Neural Networks and Business

Firstly Neural networks (NNs, ANNs) are biologically inspired and operate like a human brain. They are capable of learning, self-organisation and have a tolerance to error and noisy data. They are a set of connected artificial neurones (nodes) along with the weights for connections and they operate concurrently and collectively. Neural networks provide significant benefits in business applications. They are actively being used for such applications as bankruptcy prediction, predicting costs, forecast revenue, processing documents and more There are many reasons why they should be used in a business. Below I have listed some of the common applications of neural networks in business.

(1)Detecting common characteristics in large amounts of business data is a type of classification problem. Neural networks can be used to solve classification problems, typically through Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) type networks. Examples of classification applications in business include dividing research populations or data into groups for further study. For example, data can be extracted from databases to determine potential business ventures for investors.It can also be designed to assist the sales part of a business to predict the expected revenue range of a movie or new CD before its release. Results have demonstrated that the neural networks can predict the success or sales better than other statistical methods currently employeed.

(2)Forecasting the relationship between multiple factors in business data is a type of function approximation problem. Neural networks can be used to solve function approximation problems, typically through Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and CANFIS (Co-Active Neuro-Fuzzy Inference System) type networks. Examples of function approximation in business include predicting changes to prices and costs. For example, data from studies can potentially help predict bankruptcy predictions for credit risk or sales forecast.

(3)Forecasting the relationship between multiple factors in business data is a type of time-series prediction problem. Neural networks can be used to solve time-series problems, typically through Time-Lagged Recurrent (TLRNN) type network. Examples of time-series predictions in business include forecasting revenue and expense cost. For example, data from business studies can predict labor, cost, material, utilities, or other cost over time. By determining cost factors for engineering and business decisions you could provide better estimations towards the manufacturing process.

(4)Identifying characters in images or video feeds in business is a type of image processing problem. Neural networks can be used to solve these image processing problems, typically through Principal Component Analysis (PCA) type network. Examples of image processing in business include identifying OCR (Optical Character Recognition) and biometrics in images. For example, image data from business studies can scan business cards information to be directly inputted into contact managers such as Outlook and PDA devices.

(5)Grouping of business data based on key characteristics is a type of clustering problem. Neural networks can be used to solve clustering problems, typically through Self-Organizing Map (SOM) type network.Examples of clustering in business include the detection of key characteristics in demographics and feature extraction. For example, data from studies concerning credit risk can be evaluated extracting different rules for determining credit risk. Neural network decisions can clarify by explanatory rules that capture the learned knowledge embedded in the networks can help the credit-risk manager in explaining why a particular applicant is classified as either bad or good.