Genetic Programming with Dynamic Bayesian Network based Credit Risk Assessment Model

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Jeyakarthic:2023:ICAIS,

• author = "M. Jeyakarthic and R. Ramesh",
• booktitle = "2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)",
• title = "Genetic Programming with Dynamic Bayesian Network based Credit Risk Assessment Model",
• year = "2023",
• pages = "845--850",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Heuristic algorithms, Decision making, Organizations, Data models, Bayes methods, Dynamic programming, Credit risk assessment, Credit scoring, Dynamic Bayesian network, Data classification",
• DOI = "doi:10.1109/ICAIS56108.2023.10073788",
• abstract = "An accurate credit risk assessment system is essential to a financial organization for its impeccable and proper functioning. Precise predictions of credit risk would enable them to continue their function transparently and gainfully. Since the rate of loan defaults was progressively rising, bank officials find it very difficult to properly evaluate loan requests. Many credit risk analysis methods were used for evaluating credit risk of the customer data. The assessment of the credit risk data results in the decision to grant the loan to the debtor or deny the application of the debtor which can be tough task that includes the deep analysis of the data offered by the customer or the credit data of customer. This study develops a Genetic Programming with Dynamic Bayesian Network based Credit Risk Assessment (GPDBN-CRA) model. The presented GPDBN-CRA model helps the financial institutions in the decision making process of accepting a loan request or not. To do so, the presented GPDBN-CRA model normalizes the customer data as an initial stage. For credit risk evaluation, the presented GPDBN-CRA method applies DBN model to perform classification model. To enhance the assessment performance of the GPDBN-CRA model, the GP technique is applied for hyperparameter tuning process. The experimental validation of the presented GPDBN-CRA method can be tested using customer dataset. The extensive outcomes stated the improved outcomes of the GPDBN-CRA method.",
• notes = "Also known as \cite{10073788}",

}

Genetic Programming entries for M Jeyakarthic R Ramesh

Citations