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This paper is focused on mining and interpreting information about effect of financial literacy on individuals’ behavior from the collected data by soft computing approach. Fuzzy sets and fuzzy logic allows us to formalize linguistic terms such as most of, high literacy and the like and interpret mined knowledge by short quantified sentences of natural language. This way is capable to cover semantic uncertainty in data and concepts. The preliminary results in this position paper have shown that for majority of people of low financial literacy angst and other treats represent serious issues, whereas about half of people with high literacy do not consider these treats as significant. Finally, influence of literacy to anchoring questions is mined and interpreted. Eventually, the paper emphasises needs for further data analysis and comparison.

Mining and Linguistically Interpreting Data from Questionnaires - Influence of Financial Literacy to Behaviour