The goal of the paper is to present the opportunity of exploiting data analysis methods and semantic models to discover customer profiles from financial databases. The solution to the problem is illustrated by the example of credit cards promotion strategy on the basis of historical data coming from the bank’s databases. The database contains information, personal data, and transactions. The idea is founded on data exploration methods and sematic models. With this purpose in mind, multiple algorithms of clustering and classification were applied, the results of which were exploited to elaborate the ontology and to define the customer profile to be used in decision-making.