accepts the risk of paying this payment. However, this can happen in the case of insurance. But finance industries need a complete risk assessment before deciding if it’s worth it to provide underwriting. However, this process can be complex as applicants may hide details about their past financial history. 

Meanwhile, machine learning algorithms are a much better option for risk assessment.

Conclusion

There are a lot of benefits that machine learning can provide to finance industries. Infact, ML is useful to improve every aspect of service. This ranges from operations, security, marketing, customer experience and sales etc. And since this is still a developing technology, there are no limits to how finance and technology can create much better experiences for their customers in the future.

Visit Gyan Solutions to know more about usage of machine learning in finance industries.

 

 

 

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