Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning

The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.

Multimodal Fraudulent Website Identification Method Based on Heterogeneous Model Ensemble

The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods.

The Effects of Audit Committee Attributes on Fraudulent Financial Reporting

This study aims to examine the relationship between audit committee attributes （audit committee independence, financial expertise, meeting frequency, gender diversity, and ethnic composition） and the propensity for fraudulent financial reporting. The sample includes 116 fraudulent and non-frandulent firms listed on Bursa Malaysia from 2005 to 2010. The finding of this study indicates that audit committee independence is positively associated with fraudulent financial reporting. The higher the proportion of independent or outside directors on the committee, the higher the possibility of financial fraud, and vice versa. The results also show that the expertise of members of the audit committee is negatively associated with corporate fraud. This suggests that when audit committee members are financially literate, they are more competent to curb fraudulent financial reporting. However, the findings for frequency of audit committee meetings, gender, and ethnicity show that there is no relationship between these variables and corporate fraud. The result of this study is robust after controlling for other firm-specific effects.

Forensic Auditing Techniques and Fraudulent Practices of Public Institutions in Nigeria

Public institutions are charged with the responsibility of providing essential services for the welfare of the citizens by manipulating the economy＇s financial flow through public expenditure, taxation, and so on. The reliance on public institutions to provide public services in Nigeria has resulted in disappointing results, because chief executives of the institutions take less interest in the degree of its success, and this accounts for the high level of fraudulent practices in such institutions. This study, therefore, examined the relationship between forensic auditing and fraudulent practices in Nigerian public institutions. To achieve this purpose, some hypothetical statements were made and a review of relevant literature was explored. The population of the study consisted of the general managers and accountants of 12 public institutions in Nigeria. The data generated were statistically tested with the Pearson Product-Moment Correlation Coefficient. The findings suggest that both the proactive and reactive forensic auditing techniques have a negative significant relationship with fraudulent practices in Nigerian public institutions. Based on the above, it was recommended that： （1） The Economic and Financial Crime Commission （EFCC）, the Independent Corrupt Practices Commission （ICPC）, and other anti-corruption bodies in Nigeria should have, in their payroll, internal forensic auditors to supplement the duties of the internal auditors; （2） Forensic auditors should regularly undergo training and development programs to acquaint them with relevant knowledge and skills for effective forensic auditing; and （3） Forensic auditing should be made mandatory for public institutions by regulatory authorities rather than being voluntary.

Fraudulent Financial Reporting： Theory and Evidence

This paper develops a hypothesis of the four factors from the cause of fraudulent financial reporting in terms of thoughts, culture, motive, opportunity and economic trade-off. Then tests whether independent directors monitor and improve the quality of financial report on the basis of 1 170 firms in China by means of OLS regression. The evidence is consistent with the hypothesis.

Fraudulent phone call recognition method based on convolutional neural network

With increasingly rampant telephone fraud activities,the social impact and economic losses caused to China have increased dramatically.Precise,convenient,and efficient fraudulent phone call recognition has become a challenge since telephone fraud became more varied and covert.To deal with this problem,many researchers have extracted some statistical features of telephone fraud behavior and proposed some machine learning algorithms on the field of fraudulent phone call recognition.In this paper,the calling detail records are utilized to construct a classifier for fraudulent phone call recognition.Meantime,a deep learning approach based on convolutional neural network(CNN)is proposed for better features learning and compared with the existing state-of-the-art machine learning algorithms.It learns phone number and call behavior features of telephone fraud,and improves the accuracy of classification.The evaluation results show that the proposed algorithm outperforms competitive algorithms.

NEQM （NFC Based Equipment Qualification Management） System Preventing Counterfeit and Fraudulent Item

Qualification of equipment essential to safety in NPPs （nuclear power plants） ensures its capability to perform designated safety functions on demand under postulated service conditions. However, a number of incidents identified by the NRC （Nuclear Regulatory Commission） since 1980s catalysed the US nuclear industry to adopt standard precautions to guard against counterfeit items. The purpose of this paper is to suggest the NFC （near field communication） based equipment qualification management system preventing counterfeit and fraudulent items. The NEQM （NFC based equipment qualification management） system works with the support of legacy systems such as PMS （procurement management system） and FMS （facility management system）.

