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Machine learning in finance: Why, what & how

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Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms.

Machine learning is making significant inroads in the financial services industry. Letís see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology.

Definitions
We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related. For the sake of simplicity, we focus on machine learning in this post.

The magic about machine learning solutions is that they learn from experience without being explicitly programmed. To put it simply, you need to select the models and feed them with data. The model then automatically adjusts its parameters to improve outcomes.

Data scientists train machine learning models with existing datasets and then apply well-trained models to real-life situations.

The difference between AI, data science, deep learning, and machine learning in finance

The model runs as a background process and provides results automatically based on how it was trained. Data scientists can retrain models as frequently as required to keep them up-to-date and effective. For instance, our client Mercanto retrains machine learning models every day.

In general, the more data you feed, the more accurate are the results. Coincidentally, enormous datasets are very common in the financial services industry. There are petabytes of data on transactions, customers, bills, money transfers, and so on. That is a perfect fit for machine learning.

As the technology evolves and the best algorithms are open-sourced, itís hard to imagine the future of the financial services without machine learning.

That said, most financial services companies are still not ready to extract the real value from this technology for the following reasons:

Businesses often have completely unrealistic expectations towards machine learning and its value for their organizations.
AI and machine learning research and development is costly.
The shortage of DS/ML engineers is another major concern. The figure below illustrates an explosive growth of demand for AI and machine learning skills.
Financial incumbents are not agile enough when it comes to updating data infrastructure.
Talent shortage of machine learning engineers in finance

We will talk about overcoming these issues later in this post. First, letís see why financial services companies cannot afford to ignore machine learning.
2 Weeks Ago #1
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