Bankruptcy prediction neural networks
Growing since the day that Alan Turing introduced the world to the idea of the artificial neural network in his 1948 paper "Intelligent Machinery", neural networks have evolved as a useful and dynamic tool which offer an efficient and effective business solutions to common problems for modern businesses; problems such as fitness approximation, data processing, and robotics .They are particularly effective in situations where complexity of the data or task makes providing a solution impractical by hand.
Real life applications:
- In 2005 Sirakaya, Delen and Choi published a paper which investigated community support for commercial gaming in the United States. They utilised an artificial Neural Network to conduct the research and used variables such as geographic space, proximity to population centres and church membership to make their assessment.
- Amir F. Atiya in 2001 publised his article entitled “Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results”. This paper was written for the corporation he was working for namely IEEE. Using the neural network which he developed he was able to provide a corporate bankruptcy prediction model with an accuracy from 81.46% to 85.5%.
Neural Networks provide an alternative to conventional techniques which are often limited by strict rules of normality, linearity, variable independence etc. Because a neural network can capture many kinds of relationships it allows the user to easily show or explain phenomena which otherwise may have been very difficult or impossible to explain.
Source: CIIS SStenson
Neural networks and bankruptcy prediction: Funds flows, accrual ratios, and accounting (Turun kauppakorkeakoulun julkaisuja)
Book (Turku School of Economics and Business Administration)
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