Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy

Gateway to College: Lessons from Implementing a Rigorous Academic Program for At-Risk Young People

Despite efforts to improve the high school graduation rate in the United States, an estimated 7,200 students drop out of high school every day -- a staggering 1.3 million every year. Further, a recent report by the Center on Education and the Workforce at Georgetown University projects that by 2020, nearly 65 percent of U.S. jobs will require at least some college education, out of reach for those who are unable to earn a high school diploma. Much more comprehensive alternative education programs are needed that put dropouts and students at risk of dropping out on a path to earn high school diplomas while also providing them with the academic skills and support necessary to be successful in their postsecondary pursuits

A Review of Work Based Learning in Higher Education

The idea of work based learning in higher education might sound like a contradiction in terms. Work based learning is surely in the the workplace. The senses in which it might also, under certain conditions, be in higher education are explored in this review. There are increasing arrangements whereby people can obtain academic recognition for learning which has taken place outside of educational institutions. In addition to traditional forms of professional education and sandwich courses, one can add a host of relationships between employers and higher education institutions which involve quite fundamental questioning of the roles and responsibilities of each in the continuing education and training of adults. Such developments can be related to broader themes concerning the organisation of knowledge in society, the changing nature of work and career, the learning society and the implications they hold for individual workers, their employers and educational providers. The Department for Education and Employment sponsored the study to produce a substantial literature review of progress and issues raised in the field of work based learning in higher education. The first part of the book provides a contextual and conceptual backdrop against which more practical aspects of work based learning are then considered in part two. The final part considers strategic issues of implementation for higher education institutions, employers and individuals, before turning to more wide ranging issues of policy

Statistical analysis of identity risk of exposure and cost using the ecosystem of identity attributes

Personally Identifiable Information (PII) is often called the "currency of the Internet" as identity assets are collected, shared, sold, and used for almost every transaction on the Internet. PII is used for all types of applications from access control to credit score calculations to targeted advertising. Every market sector relies on PII to know and authenticate their customers and their employees. With so many businesses and government agencies relying on PII to make important decisions and so many people being asked to share personal data, it is critical to better understand the fundamentals of identity to protect it and responsibly use it. Previously developed comprehensive Identity Ecosystem utilizes graphs to model PII assets and their relationships and is powered by empirical data from almost 6,000 real-world identity theft and fraud news reports to populate the UT CID Identity Ecosystem. We analyze UT CID Identity Ecosystem using graph theory and report numerous novel statistics using identity asset content, structure, value, accessibility, and impact. Our work sheds light on how identity is used and paves the way for improving identity protection.Electrical and Computer Engineerin

Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

Graduate Catalog, 2002-2003

https://scholar.valpo.edu/gradcatalogs/1029/thumbnail.jp