An early warning system for loan risk assessment using artificial neural networks

Yang, Baoan; Li, Ling X.; Ji, Hai Yan; Xu, Jing

doi:10.1016/s0950-7051(01)00110-1

Cited by 85 publications

(32 citation statements)

References 7 publications

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“…Han et al [16] also developed a fully integrated risk management system (FIRMS) that considers the enterprise and individual risk management project level. Furthermore, various methodologies have been used to develop risk management systems, including case based reasoning [17], artificial neural networks [18], the total risk index [19,20], and the fuzzy analytic hierarchy process [4,21].…”

Section: Research Backgroundmentioning

confidence: 99%

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

Jang

Lee

et al. 2015

Mathematical Problems in Engineering

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Since the 1970s, revenues generated by Korean contractors in international construction have increased rapidly, exceeding USD 70 billion per year in recent years. However, Korean contractors face significant risks from market uncertainty and sensitivity to economic volatility and technical difficulties. As the volatility of these risks threatens project profitability, approximately 15% of bad projects were found to account for 74% of losses from the same international construction sector. Anticipating bad projects via preemptive risk management can better prevent losses so that contractors can enhance the efficiency of bidding decisions during the early stages of a project cycle. In line with these objectives, this paper examines the effect of such factors on the degree of project profitability. The Naïve Bayesian classifier is applied to identify a good project screening tool, which increases practical applicability using binomial variables with limited information that is obtainable in the early stages. The proposed model produced superior classification results that adequately reflect contractor views of risk. It is anticipated that when users apply the proposed model based on their own knowledge and expertise, overall firm profit rates will increase as a result of early abandonment of bad projects as well as the prioritization of good projects before final bidding decisions are made.

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Section: Research Backgroundmentioning

confidence: 99%

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

Jang

Lee

et al. 2015

Mathematical Problems in Engineering

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“…Yang, et al [45] used Artificial Neural Networks (ANN) for detecting financial risk of banks as an early warning, and tested the method.…”

Section: Early Warning Systemsmentioning

confidence: 99%

Early Warning System for Financially Distressed Hospitals Via Data Mining Application

Koyuncugil¹,

Özgülbaş

2011

J Med Syst

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The aim of this study is to develop a Financial Early Warning System (FEWS) for hospitals by using data mining. A data mining method, Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, was used in the study for financial profiling and developing FEWS. The study was conducted in Turkish Ministry of Health's public hospitals which were in financial distress and in need of urgent solutions for financial issues. 839 hospitals were covered and financial data of the year 2008 was obtained from Ministry of Health. As a result of the study, it was determined that 28 hospitals (3.34%) had good financial performance, and 811 hospitals (96.66%) had poor financial performance. According to FEWS, the covered hospitals were categorized into 11 different financial risk profiles, and it was found that 6 variables affected financial risk of hospitals. According to the profiles of hospitals in financial distress, one early warning signal was detected and financial road map was developed for risk mitigation.

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“…Using two real world data sets and testing the models using 10-fold crossvalidation, the author found that among neural architectures the mixture-ofexperts and radial basis function did best, whereas among the traditional methods regression analysis was the most accurate. Thomas (2000) surveyed the techniques for forecasting financial risk of lending to consumers, Yang et al (2001) examined the application of neural networks to an early warning system for loan risk assessment, and J. Zurada and M. Zurada (2002) reported some preliminary results comparing the performance of data mining techniques in predicting the credit worthiness of customers. Also, Feldman and Gross (2005) applied decision trees for detecting mortgage default rates.…”

Section: Prior Researchmentioning

confidence: 99%

Rule Induction Methods For Credit Scoring

Zurada

2007

RBIS

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An early warning system for loan risk assessment using artificial neural networks

Cited by 85 publications

References 7 publications

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

Early Warning System for Financially Distressed Hospitals Via Data Mining Application

Rule Induction Methods For Credit Scoring

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