Research On Pattern Recognition And Intelligent Financial Supervision Of Capital Flow In Financial Network

Finance is one of the main pillars in modern economy, and the stability of financial system is the major tasks and core responsibilities for the world's financial supervision authorities. Financial information technology has changed the traditional financial system and business model: the financial institutions become electronic connected, with more complexity and tend to be globalization; the capital flows within the financial institution are filled with large volume of transactions with high speed and showed as innovative forms. Large amount of money are flowing in the financial system to meet the needs of normal economic activities, but also few abnormal, illegal capital flows are carring a variety bad ideas and intent to escape regulation, which brought big challenges to supervision authorities. This study aims to establish a scientific and systematic analysis method to effectively provide intelligent decision support systems for financial institutions and financial regulators.This paper is organized as the following orders: first we study the main constrains and bottlenecks in anti-money laundering business process based on Theory of Constraints (TOC), then we conduct research to identify the abnormal customer in capital flow based on normal patterns; then we use Scan Statistics to identify the suspicious transactions from a single customer angle; and we establish a suspicious transaction identification method using sequence matching based algorithms; also we establish a social network based method to identify hidden groups in financial networks. In this paper, the main work are as follows:(1) We conduct a in-depth interviews under the framework of Theory of Constrains (TOC) to study the main constraints and bottlenecks in the AML organizational system, we surveyed those relevant agencies, figure out their AML responsibilities, their operations and business process, the information distribution they have for detecting suspicious reports, also the way transferring valuable data. Also we conclude the main AML bottlenecks existed in financial institutions and supervision agencies from three aspects: physical constrains, policy constraints and behavioral constraints.(2) We establish a suspicious customer identification method based on normal pattern. First we analyze the main factors which affect the capital flow of a company; those factors include the macroeconomic environment, the industry characteristics, the firm size, and the regional differences. Then we use statistical pattern recognition to analyze the distribution of capital flow from different latitude: transaction amount, time characteristics, regional characteristics, capital direction, and interval of the sequent transactions.(3) We also use scan statistic method to identify suspicious transaction activity in single account angle. Based on the principle of Scan Statistics, we transfer the identification of suspicious transaction into a scan statistics problem. In conjunction with the previous normal pattern model, we design a scan statistics based monitoring algorithm. The Experiment results show that the proposed algorithm can effectively detect the abnormal behavior in short observing period. Results also confirm that this intelligent algorithm can largely reduce the Type I error, which is also to say that it can effectively reduce the omitting rate. However, we need to find way to further reduce Type II errors (false positives) and improve the sensitivity results of the algorithm.(4) Based on sequence matching algorithm, we establish an algorithm to identify suspicious transaction using the main sources in financial institutions: customer information, account information, and transaction information. In order to achieve the final goals: classify normal transactions and suspicious transactions, we collaborate sequence matching algorithm, and establish the query sequences in this problem- high-risk transaction fragments, the reference sequence- the history of the query customer and also transactions of other customers in the same peer group. Also we use Euclidean distance based and cosine based similarity kernel to calculate the similarities between query sequences and reference sequences. The final stage of this algorithm label the query sequences and classify them into normal and abnormal based on given threshold.(5)Finally, we propose an algorithm to find hidden groups in financial networks based on social network theory. The paper first discuss the construction of a financial network using social network theory, and then bring up the practical issue of hidden group in financial supervision. Then we establish a model to identify hidden group in financial networks for supervision center. The experiment results show that this model is feasible in identifying small gangs or extracting related criminal hidden groups from a given nut.The main innovative ideas can be concluded as following:First, the method of identification of the main constraints and bottlenecks in AML organization system provide us the valuable theoretical basis to study other similar supervision problems.In previous research about current financial regulation issue, many scholars in and abroad solely study single point of the issue or focus on single link within business process chain. However, such research can hardly grasp the overall picture of a complex problem, or find the key factors which affect the supervision efficiency.This paper views the AML problem in an overall picture by surveying the upstream and downstream within the whole AML business process. Through analyzing the AML responsibilities for each AML related agencies, their operations and treatment process, the information distribution they have to detect suspicious reports, also the way to transferring valuable data, we draw the whole picture for anti-money laundering business process. We also summed up the key constraint from physical, policy and behavior angles to identify the most influential factors on AML efficiency.Secondly, we propose a pattern to identify suspicious behavior by comparing normal pattern, which can provide insight to improve Administrative rules for the reporting of large-value and suspicious activity reports, and also it explores new direction for suspicious detection.When reporting large amount or suspicious transactions according to Administrative rules for the reporting of large-value and suspicious activity reports, the financial institutions encountered problems of'hard to quantify', which brought difficulty to monitoring and detecting work. On the other hand, current extracting tool in financial institutions are mainly rule based system, with low efficiency and the criminals can easily escape the regulation by simply learn from the regulation rules.We proposed intelligent algorithm which can easily adjust the efficiency by changing different parameters, it makes the criteria difficult to escape. On the other hand, using intelligent tools, the type I and type II error can be reduced so that we can free the experts from huge labor.Thirdly, we incorporate the customer relationship information into detection, and propose algorithm to identify hidden groups in financial networks. This provides a new idea to regulation platform.The source and destination information of a transaction played important role in suspicious detection. However, traditional regulatory solely focus the direct transaction partner in suspicious detection. This encourages the criminals to use offshore company or variety financial services as medium to veil these illegal activities. Finding hidden groups within large financial networks is of urgency.In this study, we construct financial network using social network related theory, we extract networks which describe transaction relationship, and try to access the hidden groups in financial networks using intelligent monitoring tools.Fourth, we establish suspicious extracting tools from three layers: network layer, customer layer, and transaction layer for financial institutions and supervision agencies, according to their information distribution.Base on different responsibilities and information distribution of financial institutions and supervision agencies, we design intelligent tools which can exactly need their requirements, separately. The monitoring system based on algorithms from these three layers: network layer, customer layer, and transaction layer, can effectively serve the current financial regulation practice.