Introduction The advent of computer intelligence has brought dramatic changes to the art of doing business. Advanced Artificial Neuro-Computing and Knowledge management have become critical components of business intelligence. Neural Networks imitates the working of human neuron and may use artificial intelligence to function as required. They consist of different single processors Jordan Staal Jersey Sale , which network through a dense web of interconnections. Finding effective tools for complex managerial problems is one of the most important subjects in organizational management. Artificial Neural Networks is a powerful decision making tool. They can provide highly accurate results compared with statistical models Artificial Neural Networks have eased management decision making as they are a critical component of business intelligence. In business intelligence, they have been used to predict future events based on patterns, classifying unseen data into predefined groups and clustering training data into natural groups based on similarity. This paper discusses artificial Neural Networks; their application in business different processes, and their strengths and limitations.
Application of artificial Neural Network in business processes Analysis methodologies and Mathematical models for Business Intelligence include different inductive learning models for data mining such as artificial neural networks, decision trees Justin Faulk Jersey Sale , fuzzy logic, support vector machines, genetic algorithms and intelligent agents. Artificial Neural Networks or ANN has is preferred by numerous organizations for its multitude of real world applications in the business domain. There various families of Artificial Neural Networks in application and research. The diversity and lack of standard reporting scheme makes it difficult to evaluate Artificial Neural Networks. However, it is possible to present their applications in business and management science by classifying business disciplines into areas such as strategic management, human resource Authentic Sebastian Aho Jersey , finance, marketing, accounting and manufacturing.
Artificial Neural Networks in marketing
Artificial Neural Networks are applied in various marketing problems which could were previously tackled by multivariate statistical analysis. Typical problems include Classification of consumer spending patterns, market segmentation tasks, new product analysis Authentic Dougie Hamilton Jersey , Sale forecasts, 飥營dentification of customer characteristics, modeling relationships between market orientation and performance and targeted marketing.
Artificial Neural Networks in Finance
Artificial Neural Networks are frequently used in different modeling and forecasting problems. They are also increasingly applied in financial analysis. Artificial Neural Networks are also used in Signature and bank note verification, Foreign exchange rate forecasting, Mortgage underwriting Authentic Jordan Staal Jersey , Predicting stock initial public offerings and Country risk rating. Other applications include bankruptcy prediction, credit card approval, and Customer credit scoring and fraud detection. Other common uses include Bond rating and trading, Stock and commodity selection and trading, 飥燜orecasting economic turning points Authentic Justin Faulk Jersey , Economic and financial forecasting, Risk management and loan approvals.
Financial Markets are different from other sectors. It is possible to model financial markets as complex feedback mechanism working on past trends and external stimulus. In stock markets, Prices are unstable and can fall and rise by any magnitude. Since stock Market involves great amount of uncertainty, swap risk and trade risk, accurate prediction is critical. Artificial Neural networks are used by investment banks Cheap Sebastian Aho Jersey , avant-garde portfolio managers, and trading companies. Morgan Stanley and Goldman Sachs are some of the major investment banks that have significantly invested in neural networks. Fidelity Investments has established a mutual fund whose portfolio allocation is based exclusively on approval produced by an artificial neural network. The fact that major firms in the financial industry have invested resources in artificial neural networks means that they serve as an important method of forecasting.
Artificial Neural Networks in Accounting Artificial Neural Networks are mainly used in identifying tax fraud and enhancing auditing by detecting irregularities. Today, fraud is increasing dramatically with increased adoption of technology resulting to loss of money to organizations. While prevention technologies have been adopted as more effective ways of reducing fraud, fraudsters circumvent these measures. Machine learning and statistics have been used to detect fraudulent activities including credit card fraud. Additionally, Artificial Neural Networks development affected the nature of the audit process and audit skills. Auditing is a main application area of artificial neural networks. They are used in establishing management fraud Cheap Dougie Hamilton Jersey , material errors, and support for backing significant decision. They have also found huge applications in financial distress problems, control risk assessment and audit fee. Many things in the auditing and business environment have changed at an increasing rate. The need for better and faster information for decisions and increased competition characterizes today鈥檚 business environment. The complexity of systems means that auditors have more and different kinds of jobs to undertake. Auditors can use Artificial Neural Networks in areas such as in auditing for material errors.
Artificial Neural Networks in manufacturing and production Quality control, forecasting and optimization dominate production and manufacturing problems. The appropriateness of Artificial Neural Networks uses corresponds to these problems. Artificial Neural Networks have been applied in quality control; engineering design, inventory control Cheap Jordan Staal Jersey , and storage design, demand
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