<![CDATA[In the field of financial risk assessment, understanding and categorizing credit risk is critical for effective decision making. This study explores the use of KMeans Clustering, implemented via RapidMiner, to visualize and describe risky credit categories. Leverage a rich data set of related financial attributes, including total income, education, family status, residence type, ownership, and more. K-Means clustering facilitates customer segmentation into different risk groups based on similar credit profiles. Through the application of this grouping technique, financial institutions can gain insight into potential credit defaults, thereby enabling proactive risk management strategies. The visualization aspect enhances interpretability, enabling stakeholders to understand and navigate the complex credit risk landscape more intuitively. By leveraging the capabilities of RapidMiner, this research contributes to the advancement of data-driven methodologies in financial risk assessment, offering a practical approach to visualizing and understanding credit risk categories. These findings provide valuable insights to financial analysts, policy makers and decision makers, empowering them to make informed decisions and mitigate credit risks effectively.]]>
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