• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.539-547
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• 2012

Volume survey for earthwork is conducted periodically at every month at large scaled construction sites for request payment for the construction. Currently, the earthwork volume is calculated on the overlapped drawing for two cross sections which were created by new and previous surveying data observed by GPS and TS. However the survey is required lots of time and expenses as it is a pin-point surveying method thus surveyors intend to get data only for the heavy undulated topographic features chosen by himself to save time and expenses. Consequently, it causes poor results due to low dense measurement as well as it's not matched with the results by another surveyors. As a result, the conflicts related in earthwork volume calculation often arises among the owner, contractors and sub contractors. In this study, we developed a method to get surveying data for fast and accurate volume calculation using 3D Mobile Laser System and conducted an application test. Also we carried out experiments for topographic survey using MLS to judge for the application additionally. The results showed that we could reduce a time for cross section survey from 48 hours to 2 hours and collected high dense data which have 0.2m interval instead of 3m interval compare with existing method. Also we could make a map which has high accuracy within 10cm in horizontal through topographic survey using MLS.