Neural networks in finance & insurance

Let's move to other spheres where neural networks are popular - finance and insurance.

Neural networks in finance

The main purpose for using artificial neural networks in the sphere of finance and banking is their capability of forecasting. Analyzing the big scope of appropriate information, neural networks are able to make predictions. Moreover, the more information that was analyzed, the more accurate the prediction will be. Forecasts are extremely important for this sphere of activity.

Neural networks in business forecasting serve as a powerful tool for predicting exchange and stock rates and many others. However, that's not the only area where artificial neural networks have found an application within this sphere.

Banks are used to give loans in accordance with their statistical data about a person. Generally, the software is driven by statistical techniques but things have changed. Nowadays all statistical data is calculated by artificial neural networks and based on the result the final decision is made.

The neural networks in finance and investing are quite widespread. A company not only has fewer expenses but gets a system that is able to learn and improve its identification of credit risks.

Neural networks in the field of insurance

The insurance industry has got absolutely new 'superpowers' for tracing insured and non-insured events. For example, some insurance companies already use GPS tracking in order to find the client's car in case of a carjacking. Also, they keep an eye on speeding and that, in turn, may affect the insurance rate.

Artificial neural networks have also found their place in this industry. They are used to segment policyholders into groups that help companies to find out an appropriate pricing.

Another application of artificial neural networks is related to fraud detection since they are able to be taught to detect fraudulent claims, as well as strange circumstances.

Also, neural networks can be useful when it comes to the retention of customers. Since the competition in this industry is tough, every customer is important to a company. With the help of neural networks, insurance companies are able to detect the reason why the customer left by means of analyzing his or her history. Also, they can be applied for managing special offerings for certain groups of customers to motivate them to stay.

Artificial neural networks in insurance

Neural networks in telecommunications & management

It seems like the time for our last couples of spheres. They are telecommunications and operations management.

Neural networks in telecommunications

This technology in the sphere of telecommunications is able to cover quite a big range of areas. Their forecasting capabilities are used to conduct analysis of customers, as well as call data. That's needed to predict the reasons and moment when customers move to another company.

Also, the forecasting capabilities of artificial neural networks are used for defining the efficiency of upcoming promotional campaigns and searching for the clients who are likely to bring the most profit.

Except for these cases, they are also used for real-time analysis of network traffic in order to optimize routing and increase the service quality and much more.

Neural networks in the field of operations management

The sphere of manufacturing has been greatly automatized for the last couples of decades. With the development of the IT sphere, employees working in factories have got inner mobile applications intended to increase their efficiency, while the machinery got an artificial intelligence.

Artificial neural networks are successfully used for different types of scheduling. Manufacturers have started using neural networks for scheduling their machinery, lines of assembly and so on. The technology is also used to solve other problems connected with scheduling. For instance, to establish a timetable, schedule of a project or the schedule of multiprocessors.

Another artificial network example is their usage in manufacturing processes which sometimes covers a wide range of activities, including the control and scheduling of the whole floor's work or even improving the system of manufacturing itself.

Nowadays, mainly all manufacturers use robotic tools to control the quality of their products. For example, some of them use cameras to identify defective products. The artificial neural network behind this camera is able to learn over time, so the number of unnoticed defective products will be reduced in the future.

So, artificial neural networks are capable of being used not only in the IT industry but in other fields of activity as well.

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