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Neural Network. Biological Systems are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours.
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Biological Systems are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours. • An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems. • The nervous system is build by relatively simple units, the neurons, so copying their behaviour and functionality should be the solution.
Artificial neural networks • An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture. The objective of the neural network is to transform the inputs into meaningful outputs.
Biological approach to AI developed in 1943 • Comprised of one or more layers of neurons • Receives n-inputs • Multiplies each input by its weight; process the input and gives the output
Each layer receives its inputs from the previous layer and forwards its outputs to the next layer
Input Units Hidden Units Output Units Influence Map Layer 1 Influence Map Layer 2 • Could lead to a very large number of calculations
Marketing • The goal of modern marketing exercises is to identify customers who are likely to respond positively to a product, and to target any advertising towards these customers. • Target marketinginvolves market segmentation, whereby the market is divided into distinct groups of customers with very different consumer behaviour. Market segmentation can be achieved using neural networks by segmenting customers according to basic characteristics including demographics, socio-economic status, geographic location, purchase patterns, and attitude towards a product. • Neural networks can be used to automatically group the customers into segments based on the similarity of their characteristics. • Neural networks can be trained to learn the boundaries between customer segments based on a group of customers with known segment labels, i.e. frequent buyer, occasional buyer, rare buyer.
Once market segmentation has been performed, direct marketingcan be used to sell a product to customers. • Customers who are contacted are already likely to respond to the product since they exhibit similar consumer behaviour as others who have responded in the past. In this way, marketers can save both time and money by avoiding contacting customers who are unlikely to respond. • Neural networks can be used to improve response rates simply by choosing which customers to send direct marketing mail advertisements. • Neural networks can also be used to monitor customer behaviour patterns over time, and to learn to detect when a customer is about to switch to a competitor. The electronic storage of daily transaction details enables us to anticipate consumer behaviours. • Analysis of market research is also an area where neural networks can be useful.
Retail • Businesses often need to forecast sales to make decisions about inventory, staffing and pricing. • Neural networks have had great success at sales forecasting, due to their ability to simultaneously consider multiple variables such as market demand for a product, consumer’s disposable income, the size of the population, the price of the product, and the price of complementary products. • Forecasting of sales in supermarkets and wholesale suppliers can be studied and the results can be shown to perform well when compared to traditional statistical techniques like regression, and human experts. • The second major area where retail businesses can be benefited from neural networks is in the area of market basket analysis.
Daily transaction details of customers is information relating to which products are often purchased together, or the expected time delay between sales of two products. Retailers can use this information to make decisions, for example, about the layout of the store: if market basket analysis reveals a strong association between products A and B then they can entice consumers to buy product B by placing it near product A on the shelves. • If there is a relationship between two products over time, say within 6 months of buying a printer the customer returns to buy a new cartridge, then retailers can use this information to contact the customer, decreasing the chance that the customer will purchase the product from a competitor. • Understanding competitive market structures between different brands has also been attempted with neural network techniques.
Banking and Finance • One of the main areas of banking and finance that has been affected by neural networks is trading and financial forecasting. • Neural networks have been applied successfully to problems like derivative securities pricing, futures price forecasting, exchange rate forecasting and stock performance and selection prediction. • There are many other areas of banking and finance that have been improved through the use of neural networks. • For many years, banks have used credit scoring techniques to determine which loan applicants they should lend money to. • Traditionally, statistical techniques have driven the software. These days, however, neural networks are the underlying technique driving the decision making.
Hecht-Nielson Co. have developed a credit scoring systems which increased profitability by 27% by learning to correctly identify good credit risks and poor credit risks. • Neural networks have also been successful in learning to predict corporatebankruptcy. • Financial fraud detection is another important area of neural networks in business. • Visa International have an operational fraud detection systems which is based upon a neural network, and operates in 5 Canadian and 10 US banks. The neural network has been trained to detect fraudulent activity by comparing legitimate card use with known cases of fraud. The system saved Visa International an estimated US$40 million within its first six months of operation alone. • Neural networks have also been used in the validation of bank signatures, identifying forgeries significantly better than human experts.
