Research on money laundering risk assessment of customers – based on the empirical research of China

2016 ◽  
Vol 19 (3) ◽  
pp. 249-263 ◽  
Author(s):  
Yao-Wen Xue ◽  
Yan-Hua Zhang
Keyword(s):  
Risk Assessment ◽  
Decision Tree ◽  
Money Laundering ◽  
Decision Tree Model ◽  
Tree Model ◽  
Entropy Weight ◽  
Content Type ◽  
Regulatory Approach ◽  
Entropy Weight Method ◽  
Weight Method

Purpose To implement a risk-based regulatory approach, this paper aims to make an assessment on customers' money laundering risk and conducts some applications. Design/methodology/approach During the transition of a regulatory approach from “rule-based” to “risk-based”, this paper considers that the area of a customer, types of business and the industries to which the customer belongs are the main indicators to judge money laundering risk of a customer. Based on the statistical analysis of 221 typical money laundering cases, first-class index weights are given by using the entropy weight method and then by combining with the membership function, this paper determines a customer’s money laundering risk levels. On the basis of the entropy weight method, this paper uses the C5.0 algorithm to construct a decision tree model and then carries out application research on customer money laundering risk assessment to verify the effectiveness of the entropy weight method and the decision tree model. Findings This empirical research found the weights of three key money laundering indicators: customer areas, business types and corresponding industries. Originality/value Asserting that current money laundering risk assessments of customers are marginal at best, this paper contends from the perspective of practice, and applies the entropy weight method and the decision tree model for money laundering risk assessment of customers.

scholarly journals Personal bankruptcy prediction using decision tree model

2019 ◽  
Vol 24 (47) ◽  
pp. 157-170 ◽  
Author(s):  
Sharifah Heryati Syed Nor ◽  
Shafinar Ismail ◽  
Bee Wah Yap
Keyword(s):  
Decision Tree ◽  
Financial Institutions ◽  
Negative Impact ◽  
Bankruptcy Prediction ◽  
Decision Tree Model ◽  
Tree Model ◽  
Personal Bankruptcy ◽  
Content Type ◽  
To Come ◽  
The Right

Purpose Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties and not allowed to start any business nor be a part of any company’s management. Bankrupts also will be denied from any loan application, restricted from travelling overseas and cannot act as a guarantor. This paper aims to investigate this problem by developing the personal bankruptcy prediction model using the decision tree technique. Design/methodology/approach In this paper, bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24,546 cases with 17 per cent settled cases and 83 per cent terminated cases. The data included a dependent variable, i.e. bankruptcy status (Y = 1(bankrupt), Y = 0 (non-bankrupt)) and 12 predictors. SAS Enterprise Miner 14.1 software was used to develop the decision tree model. Findings Upon completion, this study succeeds to come out with the profiles of bankrupts, reliable personal bankruptcy scoring model and significant variables of personal bankruptcy. Practical implications This decision tree model is possible for patent and income generation. Financial institutions are able to use this model for potential borrowers to predict their tendency toward personal bankruptcy. Social implications Create awareness to society on significant variables of personal bankruptcy so that they can avoid being a bankrupt. Originality/value This decision tree model is able to facilitate and assist financial institutions in evaluating and assessing their potential borrower. It helps to identify potential defaulting borrowers. It also can assist financial institutions in implementing the right strategies to avoid defaulting borrowers.

scholarly journals The Risk Assessment of Debris Flow Hazards in Banshanmen Gully Based on the Entropy Weight-Normal Cloud Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
X. B. Gu ◽  
S. T. Wu ◽  
X. J. Ji ◽  
Y. H. Zhu
Keyword(s):  
Risk Assessment ◽  
Debris Flow ◽  
Cloud Model ◽  
Risk Estimation ◽  
Assessment Method ◽  
Risk Level ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Index Weight

The debris flow is one of the geological hazards; its occurrence is complex, fuzzy, and random. And it is affected by many indices; a new multi-index assessment method is proposed to analyze the risk level of debris flow based on the entropy weight-normal cloud model in Banshanmen gully. The index weight is calculated by using the entropy weight method. Then, the certainty degree of each index belonging to the corresponding cloud is obtained by using the cloud model. The final risk level of debris flow is determined according to the synthetic certainty degree. The conclusions are drawn that the method is feasible and accurate rate of risk estimation for debris flow is very high, so a new method and thoughts for the risk assessment of debris flow can be provided in the future.

