Shijia Gao, Dongming Xu, Huaiqing Wang and Peter Green
Criminal elements in today's technology‐driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many…
Abstract
Purpose
Criminal elements in today's technology‐driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many anti‐money laundering (AML) solutions have been in place for some time within the financial community, they face the challenge to adapt to the ever‐changing risk and methods in relation to money laundering (ML). This research seeks to focus on ML control and prevention, which aim to automate the monitoring and diagnosing of ML schemes in order to report suspicious activities to banks.
Design/methodology/approach
The research adopted the technology of intelligent agents to provide a more adaptive, flexible, and knowledge‐based solution for AML.
Findings
Based on the analysis of monitoring, diagnosing, and reporting of ML activities occurring in electronic transactions, several types of intelligent agents are proposed and a multi‐agent framework is presented for AML. Furthermore, business knowledge such as business rules and strategies are extracted from AML practice, and applied to the design of individual agents to make them act autonomously and collaboratively to fulfil the goal of ML detection.
Practical implications
The proposed multi‐agent framework is a stand‐alone system, which can be integrated by banks to combat ML. Although it is a uni‐bank framework at present, it can be extended to multi‐bank application in the future.
Originality/value
The research explores the approach of applying an intelligent agent for knowledge‐based AML in an electronic transaction environment for banks. By separating business logic from the business model, such a business‐rules approach can enhance the flexibility and adaptability of the agent‐based AML system.
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Kun Chen, Xin Li and Huaiqing Wang
Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including…
Abstract
Purpose
Although big data analytics has reaped great business rewards, big data system design and integration still face challenges resulting from the demanding environment, including challenges involving variety, uncertainty, and complexity. These characteristics in big data systems demand flexible and agile integration architectures. Furthermore, a formal model is needed to support design and verification. The purpose of this paper is to resolve the two problems with a collective intelligence (CI) model.
Design/methodology/approach
In the conceptual CI framework as proposed by Schut (2010), a CI design should be comprised of a general model, which has formal form for verification and validation, and also a specific model, which is an implementable system architecture. After analyzing the requirements of system integration in big data environments, the authors apply the CI framework to resolve the integration problem. In the model instantiation, the authors use multi-agent paradigm as the specific model, and the hierarchical colored Petri Net (PN) as the general model.
Findings
First, multi-agent paradigm is a good implementation for reuse and integration of big data analytics modules in an agile and loosely coupled method. Second, the PN models provide effective simulation results in the system design period. It gives advice on business process design and workload balance control. Third, the CI framework provides an incrementally build and deployed method for system integration. It is especially suitable to the dynamic data analytics environment. These findings have both theoretical and managerial implications.
Originality/value
In this paper, the authors propose a CI framework, which includes both practical architectures and theoretical foundations, to solve the system integration problem in big data environment. It provides a new point of view to dynamically integrate large-scale modules in an organization. This paper also has practical suggestions for Chief Technical Officers, who want to employ big data technologies in their companies.
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Abstract
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Huaiqing Zhang, Chunxian Guo, Xiangfeng Su and Lin Chen
The multi-quadrics (MQ) function is a kind of radial basis function. And the MQ method has been successfully adopted as a type of meshless method in solving electromagnetic…
Abstract
Purpose
The multi-quadrics (MQ) function is a kind of radial basis function. And the MQ method has been successfully adopted as a type of meshless method in solving electromagnetic boundary value problems. However, the accuracy of MQ interpolation or solving equations is severely influenced by shape parameter. Thus the purpose of this paper is to propose a case-independent shape parameter selection strategy from the aspect of coefficient matrix condition number analysis.
Design/methodology/approach
The condition number of coefficient matrix is investigated. It is shown that the condition number is only a function of shape parameter and MQ node number, and is irrelevant to the interpolated function which means case-independent. The effective condition number which takes into account the interpolated function is introduced. Then, the relation between the relative root mean square error and condition number is analyzed. Three numerical experiments as transmission line, cable channel and grounding metal box model were carried out.
Findings
In the numerical experiments, there is an approximate linear relationship between the logarithm of the condition number and shape parameter, an approximate quadratic relationship with node number. And the optimal shape parameter is corresponding to the early stage of condition number oscillation.
Originality/value
This paper proposed a case-independent shape parameter selection strategy. For a finite precision computation, the upper limit of the condition number is predetermined. Therefore, the shape parameter can be chosen where condition number oscillates in early stage.
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Zhang Huaiqing, Nie Xin, Chen Yu and Fu Zhihong
The purpose of this paper is to solve the interface discontinuities in radial basis function (RBF) method for multi-medium boundary value problems (BVPs). The discontinuity of the…
Abstract
Purpose
The purpose of this paper is to solve the interface discontinuities in radial basis function (RBF) method for multi-medium boundary value problems (BVPs). The discontinuity of the solution derivatives is not easily handled with RBF method because of infinitely smoothness.
Design/methodology/approach
The essence of solving BVP is to construct the continuous potential function surfaces. Hence, from constructing surface aspect, this paper proposed and compared the global and subzone schemes for RBF method. Their implementation schemes and mathematic models can then be derived. Numerical experiments and comparison are carried out for electric and magnetic field calculation.
Findings
In the numerical experiments, the subzone scheme has shown its significant advantageous, it can approximate not only the potential function but also its derivative on interface boundary with high accuracy. So the physical characteristics of discontinuities on the interface can be revealed clearly. The overall precision is significantly improved.
Originality/value
This paper proposed an effective subzone scheme for RBF method in multi-medium BVP. It is an improvement for RBF method based on its domain decomposition idea. And it is also a candidate for solving complex BVP.
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Sanjita Jaipuria and Siba Sankar Mahapatra
The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period…
Abstract
Purpose
The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).
Design/methodology/approach
A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.
Findings
A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).
Research limitations/implications
This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.
Practical implications
The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.
Originality/value
This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.