TOP 10 APPLICATIONS OF MACHINE LEARNING IN FINANCE & FINTECH Posted on : Oct 08 - 2020

In the modern era, financial institutions are running a race towards digitization

Machine Learning (ML) is reshaping the financial services like never before. It has become more prominent recently due to the availability of a vast range of data and more affordable computing power. It helps financial companies and banks to stand out of the box and achieve desired business growth.

In the modern era, financial institutions are running a race towards digitisation. Staying ahead of technological advancements is a mandatory resort for them. To keep up the pace, disruptive technologies like Artificial Intelligence (AI) and machine learning are improving the way finance sector functions. Leading banks and financial service companies are deploying AI technologies, including machine learning to streamline processes, optimize portfolios, decrease risk and underwrite loans amongst other things.

Machine Learning in Finance

Machine learning in finance is all about digesting large amounts of data and learning from the data to carry out specific tasks like detecting fraudulent documents and predicting investments, and outcomes. Machine learning uses a variety of techniques to handle a large amount of data the system processes. Various financial houses like banks, fintech, regulators and insurance forms are adopting machine learning to better their services.

Machine learning uses statistical models to draw insights and make predictions. Some of the major use cases of machine learning in the financial sector are underwriting processes, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, offering alternative credit reporting methods.

Functions of Machine Learning in Finance

Machine learning is well known for its predictions and delivery of accurate results. The financial sector involves issues of data-rich problems which could be solved by the implementation of machine learning. Similar financial issues in banking and financial series can find a solution using machine learning algorithms.

Machine learning algorithms are designed to learn from data, processes, and techniques to find different insights. Here are some of the reasons why the financial sector should adopt machine learning,

• Improves productivity and user experience

• Enhances revenue

• Low operational cost due to process automation

• Gives security to transactions

Use Cases of Machine Learning in Finance

Enhancing Financial Monitoring

Cyber risks in the financial sector are high. Unlike any other industry, finance involves a lot of money which could drive to a big loss or great fall if mishandled. Thus, financial monitoring is a provided solution for the issue through machine learning. Machine learning algorithms can be used to enhance network security significantly. Data scientists are also working on training systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring. View More