Top 10 Business Applications of Neural Networks in 2023

Here are the top 10 business applications of Neural networks to consider in the year 2023

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks are a subset of machine learning. They provide the foundation of deep learning techniques. And the human brain inspires their name and form, and they replicate the way real neurons communicate with one another.

In real-world commercial applications, neural networks are rapidly being deployed, and in certain situations, such as fraud detection, they have already become the technique of choice. Their application in risk assessment is expanding, and they have been used to depict complicated information for marketing segmentation. This surge in applications spans a wide spectrum of commercial interests, from banking to forecasting to manufacturing. In this article, we will see some of the top 10 business applications of neural networks in 2023. Read to know more about the business applications of neural networks.

1. eCommerce

This neural network technology is utilized for a variety of reasons in eCommerce. However, personalizing the purchaser's experience is the most common example of artificial neural network use in eCommerce. AliExpress, Amazon, and other eCommerce sites, for example, employ AI to display related and recommended items. The compilation is based on the behaviour of the users. The system analyses the features of certain objects and displays those that are comparable. In other circumstances, it specifies and remembers the person's preferences and displays things that fit those choices.

2. Healthcare

Because great precision is required, creating and training a neural network for use in this business application is extremely tough. For many years, it looked like a pipe dream to employ this technology to examine and diagnose patients. But it has finally become possible. IBM Watson is the world's most powerful artificial intelligence. The neural network for medical practice takes two years to train. For the system's learning, millions of pages of medical academic articles, medical records, and other information were uploaded. Based on the patient's symptoms and anamnesis, it may now suggest the diagnosis and provide the optimal treatment plan.

3. Finance

This technology is utilized for a variety of reasons in this business. However, personalizing the purchaser's experience is the most common example of artificial neural network use in eCommerce. Amazon, AliExpress, and other eCommerce sites, for example, employ AI to display related and recommended items.

4. Logistics

This sector requires a lot of management, which will be done manually by staff from various organizations. However, neural networks are now capable of routing and dispatching. FourKites is another option. This is a visibility programme that operates in real-time. It aids in the planning and monitoring of routes, as well as the prediction of delivery times.

5. Security

Neural networks are commonly employed to defend computers from viruses, fraud, and other threats. Symantec's ICSP Neural is one of the instances. It defends against cyber assaults by detecting faulty USB devices that carry viruses and exploits zero-day vulnerabilities. Shape security, which provides many financing solutions, is another example of applying AI and ML for security reasons.

6. Vehicle Building

In this field, AI and machine learning are utilized to automate procedures. Tesla, for example, employs a neural network in its autopilot system. It recognizes road markings, identifies impediments, and makes the road safer for the driver with the assistance of trained artificial intelligence.

7. Insurance

Insurance is another area that benefits from the advantages afforded by NNs.  Neural networks are used by insurance firms to estimate future loss rates and alter premiums. As a result, their profit margin expands.

8. Banking

Neural networks may also be used to automate banking operations. These operations include predicting currencies, approving credits, fraud detection, approving mortgages, assessment of debt risk, etc.

9. Retail and sales

Following on from the previous commercial use of ANN, retail and sales are also finding these systems and algorithms to be incredibly beneficial. They are used for demand forecasting and sales forecasting. An ANN-powered system can compute the number of goods that shops should have on hand. As a consequence, they may focus on increasing their profitability.

10. Marketing

The use of large data to train neural networks is the most recent advancement in data science. Although this technology has been around for a while, the more recent emergence of Big Data has made it far more valuable for marketing. They help in consumer behaviour, creating content predictions, understanding complex buyer segments and more.