An Anomaly Recognition Algorithm for Financial Data based on Self-Organizing Fuzzy Rule

Financial data has the characteristic of nonstationary, nonlinear and low SNR. Due to the lack of financial data anomalies training set, which results in greater difficulties in the intelligent algorithm on financial data anomaly recognition. Therefore, an anomaly recognition algorithm for financial data based on self-organizing fuzzy rule is proposed in this paper. The financial transaction data is generated by the time sequence of the time span of the week, and then select the total amount of the transaction, the discrete factor of the transaction, the number of transfer as the characteristics of the financial account data. The validity of the method is illustrated by the experimental data. Introduction Suspicious financial data monitoring is the important component of financial supervision, it can learn from a large number of complex financial data and identify the flow of funds in the financial account, automatically identify suspicious abnormal financial data from the normal mode, and submits it to the financial supervision department. Whether the financial data identified by the financial supervision department is abnormal. Anomaly recognition makes the financial supervision department only for the extraction of suspicious financial data analysis, improves work efficiency, and easily finds out abnormal financial data. Most judgment of the abnormal pattern of traditional methods on the basic theory of statistics for the base, by constructing some good model to carry on the analysis, need higher modeling skills, and to some hypothesis as a premise, such as the stationarity assumption, the assumption of normal distribution and so on, in practice content easily lead to low detection rate and high false alarm rate [1]. Along with the rise and development of data mining and knowledge discovery, some methods like neural network [2], genetic algorithm, based on case reasoning [3], rule induction [4], wavelet analysis [5] [6], k-nearest local anomaly detection [7], a supervised pattern recognition [8], are applied to the abnormal pattern recognition and discovery. In contrast, self-organizing data mining has obvious advantages in dealing with noise data, data reduction and nonlinear modeling, therefore, an anomaly recognition algorithm for financial data based on self-organizing fuzzy rule is proposed in this paper. The financial transaction data is generated by the time sequence of the time span of the week, and then select the total amount of the transaction, the discrete factor of the transaction, the number of transfer as the characteristics of the financial account data. The validity of the method is illustrated by the experimental data. Design of the algorithm Step1 The financial transaction data is generated by the time sequence of the time span of the week, and then select the total amount of the transaction, the discrete factor of the transaction, the number of transfer as the characteristics of the financial account data. Set up financial data set W, including the sample size of the two equal groups of samples A and B, the length of N. P={1,2,. ,m},Q={1,2,. ,N} (1) 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer (MMEBC 2016) © 2016. The authors Published by Atlantis Press 2137 Step2 Let k=1, for the ith variable, the least square method is used to estimate the parameters of the whole sample set, get xi=a0+bx0. The parameter estimation is carried out by using the least square method on the data set A and B respectively. 0 0 0 0  = +  = +  A A A i B B B i x a b x x a b x (2) The minimum deviation criterion is: 2 1 1 ( ) ( ) 1 ( ) ( ) η = − = ∑ A B N i i i k i x k x k N x k , 1 1 min( ) η η = i i (3) Step3 Let k=k+1, in the m independent variables, take the k different attribute variables, using the least squares method to estimate the parameters of the complete set of parameters, and get the k element equation group: 01 11 ( 1)1 1 0 02 12 ( 1)2 2 0