Unleashing the Power of Neural Networks in Finance

Table of Contents

1. Introduction
2. Classical Machine Learning Models
3. Limitations of Classical Machine Learning Models
4. Neural Networks: An Overview
5. History of Neural Networks
6. Structure of Neural Networks
7. Training Neural Networks
8. Applications of Neural Networks in Finance
9. Detecting Financial Fraud with Neural Networks
10. Importance of Testing Neural Networks
11. Conclusion

Neural Networks: An Overview

Neural networks are a Type of machine learning model that is designed to replicate the functioning of the human brain. They are made up of neurons that are linked by layers and connections between them. A neuron is a computing unit that receives information, performs simple calculations on it, and passes it on. Neurons are combined into three types of layers: the input layer, the output layer, and Hidden layers made up of artificial neurons, which are also known as nodes. A SYNAPSE is a connection between two neurons, and a characteristic of a synapse is its weight. It makes the input information change when it is passed from one neuron to the next.

History of Neural Networks

The idea of neural networks was first proposed in 1943 by McCulloch and Pitts, who developed a computer neural network model Based on mathematical algorithms and the theory of brain activity. They presented a network of electronic neurons and demonstrated that it could perform virtually any imaginable numerical or logical operation. In 1957, Rosenblatt developed mathematical and computer models of the brain's Perception of information, based on a two-layer learning neural network. This network was trained using the arithmetic operations of addition and subtraction. This was one of the first examples of a neural network called the 'single layer perceptron', which could be used to decide if an input belonged to a particular class or not.

In 1969, the idea of neural networks was harshly criticized by Minsky and Papert. They pointed out serious problems that hindered the effective use of artificial neural networks. For example, they drew Attention to the impossibility of implementing the "Exclusive OR" function in neural networks, as well as pointing out insufficient computing power available back then. This article undermined the interest in neural networks for some time. However, the field was still of interest to many researchers. Computers improved, computing power increased, scientists solved the "Exclusive OR" problem, making advances in the field more and more realistic, which brings us to the multi-layered neural network of today... deep neural networks.

Structure of Neural Networks

A deep neural network is useful when You need to replace human labor with autonomous work without losing its efficiency. In a nutshell, the idea behind the technology was to replicate the functioning of the human brain, represented here as a multitude of linked neurons. A neuron is a computing unit that receives information, performs simple calculations on it, and passes it on. Neurons are combined into three types of layers: the input layer receives inputs, and the output layer provides predictions. Between the input and output layers, lie hidden layers made up of artificial neurons, which are also known as nodes. A synapse is a connection between two neurons, and a characteristic of a synapse is its weight. It makes the input information change when it is passed from one neuron to the next.

Training Neural Networks

During training, each neuron is preset to a bias value, and the training data is passed through the network, activating functions in every neuron, which generate outputs. These outputs are then compared with the known correct results (for example, labeled data). The difference between the actual output and the known correct result is then fed back through the network to modify the values of the weights on the connections between the neurons in order to minimize this difference. This process is called backpropagation. As more training data is fed through the network, the weights are adjusted as the network learns. This continues until the outputs are considered good enough to end training.

Applications of Neural Networks in Finance

When it comes to finances, neural networks can help us in the areas of banking and credit risk, market investment, and fraud. The first one contains credit risk predictions and macroeconomic predictions. The Second one consists of financial predictions, stock trading, and portfolio management. And the third one has fraud prediction and detection. Artificial neural networks allow us to forecast the exchange rates, stock movements, and prices based on their historical data and the information extracted from the market news. For example, they can be applied in stock trading for the buy-sell-hold forecasts where stocks are traded separately using the daily close prices.

Detecting Financial Fraud with Neural Networks

As technology advances, more importance is placed on online transactions, but at the same time, ways of committing financial fraud are also becoming more sophisticated. Luckily, artificial neural networks can help to keep our finances safe. Deep learning and artificial neural networks applications are powering systems capable of detecting all forms of financial fraud. These applications can identify unusual activity, for instance, a transaction occurring outside the established time frame. This allowed Visa to cut credit card fraud by two thirds by analyzing information such as payment method, time, location, item purchased, and the amount spent. Even a small deviation from the norm in any of these categories can highlight a potential fraud case.

Importance of Testing Neural Networks

As we can see, neural networks are used to solve rather critical problems. This is why it is especially important to test them. Testing neural networks involves verifying that the network is performing as expected and that it is producing accurate results. This is important because if the network is not performing as expected, it could lead to incorrect predictions or decisions. Testing also helps to identify any bugs or errors in the network, which can be fixed before the network is put into production.

Conclusion

In conclusion, neural networks are a powerful tool that can be used to solve a wide range of problems, from financial predictions to fraud detection. They are designed to replicate the functioning of the human brain and are made up of neurons that are linked by layers and connections between them. Training neural networks involves adjusting the values of the weights on the connections between the neurons in order to minimize the difference between the actual output and the known correct result. Testing neural networks is important to ensure that they are performing as expected and producing accurate results.