This paper aims to develop and apply a clustering model to examine risk tolerance and risk-taking behavior among university students, while exploring potential connections between these two perspectives. An integrated model incorporating demographic and socioeconomic factors affecting risk profiles was developed for analyzing variables including age, gender, income, educational background, family background, and financial literacy. K-means clustering was applied to analyze data collected from a group of university students. Outliers that deviate significantly from cluster centroids was identified and analyzed. The analysis successfully categorized individuals into three distinct clusters: high, low, and moderate risk as well as identified significant outliers from cluster centroids. Based on this, specific characteristics of the clusters and outliers can be revealed by examining the various circumstances on their risk profiles. Especially those with a low risk tolerance but high risk-taking behavior are concerned, since they are exposed to serious risks that can affect their well-being.