The Various Applications of Machine Learning in Finance

With the widespread use of machine learning technology in the finance sector, the industry is set to benefit greatly from the many promising aspects of this technology.


The applications of artificial intelligence (AI) and Machine Learning (ML) in FinTech are predicted to rise to $7,305.6 million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.4%, according to a new market research report by MarketsandMarkets™.

But you may wonder what makes machine learning such an attractive proposition for the banking and finance sector.

Without any doubt, the reason can be attributed to the tremendous volume of data that is generated and managed in the segment.

Machine learning comes to the fore to help decipher valuable insights hidden in the vast heaps of data sets. Patterns and correlations of sequences and events often not visible to the naked eye are superseded by the ability of ML to learn, derive, and predict data-driven insights.

As a result of attaining valuable data-driven insights, ML enables FinTech providers to discover new business opportunities and develop proactive strategies. And that's not all it does.


Machine Learning Benefits in Finance Sector


Here are some more specific applications and benefits of machine learning platform in finance that can help FinTech providers achieve a distinct competitive edge:


1. More Loan Approvals with Lower Risks


The interest in new lending models such as Peer-to-Peer (P2P) lending, sometimes called “social” or “crowd” lending, has paved the way for novel credit scoring mechanisms.

Credit scoring is one of the most beneficial applications of machine learning that provides lenders with unique insights to assess an individual's creditworthiness.

Using alternate data points, machine learning platforms can dig deeper into thousands of factors, such as data from social profiles, telecommunications companies, utilities, rent payments, and even health checkup records, to generate an accurate risk score.


2. Fraud Detection and Prevention


Fraud in the finance sector is a complex problem for all fintech providers, regardless of their size or number of customers. Machine learning platforms hold a solution to help the industry by separating the bad and good patterns to swiftly detect anomalous activities.

For example, ML models can identify unusual activity in the course of an online transaction. Unlike a human agent, the ML algorithms can quickly assess the transaction details against thousands of data points to determine whether the attempted transaction is authentic or not.

That can not only prevent many instances of fraud, but also dissuade it from happening in the first place.


3. Compliance and Regulatory Requirements


The finance industry's ever-changing regulations are a big hassle to deal with and might prove costly when finance companies fail to comply.

ML platforms can automatically track and monitor regulatory changes and ensure that customer transactions comply with the regulatory requirements.

Besides that, the time-consuming and tricky tasks of generating compliance reports can be performed by machines with ease.


4. Enrich Customer Experience


One reason why FinTech services choose to invest in a machine learning platform over traditional legacy systems is because of the superior prowess of ML systems.

ML algorithms crunch chunks of data to identify what exactly the customer wants so that financial entities can tailor personalized products or services accordingly.

The benefits of AI and ML for the banking and finance segments, in particular, are beginning to play a pivotal role in various processes of loan approvals, fraud prevention, compliance requirements, customer experience and more.

Yet, not many financial institutions and startups have embraced machine learning as a critical driver for success. It's high time for the finance sector to jump on this trend and leverage machine learning tools, algorithms and computing capacities to stand out from the crowd.

Alexis Davis is a senior staff writer at She covers social media and other digital media news for the publication.