Machine learning: Powering business growth

How can machine learning benefit businesses?

In these past few years, many financial institutions and Fintech companies are adopting smart solutions to stay on top of industry trends. Some of these smart solutions include using Artificial Intelligence (AI) and machine learning.

In 2019, the Fintech industry reached an estimated value of US$6.67 billion in AI. And this number is not going down anytime soon. In fact, it is expected to grow in the next five years to reach up to US$22.6 billion.

AI and machine learning solutions are not only in high demand in the Fintech industry—almost all industries are looking for new solutions using these technologies. This is mainly because AI and machine learning help to reduce cost, increase productivity, improve efficiency, detect fraud, apply automation and increase customer satisfaction.

What is machine learning?

TranSwap_Machine learning: Powering business growth

Machine learning is a subset of AI that is used to make accurate predictions based on new sets of data. It also focuses on building applications that mimic the mechanics of the brain and nervous system.

For example, in data science, an algorithm is a sequence of statistical processing steps. And in machine learning, these algorithms are trained to make decisions and predictions based on new data. Better algorithms mean better predictions.  

Machine learning is widely used in cybersecurity, customer service, portfolio management, and many more. Its precise predictions are a great fit for the financial sector as the financial industry is all about collecting hard data.

Now, machine learning is all around us—digital assistance for when we are surfing the web, websites recommending movies and songs, our email spam detectors, and even our robot vacuum cleaners have machine learning algorithms.

How machine learning is changing the Fintech industry

1. Maximise business efficiency
TranSwap_Machine learning: Powering business growth

With machine learning, businesses can remove needless touchpoints and manual tasks such as security monitoring, auditing, or reporting. This way, the team can focus their attention on tasks that require more strategic plannings.  

Fintech companies also require a lot of time to deal with new rules and regulations. This time can be minimised with the use of machine learning. Machine learning algorithms can detect correlations between the different regulatory documents and guidelines, therefore ensuring that customer transactions comply with the regulatory requirements.

2. Better risk management
TranSwap_Machine learning: Powering business growth

The key to making big money is to read future risks correctly. And risk management is a complex business operation as there are plenty of variables to consider and complex decisions to make with very limited data.

But machine learning algorithms can be trained to make accurate predictions for the business, its customers, and the whole market. It offers a more complete understanding of a business’s risk profile and can be custom-made to follow the needs of the business or the organisation.

3. Improve personalisation
TranSwap_Machine learning: Powering business growth

With better personalisation and improved user experience, businesses get to enjoy increased revenue. Nowadays, businesses are looking for more personalisation to be in touch with their customers. In many sectors, including the finance sector, chatbots are widely used to automate the early stages of sales and customer service.

Now, it’s getting a little tricky for customers to distinguish if they are dealing with a chatbot or a customer service consultant as chatbots can now detect the sentiment of the client. Meaning, if customers show signs of dissatisfaction, chatbots will know what to do. And that’s not all, machine learning can also collect customer’s information and preference to better understand them and provide better service in the future.

4. Improving marketing efficiency
TranSwap_Machine learning: Powering business growth

As mentioned above, machine learning can predict customers’ preferences. From this piece of information, machine learning can send customers targeted advertisements and personalised messages to encourage customers to take action. Other than guiding each customer in their buying journey, machine learning can also be available for every customer 24-7.

Machine learning can also study market trends. What this means is that they can find new ways and opportunities to create new revenue streams. This way, businesses can focus on projects that are relevant to their customers and avoid taking a scattershot approach to marketing.

5. Outsmart hackers and detect frauds
TranSwap_Machine learning: Powering business growth

Regardless of the size of the organisation, fraud is a big challenge in the Fintech sector. And machine learning can be the perfect solution.

The typical rule-based fraud detection requires manual work, multiple verifications, and long-term processing only to catch obvious fraudulent scenarios. But machine learning, on the other hand, can find the hidden and implicit correlation of data with automatic detection and fewer verification measures, all in real-time.

All in all, machine learning and AI is changing the way we do things in the Fintech industry. They play a big role in ensuring that our services are secure, convenient, personalised, efficient, and automated.

At TranSwap, we utilise machine learning and AI to ensure that our services are safe, secure, and convenient. We also have a Research and Development Centre in the United Kingdom to not only create better solutions for our customers but also drive innovation in the Fintech industry.

TranSwap is a technology-led cross-border payment platform that helps you to move money between different countries easily and at the lowest cost possible. To get started with our services, get in touch with us now or visit our website at

Have a specific topic you would like us to cover next? Just let us know!

“Use at Your Own Risk” Disclaimer. The information and publications provided are not intended to be and do not constitute any financial advice. We do not make any warranties about the completeness, reliability, and accuracy of this information. Any action you take upon the information is strictly at your own risk, and we will not be liable for any losses and damages in connection with the use of information.