Introduction Process automation is changing the way people and corporations do business today. Conscious of their potential benefits (e.g. increasing accuracy, increasing efficiency, lower costs, resemblance of human capabilities), financial institutions have shown increasing interest towards the adoption of novel techniques that can support business management and decision making processes. In the past years, Credit Scoring and Bankruptcy prediction have been gaining momentum, particularly as we live in a period of economic downturn. Indeed, nowadays, reliance on risk prediction and assessment models that can support decision making processes has become a strategic lever to achieve competitive advantage and performance improvement. As the trend towards process automation and the bet for predictive models have been gaining pace, researchers and practitioners have strived to develop and improve automated tools which facilitate the evaluation of customers’ credit worthiness, prediction of economic behavior and propensity of default. In general, significant amount of research efforts within the financial environment are towards reducing manual processes and providing assistance to banking agents on their daily decisions, which are mainly related to the approval/ rejection of credit loans, the extension/ constriction of lines of credit, and the definition of client interest rates. Nevertheless, automation levels remain low and there is still a lack of incorporation of intelligence in financial technologies, which limits the inclusion of human centered functionalities that could result in significant improvements regarding credit scoring and predictive modeling. Credit scoring and bankruptcy prediction have long been recognized as key financial risk management tools for both regulators and stakeholders, especially in today’s globalized and competitive business environment. The main impacts of research in these areas are in decision-support and profitability-analysis of the financial institutions. Though statistical models are still the dominating techniques implemented by the financial and banking industries, credit scoring and bankruptcy prediction systems employing neural computing are gaining increasing attentions due to its potential for effectiveness and accuracy of the results.
The increasing numbers of commercial bank failures have evolved into an economic crisis that has received much attention in recent years. The economic aftermath of large-scale bank failures is devastating. It triggers a domino effect that ripples across different sectors of the economy. It is therefore both desirable and warranted to explore new techniques and to provide early warnings to regulatory agencies. In this context, neural based systems have successfully showed the potential to outperform many other techniques, and its application to credit scoring and bankruptcy prediction is now facing a new challenge. With the purpose of contributing to overcome this challenge, this issue has focused in the selection of neural based approaches and techniques that can enhance bankruptcy prediction and credit scoring models. This issue presents research results that apply novel developments in neural systems to credit scoring and bankruptcy prediction from a broad range of disciplines. In response to a general call-for-papers 22 papers were submitted for possible publication in the issue. We followed the rigorous review process of the International Journal of Neural System. seach submission was first screened on its suitability to the journal, and then assigned to a guest editor of the focus issue.
To avoid the possibility of bias, each manuscript is
handled by a guest editor from a different country of origin than the authors’, or directly administered by the Editor-In-Chief, Prof. H. Adeli. Each manuscript was reviewed by three to six referees. After taking into consideration of the recommendations from all referees, the Editor-In-Chief made the final decision on each manuscript. A manuscript was accepted only if it receives unanimous accept decision from all referees. Each manuscript received at least two rounds of reviews. Only four papers passed through the journal’s review process resulting in an acceptance rate of 18%. We would like to thank Prof. Adeli H., the Editor-in-Chief of International Journal of Neural Systems, for giving us the opportunity to guest-edit this focus issue. We would also like to thank all referees for their time and efforts to provide conducive comments to the author,, and the authors for submitting their original work to this focus issue.
Hui Li, School of Business Administration, Zhejiang Normal University, Jinhua 321004, Zhejiang Province,
PR China, Email:
[email protected];
[email protected] Melody Y. Kiang, College of Business Administration, California State University, Long Beach, California 90840, U.S.A., Email:
[email protected] Diego Andina, Head of Group for Automation in Signals and Communications GASC/UPM, Universidad Politécnica de Madrid (UPM), Madrid 28040, Spain, Email:
[email protected]
