The goal of credit scoring is to identify abnormalities, aiding decision making and
maintaining the order of financial transactions. Due to the small number of default records …

This research endeavors to comprehensively evaluate and compare the performance of
three prominent machine learning algorithms—Deep Learning (DL), Decision Trees (DT) …

Credit scoring models based on machine learning often need to work on accuracy and
interpretability in practical applications. Original KCDWU has a more prominent adaptive …

The synthetic minority oversampling technique (SMOTE) is the most general and popular
solution for imbalanced data. Although SMOTE is effective in solving the class imbalance …

Fraud detection in banking systems is crucial for financial stability, customer protection,
reputation management, and regulatory compliance. Machine Learning (ML) is vital in …

Teknik pembelajaran mesin/machine learning banyak digunakan di berbagai bidang dan
data diperlukan untuk melatih model. Namun, distribusi kelas di sebagian besar kumpulan …

With the growth of people's demand for loans, banks and other financial institutions put
forward higher requirements for customer credit risk level classification, the purpose is to …

Credit scoring is a critical function in the banking industry, helping to assess borrowers'
creditworthiness and mitigate lending risks. Traditional credit scoring systems based on …

Using norms to guide and coordinate interactions has gained tremendous attention in the
multi-agent community. However, new challenges arise as the interest moves towards …

Consumer credit assessment has always been a crucial concern in the financial industry. It
involves evaluating an individual's credit history and their ability to repay loans, playing a …