A Novel Theory in Risk-Management by Numerical Pattern Analysis in Data-Mining

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

Data Mining Applications for Empowering Knowledge Societies ◽

10.4018/978-1-59904-657-0.ch012 ◽

2009 ◽

pp. 220-238 ◽

Cited By ~ 8

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.

Special issue on data mining for decision making and risk management

Journal of Intelligent Information Systems ◽

10.1007/s10844-011-0155-6 ◽

2011 ◽

Vol 36 (3) ◽

pp. 249-251 ◽

Cited By ~ 5

Author(s):

Shusaku Tsumoto ◽

Tzung-Pei Hong

Keyword(s):

Data Mining ◽

Decision Making ◽

Risk Management ◽

Special Issue

Risk Mining in Medicine: Application of Data Mining to Medical Risk Management

Web Intelligence Meets Brain Informatics - Lecture Notes in Computer Science ◽

10.1007/978-3-540-77028-2_28 ◽

2007 ◽

pp. 471-493 ◽

Cited By ~ 20

Author(s):

Shusaku Tsumoto ◽

Yuko Tsumoto ◽

Kimiko Matsuoka ◽

Shigeki Yokoyama

Keyword(s):

Data Mining ◽

Risk Management ◽

Medical Risk ◽

Medicine Application

Bayesian analysis, pattern analysis, and data mining in health care

Current Opinion in Critical Care ◽

10.1097/01.ccx.0000141546.74590.d6 ◽

2004 ◽

Vol 10 (5) ◽

pp. 399-403 ◽

Cited By ~ 63

Author(s):

Peter Lucas

Keyword(s):

Data Mining ◽

Health Care ◽

Bayesian Analysis ◽

Pattern Analysis ◽

Analysis Pattern

Research on Financial Risk Management of Automobile Supply Chain Based on Data Mining Technology

Proceedings of the 3rd Annual 2017 International Conference on Management Science and Engineering (MSE 2017) ◽

10.2991/mse-17.2017.24 ◽

2017 ◽

Author(s):

Zi-Gui Chen ◽

Shi-Ping Guan

Keyword(s):

Data Mining ◽

Risk Management ◽

Supply Chain ◽

Financial Risk ◽

Mining Technology ◽

Financial Risk Management

Measuring the effectiveness of PBL through shape parameters and classification

Journal of Applied Research in Higher Education ◽

10.1108/jarhe-08-2018-0175 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Shilpa Bhaskar Mujumdar ◽

Haridas Acharya ◽

Shailaja Shirwaikar

Keyword(s):

Data Mining ◽

Operations Research ◽

Pattern Analysis ◽

Critical Parameters ◽

Classroom Teaching ◽

Shape Parameters ◽

Pedagogical Approach ◽

Content Type ◽

Teaching Statistics ◽

The Impact

PurposeThis paper utilizes data mining to study the effect of Problem Based Learning (PBL), an innovative pedagogical approach that has been implemented in undergraduate education at a private university in India for teaching Statistics and Operations Research (OR) to techno-management students.Design/methodology/approachThe study follows the assumptions of an in-situ experiment. It employs BBA (IT) and BCA student(s) as a subject and their end of semester GPA as a performance indicator. The pedagogical approach to this study is integrating PBL with classroom teaching. The paper uses a combination of statistics and data mining to analyze the impact of PBL and establish research conclusions.FindingsThe study concludes that the introduction of PBL positively results in an improved GPA for students with a math background. PBL is more effective for BBA (IT) male students. Female students seem to be performing equally well irrespective of the inclusion of PBL. Pattern analysis of shape parameters evidences the impact of PBL, and the results are established through the decision tree and test of proportions.Research limitations/implicationsThe study is limited to students from a single institute.Practical implicationsThis Pattern analysis, as applied in this paper, can be scaled to evaluate the impact of any innovative pedagogical approach agnostic of the field of study. Facilitators can use the process defined in the paper to implement PBL for teaching Statistics and Operations research. Shape parameters of the batch in the previous semester can be utilized by facilitators to plan remedial action for the next semester by classifying students as desirable/non-desirable. Techno-management institutes can alleviate the dread and fear of mathematical subjects by integrating PBL with classroom teaching.Originality/valueThe study utilizes an innovative analytical approach of combining shape parameters with classification. It further provides uniqueness in arriving at a classification of batch performance as desirable/non-desirable and utilizes data mining to emphasize a delineating impact of PBL across both critical parameters of the batch and the student. The study also defines a framework for the implementation of PBL for a techno-management program in Statistics and Operations Research.