Application of data mining to medical risk management

2008 ◽  
Author(s):  
Shusaku Tsumoto ◽  
Kimiko Matsuoka ◽  
Shigeki Yokoyama
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Medical Risk

Introduction to Banking Technology and Management

2008 ◽  
pp. 1-15
Author(s):  
Vadlamani Ravi
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Computer Science ◽  
Information And Communication Technologies ◽  
Science Communication ◽  
Banking Industry ◽  
Relationship Management ◽  
Communication Technologies ◽  
Customer Relationship ◽  
Information And Communication

This chapter introduces banking technology as a confluence of several disparate disciplines such as Finance (including risk management), Information technology, Computer Science, Communication technology and marketing science. It presents the evolution of banking, the tremendous influence of information and communication technologies on banking and its products, the quintessential role played by computer science in fulfilling banks’ marketing objective of servicing customers better at a less cost and thereby reap more profits. It also highlights the use of advanced statistics and computer science to measure, mitigate and manage various risks associated with banks’ business with its customers and other banks. The growing influence of customer relationship management and data mining in tackling various marketing related problems and fraud detection problems in banking industry is well documented. The chapter concludes by saying that the banking technology discipline is all set for rapid growth in future.

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

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