The widespread use of databases and the fast increase of the volume of data they store are creating a problem and a new opportunity for credit companies. These companies are realizing the necessity of making an efficient use of the information stored in their databases, extracting useful knowledge to support their decision-making process. Nowadays, knowledge is the most valuable asset a company or nation may have. Several companies are investing large sums of money in the development of new computational tools able to extract meaningful knowledge from large volumes of data collected over many years. Among such companies, companies working with credit risk analysis have invested heavily in sophisticated computational tools to perform efficient data mining in their databases. The behavior of the financial market is affected by a large number of political, economic, and psychological factors, which are correlated and interact among themselves in a complex way. The majority of these relations seems to be probabilistic and non-linear. Thus, these relations are hard to express through deterministic rules. Simon (1960) classifies the financial management decisions in a continuous interval, whose limits are non-structure and highly structured. The highly structured decisions are those where the processes necessary for the achievement of a good solution are known beforehand and several computational tools to support the decisions are available. For non-structured decisions, only the managers’ intuition and experience are used. Specialists may support these managers, but the final decisions involve a substantial amount of subjective elements. Highly non-structured problems are not easily adapted to the computer-based conventional analysis methods or decision support systems (Hawley, Johnson, & Raina, 1996).
Data Mining Usage in Corporate Information Security: Intrusion Detection Applications
