This study explores naming patterns in Turkish and Russian financial discourse through a distant reading approach, using the speeches of the Central Bank Governors of Türkiye and Russia as representative material. Conducted within the framework of digital humanities, the research demonstrates the potential of the distant reading approach for contrastive linguistic analysis. At the first stage, a small, representative corpus was compiled in Turkish and Russian using BootCat tools. At the second stage, the corpora were analyzed with Voyant Tools, which enabled the visualization of the most frequent lexical units used to name financial phenomena in both languages and the relations among these units. At the third stage, Igor Mel’čuk’s Meaning–Text Theory was applied to interpret the lexical relations between the visualized units. The focus was placed on two naming patterns: PhenomenonAttribute and PhenomenonAction/State, examined comparatively in Turkish and Russian. The results indicate that Turkish financial discourse often employs borrowings and calques, whereas Russian discourse favors lexicalized forms and secondary naming patterns for financial and economic phenomena. Although these observations are limited to the selected material, the findings highlight the pedagogical and methodological value of data-driven analysis. The study underscores the potential of digital visualization tools for linguistic inquiry and advocates for the development of applied resources, such as annotated corpora, for the Turkish-Russian language pair.