Abstract: A fraud score calculating program primarily for use in determining the possibility of credit card fraud can calculate a score reflecting the reliability of a model created based on Bayesian theory. Various factors are extracted from authorization data, and cases are selected for various combinations of the factors. A fraud score is calculated for each case, and a result of score calculation is specified using at least one of the calculated scores. The result of score calculation may be an average of a plurality of the calculated scores. When each score reflects reliability, the result of score calculation may be the largest of the calculated scores.

Abstract: A data merging program causes a computer to perform a step of selecting a first cell as a starting point of merging; a step of comparing a first numerical value, which is recorded in the first cell, with a preset reference value; a step of, if the first numerical value is smaller than the reference value, calculating a total value of the first numerical value and a second numerical value recorded in a second cell adjacent to the first cell in the same column; a step of comparing the total value with the reference value and, if the total value is smaller than the reference value, setting a third cell into which the first and second cells are merged and recording the total value in the third cell; and a step of selecting the third cell as a new starting point of merging.

Abstract: A fraud determining system adds, to authorization data concerning a present use, history information, such as a difference in time of use or monetary value between the authorization data and authorization data concerning an immediately previous use. When a determination as to whether the present use involves a fraud is made on the basis of the authorization data concerning the present use, it is possible to determine whether or not the present use is irregular from the history information. The result of the determination is reflected in a score, and therefore, the score can be calculated with high reliability.

Abstract: A fraud score calculating program primarily for use in determining the possibility of credit card fraud can calculate a score reflecting the reliability of a model created based on Bayesian theory. A model which is stored in a fraud detection model database 120 obtains new authorization data and continues learning as the number of data samples increases. Calculation of the score is performed by a calculation logic provided in a scoring subsystem 110. The sample number data for a case corresponding to the authorization data are obtained from the model, and the probability of the occurrence of fraudulent use is calculated. The reliability of the model is also calculated on the basis of, for example, the number of the registered samples, and a fraud score is calculated using both the calculated probability of the occurrence of fraud and the calculated reliability of the model.

Abstract: A fraud score calculating program primarily for use in determining the possibility of credit card fraud can calculate a score reflecting the reliability of a model created based on Bayesian theory. Various factors are extracted from authorization data, and cases are selected for various combinations of the factors. A fraud score is calculated for each case, and a result of score calculation is specified using at least one of the calculated scores. The result of score calculation may be an average of a plurality of the calculated scores. When each score reflects reliability, the result of score calculation may be the largest of the calculated scores.

Abstract: A data merging program causes a computer to perform a step of selecting a first cell as a starting point of merging; a step of comparing a first numerical value, which is recorded in the first cell, with a preset reference value; a step of, if the first numerical value is smaller than the reference value, calculating a total value of the first numerical value and a second numerical value recorded in a second cell adjacent to the first cell in the same column; a step of comparing the total value with the reference value and, if the total value is smaller than the reference value, setting a third cell into which the first and second cells are merged and recording the total value in the third cell; and a step of selecting the third cell as a new starting point of merging.