Using neural networks in business

In recent years, neural networks, or NNs for short, have come a long way from a “raw” and little-known technology to a promising tool that is being implemented by global brands from a variety of business sectors, from healthcare and the financial sector to automakers and the media. And it gives its results.

For example, by personalizing its advertising using neural networks, The Economist was able to revive the reader base – the publication not only got rid of the outflow, but also managed to sell 90,000 new paid subscriptions in a year. At the same time, Volkswagen began using NNs-based analytics in marketing, boosting car sales by as much as 20%.

These are just two examples of the successful use of neural networks, when they brought millions of dollars in revenue to a business, but in practice there are many more such cases.

The important thing is that neural networks are not the prerogative of large companies. They can just as well be used by small and medium-sized businesses, which explains the general trend of popularization of NNs in the world market. So, according to Deloitte, in recent years, the use of technology has grown by a record 270%.

Therefore, we propose to talk about exactly how neural networks can be used in different areas of business, and what benefits it will bring to companies.

First of all, it is worth saying a few words about what neural networks are. In fact, this is the most successful implementation of artificial intelligence, which imitates the work of the human brain at the software level: digital neurons are connected by virtual synapses through which data is transmitted.

Due to the similarity with the biological human brain, the neural network is able not only to “remember” data, but also to learn itself. This is its main difference from classical software, which is capable of executing only the logic of algorithms embedded in them.

The basic neural network consists of three layers of artificial neurons:

• input – receives and processes external information, classifies it, and then passes it to the next layer.
• hidden – parses the data passed by the input layer, performs processing, and then passes it to the third layer.
• output – provides the user with the final result of processing.

The most common use cases for neural networks are the generation of images and text content. But, of course, the possibilities of technology are not limited to this. We propose to consider the practical side of using NNs in various sectors of real business.

Healthcare

Neural networks are able to recognize signs of diseases on x-rays and on the basis of various analyzes, establish diagnoses and offer a treatment regimen, taking into account the age and condition of the patient.

It is important that a well-trained neural network is rarely wrong, notices the smallest details, can predict the likelihood of developing diseases based on secondary factors and is not affected by the human factor. This makes it an indispensable assistant in medical practice.

In addition, NNs is able to independently calculate staff shifts and client visit schedules, which allows minimizing queues at polyclinics and improving the quality of medical services provided.

Industry

This is one of the areas in which neural networks can be used as widely as possible. Here are some of the main uses:

• Control of the operation of mechanisms. NNs are able to provide full control over the technical condition of machines, machines, ventilation systems and other mechanisms, predict downtime and potential emergencies. In addition, they notify in advance of the need for maintenance. The neural network can receive up-to-date data on the functioning of mechanisms using special sensors.
• Design and product design. Using an engineering approach, neural networks are able to provide a generative design of created products, their drawings and related documentation.

For example, an engineer can enter the desired product parameters into the system: materials, boundary weight, dimensions, strength, and cost, after which NNs will analyze the data and generate hundreds of implementation options.

• Robotics control. In industrial enterprises, robots are widely used to perform various tasks: assembly, painting, product stacking, and so on. However, even in this case, it is necessary to control the operation of mechanisms and conveyors. The neural network will automate these processes completely.
• Cost forecasting. NNs can predict the cost of raw materials much more accurately than humans. In case of errors, they independently improve their algorithms and do not allow them again. This will increase the margin of manufactured products.

Logistics

Neural networks can independently build optimal logistics routes, taking into account all points of loading and unloading products, predict fuel consumption, depreciation and monitor the technical condition of the fleet. In addition, NNs can also solve broader problems. For example, they excel at inventory forecasting and supply planning.

Banks and financial sector

Already, banks are widely using neural networks for scoring and fraud monitoring in order to minimize the percentage of loan defaults and reduce the risks of financial fraud.

NNs will also be a great solution when launching voice assistants and chatbots, and will help save a significant budget for maintaining a call center. In fact, this way you can minimize the number of live operators – they will only deal with non-standard situations, and artificial intelligence can easily handle the main flow of questions.

In addition, neural networks will help reduce the operational burden on employees by automating document processing. For example, AI can independently read and fill in customer data when opening accounts and, at the same time, perform information verification.

AVADA MEDIA provides services for the development and implementation of neural networks for various business areas. Starting work on the project, we carefully study the priorities and vision of the client, determine the needs, and only after that we proceed to the technical implementation.

This approach allows us to create the most business-oriented NNs that effectively solve the tasks.