A Novel Financial Risk Analysis and Early Warning Method based on Data Mining

2020 ◽  
Author(s):  
Junfeng Guo ◽  
Xunchao Qiu
Keyword(s):  
Data Mining ◽  
Risk Analysis ◽  
Early Warning ◽  
Financial Risk

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.

Design and Realization on the Financial Early-Warning Analysis System of Colleges and Universities Based on Data Mining Technology

2014 ◽  
Vol 623 ◽  
pp. 229-233 ◽  
Author(s):  
De Jiang Qi ◽  
Hai Yan Hu
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Financial Management ◽  
Financial Risk ◽  
Warning System ◽  
Colleges And Universities ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
Financial Management System ◽  
Analysis System

In this thesis, in order to solve the student arrearage problems in colleges and universities, risk weight factor is introduced to improve ID3 algorithm through the research on data mining technology and the combination with financial management system of colleges and universities so that ID3 decision-making tree algorithm can classify based on the risk weights of all the factors of the financial data; the early warning system scheme on the student arrearage problems in colleges and universities is designed so as to predict the high-risk defaulting students dynamically and accurately and lay scientific foundations for avoiding financial risk in colleges and universities.

Application of Data Mining Technology in Financial Risk Analysis

2018 ◽  
Vol 102 (4) ◽  
pp. 3699-3713 ◽  
Author(s):  
Maozhu Jin ◽  
Yanan Wang ◽  
Yucheng Zeng
Keyword(s):  
Data Mining ◽  
Risk Analysis ◽  
Financial Risk ◽  
Mining Technology

Country Risk Analysis of Bangladesh

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Zahidur Rahman ◽  
Jannatul Ferdous Bristy
Keyword(s):  
Risk Analysis ◽  
Financial Risk ◽  
Political Risk ◽  
Multinational Companies ◽  
Country Risk ◽  
Low Risk ◽  
Primary Data ◽  
Policy Makers ◽  
Financial Factors ◽  
Political Economic

In the endeavor of conquering the worlds consumers, multinational companies face enormous risks. Such risks may arise from different political, economic, and financial factors. These factors are commonly referred to country risk as a whole. Focusing Bangladesh in this regard, objective of this study is to find out the level of country risk in terms of political, economic, and financial riskiness. Analysis of country risk has been done using an internationally recognized methodology named International Country Risk Guide (ICRG). For political risk analysis, primary data has been collected from 20 journalists, bureaucrats and policy makers, business persons, corporate professionals, and academicians with a structured closed-ended questionnaire. Results indicate that Bangladesh is in high risk position in terms of political risk, low risk position in terms of economic risk and very low risk position in terms of financial risk. Compositely, Bangladesh has been found to be a moderately risky country for investment.

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