Neural Networks

Neural networks are computing systems similar to a computer, but are designed to recognize patterns and work like a human brain (artificial intelligence) would work. Instead of utilizing numbers such as zero and one to form mathematical calculations, a neural network functions as an approximation tool. Originally developed in the 1950’s, neural networks have become a major force in the business district; literally transforming the way business problems are solved.

In the past, businesses would utilize an operation researcher to run a statistical analysis on a problem. This was a time-consuming process, but now operation researchers are able to use neural networks in order to give quicker, more accurate information to the company in order to achieve their goals. Some of the tasks a neural network is well-suited include: detecting common characteristics within the data in order to find customers and fraud detection, financial forecasting- bankruptcy and economic patterns as well as help with costs and prices due to changes in a business, medical data interpretation, handwriting recognition, biometrics, grouping business data such as demographics, and recognition and identification to name a few.

In the business world, one of the main problems is trying to figure out what will happen in the future and applying business operations to these trends. Therefore, businesses aim to discover and predict different outcomes. The issue lies within the complexity of the process with there being so many variables. Neural networks help with these processes by taking different factors and placing them in the network, and the information is then analyzed and learns from the data to predict the future through patterns.

To explain further how neural marketing works, let’s take a further look into marketing strategies. Most business owners and entrepreneurs are familiar with marketing as they use techniques every day in order to drum up business and discover customers. Marketing identifies potential customers who are likely to buy or respond to a product positively by advertising to their niche or group characteristics. Often a business attracts many different types of customers. This process is known as market segmentation; where customers are split into different groups based on their characteristics. The neural network can simplify this process by breaking down the groups by demographics, purchase patterns, and socio-economic status, preparing a business owner for what they are truly good at: marketing and growing their business.

The beauty of this process is that neural networks learn and can begin to tell future trends of customer characteristics. An individual can then start to employ direct marketing techniques, reaching the already interested target potential based on their characteristics, increasing sales and reviews. This is only one of many examples where neural networks are changing the way we live. Securities and investment companies utilize neural networks to determine future financial forecasting with much success. Many insurance agencies have already begun implementing the networks in order to segment groups of people based on their behavior in order to determine pricing and risk.

The limit to neural networks is endless. They don’t just help business owners, but offer help to individual people too. They are revolutionizing the mental health field by helping doctors discover probabilities of mental disorders and better ways to treat them. Home owners are retrieving home improvements and future prices from appraisers using these systems of artificial intelligence. Credit scores, securities and bonds, gas prices, environmental factors such as greenhouse gases and global warming patterns, stocks, purchasing price, and trends are all being used more frequently due to their capability of being fast and efficient; better than a statistical analysis because human assumptions are completely removed and only relevant data is analyzed. The neural network is rapidly becoming the new way of processing data for businesses and the sciences in order to illustrate patterns: both current and future.