Title Data reduction influence on the accuracy of credit risk estimation models /
Translation of Title Duomenų apimties mažinimo įtaka kredito rizikos vertinimo modelių tikslumui.
Authors Mileris, Ricardas ; Boguslauskas, Vytautas
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Is Part of Inžinerinė ekonomika = Engineering economics.. Kaunas : Technologija. 2010, vol. 66, iss. 1, p. 5-11.. ISSN 1392-2785. eISSN 2029-5839
Keywords [eng] artificial neural networks ; credit risk ; discriminant analysis ; factor analysis ; logistic regression
Abstract [eng] Credits in banks have risk of being defaulted. The main purpose of credit risk estimation in banks is the determination of company‘s ability to fulfil its financial obligations in future. It is very important to have a proper instrument for the estimation of credit risk in banks because it reduces potential loss due to crediting reliable clients. Banks develop internal credit risk estimation models and various data analysis methods can be applied for this purpose. Statistical predictive analytic techniques and artificial intelligence can be used to determine default risk levels. Banks must also have data about clients from the activity in the past. To understand risk levels of credits, banks usually collect information about borrowers. Financial ratios remain primary variables for predicting corporate financial distress. The principal financial ratios as variables for the analysis are indicators of company‘s financial structure, solvency, profitability and cash flow. Credit risk estimation models are based on the analysis of this data. Using these models it becomes possible to predict the default possibility of new clients. Credit risk estimation models in banks differ significantly in architecture and operating design. The main reason for these differences is that banks’ models are assigned by bank personnel and are usually not revealed to outsiders. [...].
Published Kaunas : Technologija
Type Journal article
Language English
Publication date 2010
CC license CC license description