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FinTech
6 minutes read

Advances in Financial Machine Learning

by Robert Kazmi
FinTech
6 minutes read

When it comes to the most recent advances in financial Machine Learning, things can get pretty technical. As Big Data and increased and more affordable computer power change the landscape, financial companies of all sorts are implementing new technologies. It is difficult to keep up with what is happening, and being outdated means risking the opportunity to innovate and satisfy your users. 

Understanding what is going on for the FinTech market in terms of Machine Learning does not have to be a responsibility solely for technical experts. In this post we discuss some of the most important advances in financial Machine Learning so that you too can understand the state of the industry.

The Impact of Financial Machine Learning

In case you are wondering, Machine Learning belongs to the broader set of techniques known as Artificial Intelligence (AI). It is a computational technique that uses large amounts of data to perform specific tasks. It can’t do everything the same way a human can, but it is perfect for a wide range of mostly simple activities. By granting a computer the ability to learn from data, repetitive tasks can be easily automated, helping reallocate resources in other more productive activities

The use of computational techniques within the financial industry is known as FinTech, short for financial technologies. Most of it occurs through powerful apps where users can easily perform a wide range of financial operations directly from their mobile devices or desktop computers. 

In general, these are some of the most important strategic ways Machine Learning has impacted the FinTech industry:

  • Improved financial access for everyone. 
  • Increased attention to digital products and services. 
  • Innovative products for all customer segments.
  • Process automation and optimization.
  • Enhanced customer experience. 

These changes have allowed new companies to grow at unprecedented rates. Market capitalization for financial companies used to take many years. Now it can be done in just a couple. Machine Learning has also helped existing players deploy new products and services. The competition is fierce, and technology is a key element that needs to be understood in order to stay afloat

Challenges

Although Machine Learning is helping change the financial industry for good, companies have many challenges to face. These are some of them:

  • Creating the right internal teams: companies need to bring in the right professionals to build great digital products. Multidisciplinary teams are the best choice for this.
  • Onboarding executives and directors: top management must believe in the importance of new technologies. A company may have the right team, but if the management is not properly aligned, efforts may fail. 
  • Provide the right training: digital technologies are moving pretty fast. It is important to stay up to date in order to avoid adopting outdated technologies. Andrew Ng, one of the world’s leading experts in AI techniques like Machine Learning, offers a free course on the subject. 
  • Align the AI strategy: the need for a specific AI strategy is something that many companies are starting to understand. AI isn’t just a nice thing to have, it is a necessity.
  • Finding the right development partner: not all companies have the necessary in-house talent to do what they need, and sometimes they don’t even need it. Finding the right partner is key for those crucial technology-related processes that do not require a dedicated internal team. 

Machine Learning Uses for Finance

In terms of the specific ways that Machine Learning is impacting finance, these are the most relevant:

Trading

This technique, also referred to as algorithmic trading, is the use of computational power to develop tools that can help make better trading decisions. Although some tools can completely replace traders, the best option is to pair up these tools with human traders. 

The combination of humans and computers working together is better than each of them individually. Nonetheless, it is important to keep in mind that if computers’ processing power and the available tools and techniques keep evolving, in the future we might see smart apps replacing traders for good. 

Risk Assessment

Calculating risks is a hard task. Many variables need to be considered, and most importantly, imagining the future is difficult. When we depend on data, we can only consider what has already occurred.

Even with these limitations, Machine Learning algorithms have the power to help calculate risks. As long as conditions do not change drastically and uncertainty is stable, there is a great opportunity.  

Fraud Detection

Technology isn’t just helping financial companies. Criminals are also taking advantage of it. In order to stay safe from scams, companies are implementing the most advanced fraud solutions. Through them, it is possible to identify irregular activities and notify users immediately. 

It is important to understand that, for fraud detection to work, IT teams need to stay up to date. They constantly need to deploy new and more powerful solutions. 

Portfolio Optimization

Using large amounts of data, computers can calculate the best portfolio combinations. Just like for trading activities, portfolio algorithms can be used to help financial employees balance clients’ assets based on specific goals. Together with risk assessment algorithms, they can provide decision-makers with valuable information. 

What’s to Come

FinTech is a dynamic and constantly evolving industry. There is no reason to believe that its innovative pace will erode any time soon. Companies need to understand this and embrace the challenges of going digital. In particular, they must focus on finding the right development partner that can help them implement the technologies they need in a strategic and optimal way. 

Koombea has been building world-class financial software tools since 2007. We realize the importance of implementing the latest technologies to your business, and we understand that every company is unique. That’s why our custom Agile methodology is designed to easily adapt to any circumstance, allowing for a smooth and productive workflow. 

If you want to find out more about our FinTech app development & software services and how we can help you implement the latest advances in financial Machine Learning features, contact us for a free consultation.