An Overview of Deep Learning in Finance

What makes new moves in the financial sector? Computational Finance, Data Analytics, Machine Learning, and Deep Learning have been essential parts of the development of finance for many years. Combining advanced computational techniques with complex financial complexity has led to a shift in financial models.

Integrating deep learning is a transformative force in a dynamic financial environment, where data-driven insights hold the key to informed decision-making.

Our today’s article will center on deep learning in finance, exploring the applications, challenges, and opportunities it presents.

What is Deep Learning?

Deep learning comes as a subfield of machine learning that deals with algorithms. It’s inspired by the structure and function of the human brain known as artificial neural networks.

We can say that deep learning models mimic the way human brains process information. Imagine a vast network of interconnected neurons much like the synapses in our brains—except these are digital neurons, working together to learn and understand complex patterns from data.

The term “deep” in deep learning refers to these systems’ multi-layered architecture. This hierarchical approach to data interpretation enables deep learning models to excel at tasks like image recognition, language translation, and data analysis.

Artificial intelligence aims to teach computer science how to think and learn as people do. Deep learning is the technology behind many artificial intelligence applications used every day, such as:

• Digital assistants
• Voice-activated television remotes
• Fraud detection
• Automatic facial recognition
• It is also a key part of new technologies like self-driving cars, virtual reality, etc.

Uses of Deep Learning in Finance

Algorithmic Trading in Finance

Algorithmic trading refers to the process of creating a computational model for making buy-sell decisions in the financial markets, and machine learning tools make their mark here as well. Besides relying on mathematical models, a trader using deep learning techniques can use approximate models to execute buy and sell trades.

The algorithmic trading process proves very time-consuming and error-prone. Sometimes, the transaction may even fail. Before machine learning was introduced into the financial field, finance staff had to manually deal with transaction errors, identify causes, manage portfolios, and resolve issues.

However, this workforce-intensive process has been simplified by using deep learning networks to detect issues and provide recommendations for resolution automatically.

An effective solution can handle huge amounts of data, news, and other market events. For this reason, Recurrent Neural Networks (RNN) models of deep learning emerge and are preferred, as they can control both textual data and time-dependent features of the model.

Customer Data Management

Customer analysis examines customer behavior and preferences. The data contains information such as purchase patterns, portfolio management, and transaction types.

Machine learning and deep learning model-based solutions are used for customer analytics. Some key examples of customer analytics include:

• Customer Segmentation: Customers can be grouped into segments based on information such as demographics or behavior. By identifying these factors, organizations can better understand what their customers seek.
• Recommendation System: A recommendation system uses historical data to suggest products or services that customers may want.
• Sentiment Analysis: Determine customer satisfaction with a product or service. Sentiment analysis uses text analysis techniques to analyze and extract useful information from textual data. Machine learning algorithms like Support Vector Machine (SVM) and Naïve Bayes and DL algorithms like Recurrent Neural Network (RNN) are all used for this.

One of the most practical applications of deep learning in financial industry is in customer relations. Financial firms use machine learning technology like chatbots to improve customer experience through on-demand help and real-time recommendations.

Besides, IoT devices generate useful data to understand customer behavior and preferences. This results in a better overall customer experience and higher customer satisfaction and retention rates.

Stock Market Prediction

Deep learning using large historical data sets in the financial industry can predict stock prices and influence trading decisions. The neural networks in deep learning can also predict future trends and identify upcoming risks and opportunities to make better investment decisions.

Methods of price prediction using deep learning:

• Recurrent Neural Network (RNN): RNN studies historical financial transaction data. It then looks at sequential patterns to predict short-term price movements by learning from trends in the past and market sentiment.
• Short-term Long-term Memory (LSTM) Model: LSTMs are a type of RNN that capture complex long-term connections in data. Your organization can use this analysis to predict prices over the medium to long term.
• Multilayer Perceptron (MLP): MLP is a flexible neural network that predicts prices in the financial sector by processing different types of data. Simultaneously, capture the complex links between factors and prices, and work in different forecast periods.

Automated Risk Assessment

Risks in finance stem from various sources such as competitors, investors, regulators, and even the company’s own customers. Moreover, these risks vary in terms of importance and potential impact.

