Have you ever wondered how so many automated processes act as efficiently as responses in the human brain? That’s because of technology that is designed to emulate what goes on in our minds. Artificial neural networks, or ANN, are computing systems inspired by the neural networks that constitute the human brain. Through the algorithms that are structured through input, hidden, and output layers, several sectors are capitalizing on the visual results from this framework. Here are just some of the ways these efforts are helping organizations get ahead of the game.
#1 Sales Forecasting
One of the common applications for neural network software is by helping retailers and other outlets project their sales. With trained algorithms, networks are applied to predict the future values of time series that consist of the weekly demand on items. In a supermarket, for example, a neural network model can influence indicators on pricing, advertising campaigns, and holiday sales drivers. The design of a neural network forecasting system supports the market’s management in the process of determining the expected sales figures through the use of these different applications.
The performance of a neural network is evaluated by comparing it with prediction techniques under a properly formatted computer program. Deep learning has allowed management and data analysts to use these software applications to mine vast amounts of data for pattern recognition. A neural network delves into analytical tools sitting on a hotbed, without an efficient and state-of-the-art processing unit, allowing for the extraction of anything valuable through all that information. Deep learning has the potential to implement repetitive tasks to better assess overall trends.
#2 Customer Research
Neural networks offer a great prediction model to allow companies to better understand customer behavior. This starts with input layers within the network where standard information can be provided for a financial institution. Monitoring the information provided for certain applications allows an institution to understand customer demand within these data sets. The installation of these demands will give banks a better idea of what is being sought of their services. This can end up providing greater flexibility and a better approximation of an applicant’s needs.
The physical structure of the neural network takes this input, pushing through hidden layers that allow for data analysts to put certain information from customers in the primary focus. With proper output, financial institutions can discover data beyond the normal findings. This also allows for a quicker turnaround to requests from applicants and pinpoints areas where banks need better compliance or customer service. Rather than finding datasets in an endless loop, there’s the ease of use in a deep learning model that provides real-time data analysis simulators.
#3 Risk Management
Neural network models can be expected to self-train efficiently to further enhance internal data patterns. These applications offer an analytical alternative to standard techniques, examining a variety of relationships to make it part of an eventual normalization of deep learning. In the insurance industry, neural networks allow for data governance and security that can expedite claims based on the information provided at the input into a system. Think of a claim application from the moment of deployment, and how fast it can be pushed through hidden layers.
Neural network analysis takes the findings of the claims processes from the input to the output to understand where there may be underlying issues that need to be addressed. For example, if more claims are being seen in a particular region of the United States for home coverage, it could be because of weather-related phenomena on the rise. This expert system will recognize this pattern, and push for insured parties to pursue additional coverage to better protect them in the event of, say a flood. It’s all about quick thinking.