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yejin Lee ◽  
Dae-Young Kim
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Tourism Industry ◽  
Decision Tree Model ◽  
Geographical Distance ◽  
Tree Model ◽  
Content Type ◽  
Hotel Booking ◽  
The Relationship

Purpose Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia). Design/methodology/approach The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia. Research limitations/implications This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration. Originality/value This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

Regional foods in Brazilian school meals

2015 ◽  
Vol 117 (6) ◽  
pp. 1706-1719 ◽  
Author(s):  
Rafaela Karen Fabri ◽  
Rossana Pacheco da Costa Proença ◽  
Suellen Secchi Martinelli ◽  
Suzi Barletto Cavalli
Keyword(s):  
Decision Tree ◽  
School Environment ◽  
Decision Tree Model ◽  
School Meals ◽  
Tree Model ◽  
School Food ◽  
Content Type ◽  
Cultural Aspects ◽  
Stage 1 ◽  
Two Stages

Purpose – The purpose of this paper is to identify regional foods and analyze its use on school menus of a Brazilian city, as well as the respect to symbolic and cultural aspects related to it. Design/methodology/approach – The study was conducted in two stages. In the first stage, regional foods were identified through interviews with key school meal and city agents. In the second stage, the inclusion of these foods in school menus from 2009 to 2013 was assessed. Findings – In total, 142 regional foods were identified and classified into four groups. This classification resulted in a decision tree model to identify regional foods. Approximately 45 percent of regional preparations and 82.5 percent of regional foods were offered totaling 455 preparations and 977 foods analyzed. However, 31 percent of the regional foods identified in Stage 1 were not offered in the menus analyzed. Various regional preparations lost their authenticity, possibly not being recognized because of a lack of traditional ingredients or because they contained non-regional foods that changed their character. Practical implications – The results mainly point to symbolic aspects of the production and consumption of regional foods and preparations that are important to promoting healthy diets. In addition, they can support public policies that promote the use of these foods in the school environment. Originality/value – This study analyzes the inclusion of regional foods in school meals--a topic rarely explored in the scientific literature – and proposes a decision tree model to identify regional foods with methodological rigor. This model can assist school food managers in including regional foods and developing studies related to this topic.

The Risk Assessment of Tunnels Based on Grey Correlation and Entropy Weight Method

2017 ◽  
Vol 36 (3) ◽  
pp. 1621-1631 ◽  
Author(s):  
Cheng-lu Gao ◽  
Shu-cai Li ◽  
Jing Wang ◽  
Li-ping Li ◽  
Peng Lin
Keyword(s):  
Risk Assessment ◽  
Entropy Weight ◽  
Grey Correlation ◽  
Entropy Weight Method ◽  
Weight Method

scholarly journals Prediction of Antimicrobial Activity of Synthetic Peptides by a Decision Tree Model

2013 ◽  
Vol 79 (10) ◽  
pp. 3156-3159 ◽  
Author(s):  
Felipe Lira ◽  
Pedro S. Perez ◽  
José A. Baranauskas ◽  
Sérgio R. Nozawa
Keyword(s):  
Antimicrobial Activity ◽  
Decision Tree ◽  
Antimicrobial Peptides ◽  
Synthetic Peptides ◽  
Decision Tree Model ◽  
Tree Model ◽  
Computational Tools ◽  
Content Type ◽  
Persistent Problem ◽  
Peptide Database

ABSTRACTAntimicrobial resistance is a persistent problem in the public health sphere. However, recent attempts to find effective substitutes to combat infections have been directed at identifying natural antimicrobial peptides in order to circumvent resistance to commercial antibiotics. This study describes the development of synthetic peptides with antimicrobial activity, createdin silicoby site-directed mutation modeling using wild-type peptides as scaffolds for these mutations. Fragments of antimicrobial peptides were used for modeling with molecular modeling computational tools. To analyze these peptides, a decision tree model, which indicated the action range of peptides on the types of microorganisms on which they can exercise biological activity, was created. The decision tree model was processed using physicochemistry properties from known antimicrobial peptides available at the Antimicrobial Peptide Database (APD). The two most promising peptides were synthesized, and antimicrobial assays showed inhibitory activity against Gram-positive and Gram-negative bacteria. Colossomin C and colossomin D were the most inhibitory peptides at 5 μg/ml againstStaphylococcus aureusandEscherichia coli. The methods described in this work and the results obtained are useful for the identification and development of new compounds with antimicrobial activity through the use of computational tools.

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