Deep learning has reshaped risk management in the finance sector. It’ll effectively identify, prioritize and monitor these risks. By harnessing algorithms, it refines risk-scoring models, leveraging extensive customer data to enhance accuracy and cost-effectiveness.

JPMorgan Chase, one of the world’s largest and most renowned financial institutions, is a really good example of embracing deep learning to enhance risk assessment. The financial company employs sophisticated deep learning algorithms to analyze vast amounts of data and predict potential market risks, fraud, and credit defaults.

This enables JPMorgan Chase to make more informed trading decisions and mitigate potential financial losses. Through deep learning technique integration, the financial company showcases how a leading financial institution can harness the power of advanced AI technology.

Security Breach Detection

Financial fraud poses a significant concern for financial companies. The FBI’s 2021 Internet Crime Report in the United States revealed an alarming 847,367 Internet fraud complaints, causing a staggering $6.9 billion USD loss. These fraud cases encompassed various areas, including credit card scams, loan fraud, false claims, identity theft, and document forgery.

To combat this challenge, machine learning models emerge as a promising solution. Deep learning algorithms can detect unusual patterns indicative of fraud and risk management. These algorithms can learn independently over time as they ingest more data, enhancing effectiveness and intelligence.

But this work doesn’t stop. The machine learning models must be consistently monitored and trained using an MLOps platform. As cyber criminals devise new tactics, data scientists can create perfect algorithms for their detection.

Why Is Deep Learning Important for Finance?

Deep learning models are a subfield of machine learning that uses neural networks, in particular, to perform more complex tasks involving unstructured data. In addition, machine learning careers are thriving in the finance industry, driving innovation and data-driven decision-making. Machine learning algorithms group and predict organized info, while deep learning handles trickier tasks like images, sound, and text.

Machine learning and deep learning are strongly correlated. To answer the question of why deep learning is important for the finance industry, let’s explore three aspects:

• Pattern recognition: Machine learning and deep learning use math and statistics to find patterns in financial data. This is much faster and more accurate than humans figuring out patterns independently. Algorithms such as classification or clustering can detect patterns to separate different groups, even in new data.
• Improve efficiency: The financial services industry must be fast and accurate in today’s fast-paced economy. Instead of manual methods, organizations combine two main approaches: human-made rules and ML-based solutions. Humans censor and issue requests, then the machine learning model learns and provides resolution methods for decisions. This saves time, reduces errors, and supports complex cases.
• Constant improvement: In cases like spotting fraud or objects, data changes over time. If you’re not using machine learning or deep learning, you’ll need to adjust for these changes manually. But the system can learn and adapt with ML/DL and good monitoring. This keeps accuracy high without needing constant human changes.

Challenges and Limitations of Deep Learning in Finance

Deep learning systems come with their own set of challenges.

Firstly, they learn through observations, which means these systems learn from what they see in training data. The extent of their knowledge hinges on the volume and diversity of data they encounter. If a user’s dataset is small or derived from a single source, the resulting models may lack the generalizability needed for broader applications.

Another significant concern that arises is data bias. This raises crucial questions about the ethical implications of their predictions. If the data used for training contains biases, the models will repeat those biases in their predictions.

In addition, we face difficulty in determining the optimal learning rate for these systems. How do we strike the right balance? If the rate is too high, then the model converges too quickly, producing a less-than-optimal solution. Conversely, a low rate can impede progress, sending the system into a vicious cycle of inefficiency and failure.

Finally, there is the issue of facility costs. High-performance graphics processing units (GPUs) are key in improving efficiency but do their benefits outweigh the costs? These units not only put a strain on the budget but also consume a significant amount of energy, making us think about the environmental impact of the technology.

Final Thoughts

The application of deep learning is significant processing power, using GPUs with performance-enhancing capabilities to handle the required computation. The deep learning chip market is growing rapidly and is forecast to exceed $21 billion by 2027.

We’ve touched upon the basics of deep learning. Deep learning is a subset of machine learning, and machine learning algorithms are a subset of artificial intelligence (AI).

Looking for a deep learning partner or simply want to learn more about this special AI field? Contact us now!