Hybrid 1DCNN-Attention with Enhanced Data Preprocessing for Loan Approval Prediction

In order to reduce the risk of non-performing loans, losses, and improve the loan approval efficiency, it is necessary to establish an intelligent loan risk and approval prediction system. A hybrid deep learning model with 1DCNN-attention network and the enhanced preprocessing techniques is proposed for loan approval prediction. Our proposed model consists of the enhanced data preprocessing and stacking of multiple hybrid modules. Initially, the enhanced data preprocessing techniques using a combination of methods such as standardization, SMOTE oversampling, feature construction, recursive feature elimination (RFE), information value (IV) and principal component analysis (PCA), which not only eliminates the effects of data jitter and non-equilibrium, but also removes redundant features while improving the representation of features. Subsequently, a hybrid module that combines a 1DCNN with an attention mechanism is proposed to extract local and global spatio-temporal features. Finally, the comprehensive experiments conducted validate that the proposed model surpasses state-of-the-art baseline models across various performance metrics, including accuracy, precision, recall, F1 score, and AUC. Our proposed model helps to automate the loan approval process and provides scientific guidance to financial institutions for loan risk control.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

A Study on the Ideographic Trend of Chinese Loan Words in Translation

Based on the redefinition of Chinese loan words and corresponding types of Chinese loan words, this article makes an initiative hypothesis that nowadays the ideographic trend of Chinese loan words is being hastened to a greater degree than before.Depending on the Prototype Models Theory for the types of Chinese loan words, a comprehensive analysis of this trend is made in four aspects:the transfer from transliteration loans to loan translation;the ideographic trend of transliteration loans;the full ideograph of shift loan words;word-for-word translation of loan words.

English Loan Words in Chinese and Their Impact on Chinese

The development of China has tremendously enhanced the intercultural communication among various nations and ethnic groups. As a result, more and more words are borrowed from English into Chinese to express those western-oriented objects,concepts, values, etc. This paper focuses on English loan words and their impact on Chinese. First, their classification will be presented; then, this paper will elaborate on the main characteristics of them; at last, their lexical as well as cultural impact on Chinese are revealed. Overall, it aims at proving that English loan words help promote the development and innovation of the Chinese language and enhance the performance of different language learners thus facilitating international exchange and crosscultural communication.

骗取贷款罪中“重大损失”之理解

虽然《中华人民共和国刑法修正案(十一)》已经修改骗取贷款罪的入罪条件,然而关于重大损失的数额计算、产生时间和体系位置等问题的学理讨论尚不充分。重大损失不应包括放贷利息,其产生于贷款到期之后。将重大损失理解成骗取贷款罪客观处罚条件的观点,不符合立法修改主旨和刑法条文内在逻辑,缺乏刑事政策上的合理性。客观超过要素理论本身存在缺陷,其也不适合描述重大损失在骗取贷款罪中的体系位置。骗取贷款罪中“重大损失”与“特别重大损失”直接影响行为刑事可罚性,属于骗取贷款罪的构成要件要素。

On loan words between English and Chinese——Discussion about the openness and assimilation of the words system

This paper mainly discusses the openness and assimilation of the words system from the viewpoint of loan words between English and Chinese.Both the history of loan words and the ways of borrowing words in English and Chinese have shown us the strong abilities of languages.

A Knowledge-based System for the Analysis of the Ability of Paying back Loans

This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.

