Deep Learning: What Is It and Why Is It Important for Finance?

Michael Yurushkin -

Data science has been an essential component of the finance sector for a while now. The finance industry provides the necessary ingredient for deep learning - big data. This post will dive into the basics of deep learning, its applications, and its relation to finance.

Deep Learning 101

Deep learning is a subfield of machine learning. It focuses on algorithms based on the function of the brain called artificial neural networks. This means it is a part of a bigger picture, as illustrated below:

Source: Artificial Intelligence, Machine Learning, and Deep Learning. What’s the Real Difference? | medium.com

Now let’s look at how all these concepts link to each other.

Artificial intelligence describes the idea of machines mimicking human actions. In essence, AI shows the human mind's traits,which are problem-solving, learning, and rationalizing.

Machine learning involves the study of stats models and algorithms. This allows the machines to perform specific tasks without the explicit instructions of humans. They use learned patterns and inferences from their studies.

As mentioned before, deep learning is a sub branch of Machine Learning. The rationalization for its naming is that it utilizes vast amounts of data. With this information, the Deep Learning model identifies the errors and corrects them on its own. So for a complex input, a Deep Learning model provides an output based on the data provided. In doing so, the system learns from its successes and failures after recording data.

How Does It Work?

Most deep learning methods use neural network architectures. Models created using these are called deep neural networks. “Deep” refers to the number of hidden layers in the neural network. Deep networks contain as many as 150 layers, whereas the traditional ones have around 2-3 hidden layers.

We train these models with large sets of labeled data. Neural network architectures study features directly from the data, without manual interference.


Source: Artificial neural network architecture | researchgate.net

Convolutional neural networks (CNN) are one of the most common types of deep neural networks. It convolves learned features with input data utilizing 2-D convolutional layers. They are often used to process 2D data, like images.

CNN does not need manual feature extraction. It extracts them directly from images. Features of interest are not pre-trained, meaning they get learned while the network trains on an image collection. It makes deep learning models incredibly accurate for computer vision (e.g., object classification).

Deep neural networks detect different features using hundreds of hidden layers. So how does an example of this process look? Using the example of an image processing model, it could follow these steps:

  1. The first layers learn to detect edges, corners, etc.
  2. The middle layers learn to detect parts of objects. Regarding faces, they might learn to respond to eyes, noses, etc.
  3. The last layers recognize full objects in different shapes and positions.

This was just an overview of deep learning. If you are looking for in-depth insight, be sure to check BroutonLab's free course on deep learning.

Applications of Deep Learning In Finance

The financial industry generates trillions of data points that need serious processing power. That's why it is a premier spot for the application of deep learning techniques. We have isolated these specific areas:

  • Algorithmic Trading
  • Stock Market Predictions
  • Robo-advisory
  • Automated risk assessment
  • Credit Card Customer Research
  • Customer Data Management
  • Security Breach Detection

Algorithmic Trading

This process involves the creation of a computational model to install buy-sell decisions in the market. In theory, they base them on mathematical models. Yet, traders have started using deep learning techniques that rely on approximation models.

Having the fastest method of analyzing data is essential to remain competitive. To make real-time decisions, financial institutions rely on traditional and non-traditional data analysis. Combining real-time and predictive analytics opens doors to new opportunities.

Stock Market Prediction

The neural networks in Deep Learning predict the stock values based on the historical data and present market situation. They are utilizing the full potential of the hidden layers. It causes an increase in the prediction's accuracy.

Techniques used for Deep Learning price forecasting:

  • Recurrent neural network (RNN)
  • Long Short Term Memory Models (LSTM)
  • Multilayer Perceptron (MLP)

Analytics is the core of financial services. Predictive analytics reveal patterns in the data that can predict future events that need immediate action. Such events are prices, customers' lifetime value, and stock market moves. For this, they use social media, news trends, etc.These techniques answer the most complicated question - the optimal way to intervene.

Robo Advisory

The primary function of a Robo-advisor is automated customer onboarding. They collect essential data including investment goals, personal information, and wealth management experience. For this, they use a detailed questionnaire.

Upon the completion of data collection, Robo-advisors analyze it using deep learning. To maximize gains, they test finances, risk tolerance, and investment strategies. Based on that analysis, they create a customized portfolio. Such a portfolio contains critical wealth management tips, investment strategies, etc.

Automated Risk Assessment

Risk management handles an institution’s security, strategic decisions, and trustworthiness. Deep learning in finance has revolutionized the approach to handling risk management. Deep learning models define the vectors of business development.

Risks can come from competitors, investors, regulators, or the company customers. Furthermore, they can be different in priority and potential losses. Hence, deep learning needs to identify, rank and track these risks. Algorithms can increase the risk scoring models and enhance cost efficiency using larger amounts of customer data.

Credit Card Customer Research

Making credit accessible to a range of people is what most banks want to do. To do that, finance companies want to create a faster way to test credit risks. This system needs to provide more meaningful questions during credit card applications.

A valid solution is a combination of several data sources and calibration methods against training data. Companies like Titan, JP Morgan Chase, Swiggy, etc., have all implemented Deep Learning for system automation.

Elder Research has reported that using a predictive model reduced the number of credit card accounts that defaulted by over 10%. This model used the client evaluation dataset rather than their production model.

This proves that a good predictive model can be a driving force behind sales.

Customer Data Management

As noted before, data is the most crucial resource for financial firms. Efficient data management is a fuel for business success. Data can come from social media activity, mobile interactions, transaction details, and so on.

Looming issues for many financial specialists are semi-structured or unstructured data. Manually processing them is no small feat.

Integrating deep learning techniques to extract real intelligence from data makes this easier. These techniques include natural language processing, text analytics, and data mining. Data needs to contribute to better business solutions - resulting in increased profitability.

Let us give an example. Deep learning algorithms can analyze specific financial trends and market developments. The source is customer financial historical data. The end output is an automatically generated report, saving both time and resources for the company.

Security Breach Detection

Guaranteeing the highest level of security to their users is essential for financial companies. As criminals develop new ways of hacking, data scientists can create perfect algorithms for their detection.

In the stock market, deep learning algorithms can identify abnormal patterns. They generally denote manipulations. The staff can act upon it later. The best thing about these algorithms is the self-teaching ability. As time progresses and as they feed them with more data, such algorithms become increasingly effective and intelligent.

Final Words

In this brief post, we’ve touched upon the basics of deep learning. It represents a subset of machine learning which displays human-like decision making. Making sense of big datasets is what characterizes deep learning.

Convolutional neural networks are at the heart of any deep learning model. These models can provide many incredible feats, such as facial recognition, investment modeling, and CRM, utilizing many hidden layers.

But the area that benefits the most is the financial sector. We have discussed the following:

  1. Algorithmic Trading
  2. Stock Market Predictions
  3. Robo-advisory
  4. Automated risk assessment
  5. Credit Card Customer Research
  6. Customer Data Management
  7. Security Breach Detection

However, this is not where the story ends. Many predict that the financial industry will become heavily reliant upon deep learning. Engineers will play a fundamental role in decision making.