A two-route CNN model for bank account classification with heterogeneous data

Abstract

Classifying bank accounts by using transaction data is encouraging in cracking down on illegal financial activities. However, few research simultaneously use heterogenous features, which are embedded in the time series data. In this paper, a two route convolution neural network TRHD-CNN model, fed with two types of heterogeneous feature matrices, is proposed for classifying the bank accounts. TRHD-CNN adopts divide and conquer strategy to extract characteristics from two types of data source independently. The strategy is proved able in mining complementary classification characteristics. We firstly transfer the original log data into a directed and dynamic transaction network. On the basis of that, two feature generation methods are devised for extracting information from local topological structure and time series transaction respectively. A DirectedWalk method is developed in this paper to learning the network vector of vertices used for embedding the neighbor relationship of bank account. The extensive experimental results, conducted on a real bank transaction dataset that contains illegal pyramid selling accounts, show the significant advantage of TRHD-CNN over the existing methods. TRHD-CNN can provide recall scores up to 5.15% higher than competing methods. In addition, the two-route architecture of TRHD-CNN is easy to extend to multi-route scenarios and other fields.

A Collaborative Mining-Based Decision Support Model for Granting Personal Loans in the Banking Sector

“…The performance of the proposed approach has been measured and according to the research's results, the proposed approach reached a higher performance when compared with statistical approaches. Customers' classification is also targeted in detecting fraudulent customers' accounts such as in (Lv, et al, 2019). The research applied convolution neural network mining technique in detecting the illegal account.…”

Data Mining in Banking Sector Using Weighted Decision Jungle Method

“…Banking industries have used data mining techniques in various applications, especially on bank failure prediction [1][2][3], possible bank customer churns identification [4], fraudulent transaction detection [5], customer segmentation [8][9][10], predictions on bank telemarketing [11][12][13][14], and sentiment analysis for bank customers [15]. Some of the classification studies in the banking sector have been compared in Table 1.…”

“…Hence, data mining can be used to extract meaningful information from these collected banking data, to enable banking institutions to make better decision-making process. For example, classification, which is one of the most popular data mining techniques, can be used to predict bank failures [1][2][3], to estimate bank customer churns [4], to detect frauds [5], and to evaluate loan approvals [6].…”

Various Classifiers Performance Based Machine Learning Methods

“….Lv, et al [14] studied about the routes of convolution neural network TRHD-CNN model, input with the different feature matrices, regarding classification of the bank accounts. TRHD-CNN follows divide and conquer techniques for the extraction of features from the data source in an independent way.…”