ERIC Number: EJ1413314
Record Type: Journal
Publication Date: 2024
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1536-6367
EISSN: EISSN-1536-6359
Two-Stage Classification Method for Individual Workout Status Prediction with Machine Learning Approach
Yoonjae Noh; YoonIl Yoon; Sangjin Kim
Measurement: Interdisciplinary Research and Perspectives, v22 n1 p121-129 2024
The default risk, one of the main risk factors for bonds, should be measured and reflected in the bond yield. Particularly, in the case of financial companies that treat bonds as a major product, failure to properly identify and filter customers' workout status adversely affects returns. This study proposes a two-stage classification algorithm for workout prediction based on the history data of individual customers such as transaction details of financial companies secured after loans, which is collected over 10 years. The first stage is to rank variables that are closely related to the workout application based on feature selection. In the second step, the first to nth cumulative variables input to each machine learning method generate n candidate classifiers, respectively. Among the total candidates, the model with the highest classification accuracy was selected as the optimal one, which is the Gradient Boost combined with F-score-based feature selection.
Descriptors: Prediction, Classification, Accuracy, Risk, Models, Credit (Finance), Measurement Techniques, Money Management, Computer Software, Financial Services, Corporations, Algorithms, Artificial Intelligence, Foreign Countries
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: South Korea
Grant or Contract Numbers: N/A