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Top uses of Machine Learning in Fintech Companies

With Fintech startups mushrooming everywhere there is a rise in demand for AI and Machine Learning as well. More and more Fintech companies are are incorporating artificial intelligence and machine learning in their business. Find out how you can introduce machine learning and AI in your fintech startup.


Technology has impacted our daily lives in different ways and has benefited all industries. One such technology which has unprecedented demand in the industries these days is machine learning and artificial intelligence. Machine learning and AI has revolutionised our world and daily lives. Machine learning can enhance and create new breakthroughs across a wide range of industries, the finance sector being one of them.

Many companies in the financial sectors enhance their performance and cost efficiency while improving their sustainability in the market by training machine learning models using a large amount of data that is available from customers, markets, rivals, etc. So let's understand what FinTech companies are and the various roles that machine learning can play to improve their business.

What is Fintech ?

Fintech is the combination of two words ``financial” and “technology”, these companies integrate technology in their business to improve the financial services and operations. In today's day and age Fintech is humongous and is predicted to grow more in the future. There are different types of Fintech Companies in the market Like Consumer Banking and investment, Mobile payments, Insurance Tech, Digital Trading, Lending and leasing, credit etc. This is the industry where new technologies are developed and deployed to streamline more traditional looking finance functions.

Benefits of Machine Learning in Fintech

Security and fraud Detection

Digital transformation process taking over the world, along with this financial cyber crime will also increase. The bright side is that businesses and consumers can now secure themselves and their accounts owing to AI and ML. Blockchain technology and cryptocurrencies are frequently linked to financial cybersecurity. However, in the near future, we'll also link AI and ML to anti-money laundering and digital security solutions. In addition to being able to spot questionable activities, algorithms are also able to alert people. There is no need to stay on guard around the clock because these technologies can continuously monitor strange patterns. Users may be confident that their privacy is protected while keeping track of everything that occurs behind their backs.

Algorithm Trading

Nowadays, algorithmic trading is more popular. In fact, algorithmic trading, a machine learning application, accounts for over 70% of all daily trading conducted globally. But how does algorithmic reading vary from conventional trading, and what is it? Trading orders that are executed using machine learning algorithms in conjunction with financial formulas are done so while keeping in mind pre-programmed trading instructions. Algorithmic trading does not involve human emotions or preconceptions because it is automatic and takes into account changing market elements like price, time, volume, etc. Another benefit of algorithmic trading is that it eliminates the requirement for humans to continuously monitor the market, which is a requirement in manual trading. All these elements

Customer Support

Chatbots is one of the most popular AI applications. Although they have been around for a while, ML algorithms have only lately begun to help them gain popularity. Strong chatbots that can engage with customers and provide an instant response to a variety of customer demands are currently on the increase.

Bots are a key route that FinTech companies use to address client complaints. Some of the most popular ML solutions are automated customer support and robo advisers. Chatbots enable businesses to cut expenses and boost customer pleasure, and the results have been significant.

Mobile Banking

The main focus of many financial technology companies is mobile banking. Consumers now expect to have quick access to their bank accounts in the world of personal finance, especially on a mobile device. Due in large part to the emergence of neobanks, or digital-first banks, the majority of big banks now provide some sort of mobile banking functionality. Neobanks, which provide consumers with checking, savings, payment, and loan services through entirely mobile and digital infrastructure, are essentially banks without any physical branch locations. Open banking refers to the practice of some banks of granting access to user financial data to outside software programmes. Chime, Current, Aspiration, and Varo are a few instances of fintech banks, often known as neobanks.

More Loan approve with lower risk

Through AI and ML, loans can be processed more quickly while reducing inefficiencies. A better customer risk profile method also makes them more accurate than the conventional underwriting process. Some academics even contend that by reducing biases that can develop during human decision-making, this might benefit buyers. Although the last is true, biases that are detrimental might also exist. Agents that employ these processes must ensure that their methods for determining credit scores are perfect; otherwise, they run the danger of alienating a significant segment of their clientele.

To Conclude

There are a million reasons why Machine Learning is essential for business especially for Fintech industries. Since ML is still in its developmental stages there are negligible constraints in the integration of Fintech and Machine Learning.


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