Neural Network Applications in Business

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.

Below we have listed some of the common applications of neural networks in business. If you are currently using neural networks in your business application, we would love to hear about it.

NeuroDimension has also used its leading edge neural network technology to develop numerous business applications with a variety of companies. If you need neural network consulting for your business application, please contact NeuroDimension.

Sample Applications

Detect common characteristics in large amounts of data (key customers, fraud detection).
Determine relationship between business factors to forecast effects of changes (prices, costs).
Forecast trends based on previous data (revenue, phone calls).
Process documents or images electronically (OCR, biometrics).
Group business data based on key characteristics (demographics).

Detect common characteristics in large amounts of data

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.

Sample Study: Searching for “interesting” Business Applicationss

Using NeuroSolutions, this study searched through a database of WWW businesses and classified them as “interesting” and “not interesting” for determining future business ventures. With generalized models, the new data sets were classified at 84.75% on average correctly. Locate this paper on Google Scholar!

Sample Study: Can computers predict which movies will flop?

Using NeuroSolutions, this study, which made headlines at MSNBC.com, is designed to predict the expected revenue range of a movie before its theatrical release. The results demonstrated that the neural networks can predict the success category of a motion picture better than other statistical methods currently employeed. Predicting box-office success of motion pictures with neural networks - Ramesh Sharda, Dursun Delen View this paper at ND.com!

Our NeuroSolutions product is an excellent resource for classification applications. For an interactive example of classification in NeuroSolutions for Excel, download the free evaluation version and view the demo called “Testing Classifiers” in the Help menu.

Determine relationship between business factors to forecast effects of changes

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.

Sample Study: Bankruptcy Prediction for Credit Risk

This sample study highlights important and widely studied topic since it can have a significant impact on bank lending decisions and profitability. Inspired by one of the traditional credit risk models, the neural network approach provides a significant improvement in the out-of-sample prediction accuracy (from 81.46% to 85.5% for a three-year-ahead- forecast). Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results - Amir F. Atiya, Senior Member, IEEE Locate this paper on Google Scholar!

Sample Study: Forecasting Gaming Referenda

Using NeuroSolutions, this study developed and test models to predict community support for commerical gaming. The study specifically examined the role of factors that contribute to legalization and/or probation of gambling activities using neural networks. On average, the models accurately predicted 4 out of every 5 counties (approximately 82% accuracy) on the out of sample data set. FORECASTING GAMING REFERENDA - Ercan Sirakaya, Dursun Delen & Hwan-Suk Choi View this paper at ND.com!

Our NeuroSolutions product is an excellent resource for function approximation applications. For an interactive example of function approximation in NeuroSolutions, download the free evaluation version of the software and view the demo called “Multi-Layer Perceptron, Basic” in the Help menu.

Forecast trends based on previous data

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.

Sample Study: Predicting Expense Cost

Using NeuroSolutions, this study is used to predict the total contingency cost allowance for variations on a construction project is described. By determining cost factors for engineering and business decisions you could provide better estimations towards the manufacturing process. A Neural Network Model for Predicting Building Projects' Contingency Allowance - Akinsola, A.O. View this paper at ND.com!

Our NeuroSolutions product is an excellent resource for time-series prediction applications. For an interactive example of time-series prediction in NeuroSolutions, download the free evaluation version of the software and view the demo called “Time Lagged Recurrent Network” in the Help menu.

Process documents or images electronically

Identifying characters in images or video feeds in business is a type of image processing problem. Neural networks can be used to solve 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.

Our NeuroSolutions product is an excellent resource for image processing applications. For an interactive example of image processing in NeuroSolutions, download the free evaluation version of the software and view the demo called “Linear Associator” in the Help menu.

Group business data based on key characteristics

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.

Sample Study: Credit Risk Evaluation

This sample study highlights the usage of neural networks in extracting credit risk information. 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. Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation - Bart Baesens, Rudy Setiono, Christophe Mues and Jan Vanthienen Locate this paper on Google Scholar!

Our NeuroSolutions product is an excellent resource for clustering applications. For an interactive example of clustering in NeuroSolutions, download the free evaluation version of the software and view the demo called “Unsupervised Learning” in the Help menu.