LAUSR

Abstract Recently, there is increasing need of banks for targeting and acquiring new customers, for fraud detection in real time and for segmentation products through analysis of the customers. Doing it, they can serve their customers better, and can increase the effectiveness of the company. For this purpose, various data mining methods are used which enable extraction of interesting, nontrivial, implicit, previously unknown, and potentially useful patterns or knowledge from huge amounts of data. Traditional data mining methods include classification rule tasks, for their solution there are a number of methods. Among them can be mentioned, for example, Random forest algorithm or C4.5 algorithm. However, accuracy of these methods significantly reduces in the event that some data in databases is missing. These methods are always not optimal for very large databases. The aim of our work is to verify a possible solution of these problems by using the algorithm based on artificial ant colonies. This algorithm was successful in other areas. Therefore, we tested its applicability and accuracy in marketing and business intelligence and compared it with so far used methods. The experimental results showed that the presented algorithm is very effective, robust, and suitable for processing of very large files. It was also found that this algorithm overcomes the previously used algorithms in accuracy. Algorithm is easily implementable on different platforms and can be recommended for using in banking and business intelligence.