<![CDATA[In an era of increasing financial complexity, the ability to apply mathematical reasoning in financial decision making has become a critical competence for university students, yet many still struggle to translate mathematical knowledge into practical financial contexts. This study aims to develop a data-driven empirical profile of students’ financial mathematics ability based on academic performance records. A descriptive quantitative approach was employed using secondary data from 26 students enrolled in an Economic Mathematics course, including assignment scores, midterm examinations, final examinations, and final academic scores. The data were analyzed using descriptive statistics combined with interpretive analysis to identify performance patterns, variability, and achievement distribution. The findings reveal that students’ financial mathematics ability is generally positioned at a moderate to high level, with a mean final score of 70.09 and a median of 78.25, indicating a negatively skewed distribution influenced by lower-performing students. While 61.54% of students were classified in high and very high categories, 23.08% remained in the low category, reflecting a substantial performance gap. In addition, students performed better in assignments and midterm examinations than in final examinations, suggesting challenges in comprehensive and independent problem-solving contexts. This study introduces a data-driven descriptive profiling approach that integrates financial literacy, mathematical literacy, and academic performance into a unified analytical framework. The findings offer meaningful implications for strengthening financial numeracy and improving instructional strategies in higher education.]]>
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