Insurance • There are many areas of the insurance industry which can benefited from neural networks. • Policy holders can be segmented into groups based upon their behaviours, which can help to determine effective premium pricing. • The insurance industry, like the banking and finance sectors, is constantly aware of the need to detect fraud, and neural networks can be trained to learn to detect fraudulent claims or unusual circumstances. • The final area where neural networks can be of benefit is in customer retention. • Insurance is a competitive industry, and when a policy holder leaves, useful information can be determined from their history which might indicate why they have left. • Offering certain customers incentives to stay, like reducing their premiums, or providing no-claims bonuses, can help to retain good customers. • Risk Data Corporation used neural networks to detect fraudulent insurance claims for the Workers' Compensation Fund of Utah, as well as estimating the financial impact of predicted claims.
Telecommunications • Like other competitive retail industries, the telecommunications industry is concerned with the concepts of churn(when a customer joins a competitor) and winback (when an ex-customer returns). • Neural Technologies Inc., is a UK-based company which has marketed a product called DA Churn Manager. Specifically tailored to the telecommunications industry, this product uses a series of neural networks to: analyse customer and call data; predict if, when and why a customer is likely to churn; predict the effects of forthcoming promotional strategies; and interrogate the data to find the most profitable customers. • Telecommunications companies are also concerned with product sales, since the more reliant a customer becomes on certain products, the less likely they are to churn. • Market basket analysis is significant here, since if a customer has bought one product from a common market basket (such as call waiting), then enticement to purchase the others (such as caller identification) can help to reduce the likelihood that they will churn, and increases profitability through sales. • There are also many other applications of neural networks in the telecommunications industry, and while these are more engineering applications than business applications, they are of interest to the operations researcher because they involve optimisation. • These include the use of neural networks to assign channels to telephone calls, for optimal network design and for the efficient routing and control of traffic.
Operations management • There are many areas of operations management, particularly scheduling and planning, where neural networks have been used successfully. • The scheduling of machinery, assembly lines, and cellular manufacturing using neural networks have been popular research topics over the last decade. • Other scheduling problems like timetabling, project scheduling and multiprocessor task scheduling have also been successfully attempted. All of these approaches are based upon the neural network. • The neural networks can be used in shop floor scheduling and control.
Neural networks can be used to integrate marketing and manufacturing functions in an organization. • Neural networks have also been used in conjunction with simulation modelling to learn better manufacturing system design. • The other area of operations management which benefits from neural networks is quality control. • Neural networks can be integrated with traditional statistical control techniques to enhance their performance. Examples of their success include a neural network used to monitor soda bottles to make sure each bottle is filled and capped properly. • Neural networks can also be used as a diagnostic tool, and have been used to detect faults in electrical equipment and satellite communication networks. • Project management tasks have also been tackled using neural networks. • It can be used to forecast project completion times for knowledge work projects and for estimating several software metrics in software development projects.
Other industries • IBM's computer virus recognition software IBM AntiVirus uses a neural network to detect boot sector viruses. In addition to the viruses it was trained to detect, the software has also caught approximately 75% of new boot viruses since the product was released. • Sensory Inc. have used neural networks to create a speech recognition chip, which is currently being used in Fisher-Price electronic learning aids, and car security systems. • Companies like Siemens use neural networks to provide automation for manufacturing processes, saving operating costs and improving productivity. • Handwritten character recognition software like that used in Apple Computer's Newton MessagePad uses neural network technology as well.
Conclusion • What emerges from this discussion is the complete diversity of the application areas which are reaping the advantages and benefits of neural networks. • The important point about these applications is that they have effectively driven research over the last decade. • Banks cannot reject a loan applicant because their neural network advised them that the applicant would be a bad risk. They must provide reasons why the application was not successful, and give suggestions as to how the applicant could improve their chances next time.