Loan growth and bank solvency:evidence from the Pakistani banking sector

Background:The dramatic loan growth and changes in the Pakistani banking system in mid-2000s have led to significant research attention on borrowers and lenders.This expansion and diversification in financial sector was driven by structural reforms,political stability and significant economic growth.Against this background,this study investigates the loan growth and risk-taking behavior of the banks during the expansionary periods of lending.Method:This study used dynamic two-step system generalized method of moment’s estimation technique,based on data taken from 32 banks in Pakistan over 2006-2014.Result:Loan growth has a significant effect on bank-specific and macroeconomicspecific variables.Loan growth in the previous year raises non-performing loans and decreases the solvency of banks with a time lag of many years.The driving force behind this phenomenon is weak prudential regulation among competitors,the asymmetric information of the borrowers,and,most importantly,that banks underestimate the risk of lending during credit booms.Conclusion:More regulatory measures are required to ensure a strong financial system when the volume of non-performing loan grows significantly.An increase in the capital requirement policy for rapidly growing banks is also needed because the problem of abnormal loan growth cannot be detected at the current time.At the same time,strong supervision is necessary to avoid the adverse consequences of borrower selection.

A Review of Rural Policy-favored Petty Loan Forest Property Certificate Mortgage

Studying petty loan forestry property mortgage mode has significant practical meaning in fund-raising and income-increasing in the forest- agriculture industry,restructuring and expanding the industry as well as setting up scientific industrial public policies. On the basis of literature analysis and field research data,this paper will classify and analyze research papers studying in forestry,property mortgage,and therefore draws conclusions on relevant concepts of petty loan forestry property mortgage,economics and property rights theory as well. At present,the studies of petty loan forestry property mortgage are more practical that are lack of an overall theoretical framework. Considering China's real situation of petty loan forestry property mortgage mode,we have investigated some foreign cases in operating forestry property mortgage systemically and some challenges and difficulties confronted by stakeholders in real cases. Therefore,we put forward that in the current period the fund-raising in forestry industry should focus on establishing and improving an overall rural petty loan forestry property mortgage mode.

Determinants of loan repayment among agricultural microcredit finance group members in Delta state, Nigeria

Background:This study examined the determinants of loan repayment among microcredit finance group members in Delta State,Nigeria.Methods:To capture the determinants of loan repayment in the study area,a total of 48 microcredit groups and 300 microcredit group members were randomly selected through a multi-stage random sampling technique.The study data was collected by questionnaire.Statistical tools such as simple descriptive statistics(table,frequency,percentage and mean)and a multiple regression analysis were used to examine the data.Results:The results indicate that females form a greater proportion of the study area microcredit group members at 70%,moreover 73%of the respondents have a household size of between 6 to 10 persons.These findings revealed that the groups had a mean 8 years of existence with a mean membership size of 13 persons.Further,an average interest rate of 40 percent per annum was charged on loans,with mean loan duration of 6 months.The regression’s result demonstrated that the determinants of the group member’s loan repayment included the group member’s age,household size,house income,and educational level,the amount of credit received,length of stay in their locality,distance to the credit source,supervision and disbursement lag.Conclusion:Therefore it was suggested that the various agricultural microcredit finance groups should carefully examine the significant determinants of loan repayment for the approach’s viability and sustainability and for optimum repayment performance.

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.

Designing of Commercial Bank Loans Risk Early Warning System Based on BP Neural Networks

According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.

Central bank digital currency,loan supply,and bank failure risk:a microeconomic approach

Central bank digital currencies(CBDCs),which are legal tenders in digital form,are expected to reduce currency issuance and circulation costs and broaden the scope of monetary policy.In addition,these currencies may also reduce consumers’need for conventional demand deposits,which,in turn,increases banks’loan provision costs because deposits require higher rates of return.We use a microeconomic banking model to investigate the effects of introducing an economy-wide,account-type CBDC on a bank’s loan supply and its failure risk.Given that a CBDC is expected to lower the cost of liquidity circulation and become a strong substitute for demand deposits,both the loan supply and the bank failure risk increase.These increases are countered by subsequent increases in the rates of return on term deposits and loans,which,in turn,reduce the loan supply and thus bank failure risk.These offsetting forces lead to no significant change in banking,as long as the rate of return on loans is below a certain threshold.However,once the rate is above the threshold,bank failure risk increases,thereby undermining banking stability.The problem is more pronounced when the degree of pass-through of funding costs to the loan rate is high and the profitability of a successful project is low.Our results imply that central banks wishing to introduce an economy-wide,account-type CBDC should first monitor yields on bank loans and consider policy measures that induce banks to maintain adequate liquidity reserve levels.