2 Major Applications of Machine Learning in the Fintech Segment

Artificial Intelligence technology has drastically transformed businesses that are heavily dependent on the use of progressive technology. Machine learning is a part of the bigger whole that we call Artificial Intelligence. So what exactly is machine learning? Machine Learning can be defined as an application of the artificial intelligence technology that provides systems with the ability to automatically learn and improve from processing experience. Machine learning doesn’t require any explicit exclusive programming for systems to process new information.

Data science is at the core of machine learning technology. Tons of data is used to facilitate the automatic learning process in systems. These data sets help to train the systems for multiple scenarios and then this learning experience is used by machines to process real-world scenarios. The Fintech industry is highly dependent on the use of data to function. Machine learning technology has a crucial role to play in the Fintech sphere. Let’s peep into two prominent applications of machine learning in the Fintech segment.

Algorithm Trading

Algorithm trading is a smart use of technology in the financial sector. It is a powerful strategy that is used by companies in the financial domain to automate their decisions and increase the volume of trades. As per research reports, more than 75% of daily trading is automated and carried out by machines.

The frequency of trades executed using machine learning technology is unmatched by any manual counterpart. Algorithms trading methods are free of biases and there is no chance of random human error. Also, it reduces slippages to a great extent. BofAML by Bank of America is a good example of a high-frequency trading platform.

Fraud Detection

The use of progressive technology doesn’t make businesses immune to criminal intent. Cyber thefts are very prominent and result in a great financial loss for organisations. Also, the use of progressive technology has some scope for fraudulent transactions. As per research reports, almost 17 million corporations and individuals in the United States have experienced fraudulent transactions. Over the years, corporations in the financial domain have used various strategies to detect and prevent fraud.

There are primarily two types of fraud detection practices; the rule-based practice and the machine learning based practice. The machine learning based fraud detection techniques are far more efficient in detecting fraudulent transactions and preventing it. It provides real-time data processing and also finds hidden fraudulent activities which would have not been otherwise detected. Machine learning practice automatically detects all possible abnormalities and the number of verification measures used is also on the lower side.

Conclusion

The article aims to edify the reader on the use of machine learning technology in the Fintech industry. It focuses on two major applications of machine learning in the Fintech domain, this includes Algorithm trading and Fraud detection.