Chapter 6: Neural Networks: Classifying Patterns

6.1 What Do Neural Networks Do?

Neural networks are software systems modeled after the human process of learning and remembering. They mimic the cognitive neurological functions of the human brain. As such they are capable of predicting new observations from historical samples after executing a process of learning. A neural network can be used to detect a fraudulent transaction, a computer intrusion, and an assortment of other criminal activities, so long as examples of observations are available for training it.

Neural network software comprises programmable memories designed to make predictions. Neural networks were introduced to the marketplace in the mid-1980s and became practical commercial software products only after advances in computing power at the desktop and server level became a reality. They have been used in private industry to do one or more of the following:

• Classification: discriminating between two things based on similarities, such as separating loan applications into good or bad risks or distinguishing a legal from a fraudulent transaction. They can also be used to discriminate between criminal and legal activities. Chapter 8 illustrates how neural networks are used to detect Internet fraud on an e-commerce site.

• Clustering: organizing observations into groups with similar features or attributes—for instance identifying groups of customers who buy the same type of products, also referred to as affinity market-basket analysis; they can also be used to group criminals who perpetrate the same types of crimes. This involves using a special type of Kohonen neural network (named after its creator, Dr. Tevo Kohonen). They are also known as self-organizing maps (SOM). Several case studies demonstrating this type of analysis are provided further in this chapter, and book.

• Generalizing: generalizing from examples about new cases or problems just as humans can learn to model relationships from examples. This same process can be used to model criminal signatures. A case study is provided in this chapter illustrating how neural networks are used to recognize the signature of kerosene in arson investigations.

• Forecasting: looking at current information and predicting what is likely to happen. Prediction is a form of classification into the future. Neural networks can also be trained on observations in order to predict (with some probability), for example, who is likely to be a smuggler. A demonstration will be provided in this chapter illustrating this process.