Machine Learning and Its Role in Finance

When hearing the words “machine learning” most people automatically think of artificial intelligence and, furthermore, will be tempted to put an equal sign between the two concepts. However, others strongly disagree. And although both of these sides are in agreement with the fact that ...

When hearing the words “machine learning” most people automatically think of artificial intelligence and, furthermore, will be tempted to put an equal sign between the two concepts. However, others strongly disagree. And although both of these sides are in agreement with the fact that machine learning is a part of artificial intelligence and has emerged from it, some experts will want to emphasize that ML has separated itself from AI.
Since the early 90s, machine learning has started to step out of AI’s shadow and follow its own path, as a distinct domain. It no longer applied AI’s traditional techniques and practices, instead it had turned towards statistic and probabilities for guidance. But how did it all start?

What is machine learning?

The term “machine learning” was first introduced in the late mid-90s, but research and writings on the matter have surfaced as far as three decades before this. It had risen from AI, but specialists have manifested much interest in machine learning both from the perspective of it being a subfield of AI, and from the point of view of it being a distinct branch. So what exactly is ML?
To put it in simple words, machine learning provides computer programs with the capacity to learn from previous experiences, without them having to be directly programmed by specialists for this regard. This technology uses complex algorithms such as regression, clustering, instance-based or association algorithms, and enables machines to give predictions.
Because it offers many advantages, ML is used in various industries and its future seems highly promising. Healthcare, ecommerce, transportation, and banking are just some of the areas that machine learning has started to make its presence felt in, as executives and companies realize its great significance.

How does the finance branch benefit from machine learning?

Machine learning brings numerous benefits to the financial sector, and probably among the most noteworthy of the lot, is the advantage related to data. And lots and lots of it too. With the help of ML, machines are able to analyse large amounts of information and provide companies with valuable insights. And this is especially relevant since data is becoming increasingly harder to review, due to the large volume of it that businesses have to deal with, in order to improve customer experience or optimize processes.
And since we have mentioned customer experience, we must bring attention to another way in which machine learning benefits the finance branch, which is by enhancing interactions between those who offer financial services and their clients. The latter will surely be enjoying a more personalized experience and a comforting sense of security, due to the use of machine learning within the contracted company.

What are some of the most notable applications of machine learning in the financial sector?

When it comes to machine learning’s applications in the financial field, there are a few of them that are definitely worth mentioning. But let’s start with those regarding security. ML is successfully used in fraud prevention and detection of suspicious or unusual activities, contributing to the well-being and protection of account holders. The latter are immediately warned and informed about any anomalies that could potentially represent a threat to the security of their data or accounts. And it doesn’t stop here. Machine learning also makes an accelerated response possible in the case of an unfortunate event, further strengthening customer satisfaction and trust.
Another area in which machine learning has had a positive impact is user experience. Virtual advisors that are available 24/7 can answer questions, give financial advice, and assist users in making financial decisions or even transactions or credit card cancellations. Furthermore, along with the extra help of natural language processing, clients can make voice commands, which are successfully processed by financial applications.

Will machine learning continue to shape the financial world?

A simple answer to this question would be yes, most definitely. As artificial intelligence continues to develop and reach new heights, so does machine learning. And when it comes to the financial sector, the future of ML is certainly brighter than we can imagine. In the coming years, we should expect an even more enhanced customer experience, heightened security, and increasingly complex activities being performed by computer programs and applications.


Even though you might not be hearing the words “machine learning” very often, you would be surprised as to how many areas it has already infiltrated in. And finance makes no exception. Machine learning, although a relatively new technology, has, since its appearance, sparked the interest of specialists and companies alike, and its successful applications stand as a witness for its importance in the financial domain.

Improve your digital banking offering with Arnia Software

We provide Banking Software Development Services that will help you maximize the benefits of your banking software development outsourcing projects. We have more than 14 years of extensive expertise in providing such services and proven ability to deliver high quality services, on-time, with maximized Return on Investment. We have successfully completed banking software development projects with customers from United Kingdom, Denmark, Netherlands, and multiple other European countries.

Arnia Software has consolidated its position as a preferred IT outsourcing company in Romania and Eastern Europe, due to its excellent timely delivery and amazing development team.

Our services include:

Nearshore with Arnia Software
Software development outsourcing
Offshore Software Development
Engagement models
Bespoke Software Development
Staff Augmentation
Digital Transformation
Mobile App Development
Banking Software Solutions
Quality Assurance
Project Management
Open Source
Nearshore Development Centre
Offshore Development Centre (ODC)
Unity Development