Kate Smith – Neural Networks in Business

Review

Neural networks in business are covered in this book. Mark Embrechts is a faculty member at the Rensselaer Polytechnic Institute.

From the inside.

The most important asset of an organization is business data. Whether the data records sales figures for the last 5 years, the loyalty of customers, or information about the impact of previous business strategies, the potential for improving the business intelligence of the organization is clear. Businesses are realizing the value of huge volumes of data in data warehouses. The process of converting data into business intelligence is a mystery to the business community.

Neural networks are able to model the relationships that exist in data collections, and can be used for increasing business intelligence across a variety of business applications. Neural networks have been successfully used for engineering applications for decades, but the mathematical nature of neural networks has limited their adoption by the business community.

The aim of the book is to demystify neural network technology by taking a how-to approach through a series of case studies from different functional areas of business. Chapter 1 introduces the field of neural networks and describes how they can be used in a wide variety of business areas. There are two main types of neural networks presented in this introductory chapter. The first is the feed forward neural network which is used for prediction problems such as stock market prediction and classification problems such as classifying bank loan applicants as good or bad credit risks. The self-organizing map is a type of neural network that is used for clustering data according to similarities. Retail sales, marketing, risk assessment, accounting, financial trading, business management, and manufacturing are some of the areas in which the two main neural network architectures have been successfully used.

The rest of the book contains a series of case study chapters and is divided into sections based on the most common functional business areas: sales and marketing, risk assessment, and finance. Neural network models are sometimes used within the chapters to provide increased business intelligence.