The Small Business Times

Machine Learning in Fintech: Top 5 ML Applications

The financial industry continues to look for new ways to implement technology. That’s why the value of ML and AI is becoming more and more apparent.

Nowadays, the fintech industry uses machine learning for a wide range of purposes, e.g., improving results, being more profitable, providing better services, or making better decisions. This article will explore the top 5 machine learning applications in fintech.

If you want to know more, consult a trusted machine learning agency.

The Importance of Machine Learning in Fintech

As financial institutions and banks strive to develop processes, increase security, and better financial analysis, machine learning is becoming the most desired technology.

ML enables big data processing to perform specific tasks, such as fraud detection, stock market forecasting, risk management, better customer support, algorithmic trading, process automation, and many, many more.

Certainly, the use of ML in Finance has vast potential. Why? Well,  because of ML applications. This technology:

  • Improves customer experience by providing ongoing support (chatbots)
  • Reduces the operational costs and human efforts through process automatization
  • Provides businesses with better decision-making
  • Ensures secure transactions
  • Provides better market opportunities which translates into higher revenues

Machine Learning Applications in Fintech

There are countless machine learning applications in fintech. Here, we focus on the top 5, i.e., fraud detection and cybersecurity, algorithmic trading, customer support, investment prediction, and loans underwriting. Let’s take a closer look at each of them in detail.

Fraud Detection and Cybersecurity

The growth of technological innovations increases the risk of fraudulent activity and cyberattacks. Banking and financial institutions keep a large amount of data online, which increases the risk of violating the security of sensitive information. Hence, early fraud detection is urgent for any financial company.

The sooner banks can detect suspicious activity (taking large withdrawals), the faster they will minimize their losses, and most importantly, they will be able to provide better protection for their customers.

Apart from a rule-based approach, the financial industry takes an ML-based approach to fraud detection. To detect unique, unusual activities, ML scans the dataset to determine the likelihood of fraud.

Algorithmic Trading

Algorithmic trading (AT) is automated trading that uses the processing power of electronic devices and well-thought-out algorithms to optimize trading operations and make better decisions.

The mathematical model analyzes business news and trading results in real-time and looks for patterns that could drive security prices up. It can also proactively hold, sell, or buy stocks as anticipated.

Algorithmic trading is more efficient than human-based trading as it analyzes large volumes of data and makes thousands of trades each day. What’s more, it can generate profits at a speed and frequency that is impossible for a human trader.

ML not only enables traders to make quick trading decisions but also increases accuracy while reducing the risk of mistakes.

Customer Support

Fintech machine learning companies develop effective solutions in advanced customer support. Bank clients don’t have to waste their time in long queues since new technologies have allowed banks and financial institutions to digitalize their services.

One of the most well-known AI applications is chatbots. Currently, there is a noticeable increase in the use of bots that enable automated interaction with customers, providing immediate response to many requests. They allow not only to increase customer satisfaction but also to reduce costs.

Also, business leaders say chatbots have increased sales by 67% on average. Another aspect of customer support is a personalized experience. ML algorithms can predict what services customers may like or provide useful advice. In the case of fintech organizations, personalization is crucial as it allows institutions to build customers’ loyalty and trust.

Investment Predictions

Machine Learning algorithms play a crucial role in investment predictions.

Machine learning algorithms analyze past and real-time data about companies (such as financial interactions, loan repayments, company stocks, and customer interactions) and predict future trends based on this information. Forecasting helps fintech companies reduce costs, increase revenues, achieve financial goals, or prepare a budget.

Loans Underwriting

Last but not least, loans underwriting. Machine Learning algorithms, in addition to being able to assess risk, can also make quick insurance decisions. Algorithms analyze consumer data (age, occupation, income, consumer’s credit behavior) to decide whether an individual qualifies for an insurance policy or a loan.

Fintech companies need a reliable risk assessment, which is what machine learning provides. In this case, the client cannot hide any details of the financial past as ML algorithms are more accurate and efficient than the traditional process.


AI and ML are two potent high-end tools having a tremendous impact on the lives of people. Considering all the five applications outlined above, it is obvious that ML algorithms are beneficial for financial institutions. So without a doubt in the future, the financial sector will continue to use machine learning.