Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Money laundering and virtual money: the bitcoin example

Money laundering operations faced multiple changes and become more complex, in line with financial innovation. Often, regulation does not follow that innovation, giving opportunity to take advantage of thes e gaps in less lawful activities. The bitcoin is a virtual currency that has grown significantly, both in value and in volume of transactions, in recent years. The dimension of the phenomenon led to an increasing surveillance from the financial supervisors . We search for evidence of the relationship between money laundering and transactions carried out in bitcoins, the most popular virtual currency at the moment. We analyse the official publications relating the two themes, academic research and the notori ety within the international media. The attitudes toward the bitcoin are diverse, but all share concerns about its future impact. Some sparse evidence indicates that bitcoin may be an opportunity for money laundering, however more data is required.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

IDEA MANAGEMENT SYSTEM - AN ESSENTIAL COMPONENT IN THE CONTINUOUS IMPROVEMENT OF QUALITY AND INNOVATION

It is known the power of ideas is tremendous. But there are employees in many companies who have good ideas but not put them into practice. On the other hand, there are many others who have good ideas and are encouraged to contribute their ideas for innovation in the company. This study attempts to identify factors that contribute to success in managing ideas and consequent business innovation. The method used was the case study applied to two companies. During the investigation, factors considered essential for the success of an idea management program were identified, of which we highlight, among others, evidences the results, involvement of the top management, establishment of goals and objectives; recognition; dissemination of good results. Companies with these implemented systems, capture the best ideas from their collaborators and apply them internally. This study intends to contribute to business innovation in enterprises through creation and idea management, mainly through collecting the best ideas of their own employees. The results of this study can be used to help improving deployed suggestions systems, as well as, all managers who wish to implement suggestions systems/ideas management systems.