Search results for: Credit Card Fraud Detection
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3910

Search results for: Credit Card Fraud Detection

3910 Advanced Machine Learning Algorithm for Credit Card Fraud Detection

Authors: Manpreet Kaur

Abstract:

When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

Procedia PDF Downloads 65
3909 Efficient Credit Card Fraud Detection Based on Multiple ML Algorithms

Authors: Neha Ahirwar

Abstract:

In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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3908 A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection

Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

Abstract:

With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

Procedia PDF Downloads 77
3907 Self-Organizing Maps for Credit Card Fraud Detection

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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3906 Self-Organizing Maps for Credit Card Fraud Detection and Visualization

Authors: Peng, Chun-Yi, Chen, Wei-Hsuan, Ueng, Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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3905 Fraud Detection in Credit Cards with Machine Learning

Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

Abstract:

Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

Procedia PDF Downloads 105
3904 Profit-Based Artificial Neural Network (ANN) Trained by Migrating Birds Optimization: A Case Study in Credit Card Fraud Detection

Authors: Ashkan Zakaryazad, Ekrem Duman

Abstract:

A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

Procedia PDF Downloads 434
3903 Credit Card Fraud Detection with Ensemble Model: A Meta-Heuristic Approach

Authors: Gong Zhilin, Jing Yang, Jian Yin

Abstract:

The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 88
3902 An Investigation into Fraud Detection in Financial Reporting Using Sugeno Fuzzy Classification

Authors: Mohammad Sarchami, Mohsen Zeinalkhani

Abstract:

Always, financial reporting system faces some problems to win public ear. The increase in the number of fraud and representation, often combined with the bankruptcy of large companies, has raised concerns about the quality of financial statements. So, investors, legislators, managers, and auditors have focused on significant fraud detection or prevention in financial statements. This article aims to investigate the Sugeno fuzzy classification to consider fraud detection in financial reporting of accepted firms by Tehran stock exchange. The hypothesis is: Sugeno fuzzy classification may detect fraud in financial reporting by financial ratio. Hypothesis was tested using Matlab software. Accuracy average was 81/80 in Sugeno fuzzy classification; so the hypothesis was confirmed.

Keywords: fraud, financial reporting, Sugeno fuzzy classification, firm

Procedia PDF Downloads 206
3901 Government Credit Card in State Financial Management: Public Sector Innovation in Indonesia

Authors: Paramita Nur Kurniati, Stanislaus Riyanta

Abstract:

In the midst of the heightened usage of electronic money (e-money), Indonesian government expenditure is yet governed through cash-basis transactions. This conventional system brings about a number of potential risks and obstacles to operational conduct, including state financial liquidity issue. Consequently, Ministry of Finance is currently establishing the cashless payment methods for State Budget (APBN). Included in those advance methods is credit card facility as a government expenditure payment scheme. This policy is one of the innovations within the public sector learned from other countries’ best practices. Moreover, this particular method is already prominent within the private-sector realm. Qualitative descriptive analysis technique is implemented to evaluate the contemporary innovation of using government credit card in the path towards cashless society. This approach is expected to generate several benefits for the government, particularly in minimizing corruption within the state financial management. Effective coordination among policy makers and policy implementers is essential for the success of this policy’s exercise, without neglecting prudence and public transparency aspects. Government credit card usage shall be the potent resolution for enhancing the government’s overall public service performance.

Keywords: cashless basis, cashless society, government credit card, public sector innovation

Procedia PDF Downloads 114
3900 Automatic Teller Machine System Security by Using Mobile SMS Code

Authors: Husnain Mushtaq, Mary Anjum, Muhammad Aleem

Abstract:

The main objective of this paper is used to develop a high security in Automatic Teller Machine (ATM). In these system bankers will collect the mobile numbers from the customers and then provide a code on their mobile number. In most country existing ATM machine use the magnetic card reader. The customer is identifying by inserting an ATM card with magnetic card that hold unique information such as card number and some security limitations. By entering a personal identification number, first the customer is authenticated then will access bank account in order to make cash withdraw or other services provided by the bank. Cases of card fraud are another problem once the user’s bank card is missing and the password is stolen, or simply steal a customer’s card & PIN the criminal will draw all cash in very short time, which will being great financial losses in customer, this type of fraud has increase worldwide. So to resolve this problem we are going to provide the solution using “Mobile SMS code” and ATM “PIN code” in order to improve the verify the security of customers using ATM system and confidence in the banking area.

Keywords: PIN, inquiry, biometric, magnetic strip, iris recognition, face recognition

Procedia PDF Downloads 326
3899 Harnessing Artificial Intelligence and Machine Learning for Advanced Fraud Detection and Prevention

Authors: Avinash Malladhi

Abstract:

Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

Procedia PDF Downloads 49
3898 The Determinants of Customer’s Purchase Intention of Islamic Credit Card: Evidence from Pakistan

Authors: Nasir Mehmood, Muhammad Yar Khan, Anam Javeed

Abstract:

This study aims to scrutinize the dynamics which tend to impact customer’s purchasing intention of Islamic credit card and nexus of product’s knowledge and religiosity with the attitude of potential Islamic credit card’s customer. The theory of reasoned action strengthened the idea that intentions due to its proven predictive power are most likely to instigate intended consumer behavior. Particularly, the study examines the relationships of perceived financial cost (PFC), subjective norms (SN), and attitude (ATT) with the intention to purchase Islamic credit cards. Using a convenience sampling approach, data have been collected from 450 customers of banks located in Rawalpindi and Islamabad. A five-point Likert scale self-administered questionnaire was used to collect the data. The data were analyzed using the Statistical Package of Social Sciences (SPSS) through the procedures of principal component and multiple regression analysis. The results suggested that customer’s religiosity and product knowledge are strong indicators of attitude towards buying Islamic credit cards. Likewise, subjective norms, attitude, and perceived financial cost have a significant positive impact on customers’ purchase intent of Islamic bank’s credit cards. This study models a useful path for future researchers to further investigate the underlined phenomenon along with a variety of psychodynamic factors which are still in its infancy, at least in the Pakistani banking sector. The study also provides an insight to the practitioners and Islamic bank managers for directing their efforts toward educating customers regarding the use of Islamic credit cards and other financial products.

Keywords: attitude, Islamic credit card, religiosity, subjective norms

Procedia PDF Downloads 90
3897 The Underground Ecosystem of Credit Card Frauds

Authors: Abhinav Singh

Abstract:

Point Of Sale (POS) malwares have been stealing the limelight this year. They have been the elemental factor in some of the biggest breaches uncovered in past couple of years. Some of them include • Target: A Retail Giant reported close to 40 million credit card data being stolen • Home Depot : A home product Retailer reported breach of close to 50 million credit records • Kmart: A US retailer recently announced breach of 800 thousand credit card details. Alone in 2014, there have been reports of over 15 major breaches of payment systems around the globe. Memory scrapping malwares infecting the point of sale devices have been the lethal weapon used in these attacks. These malwares are capable of reading the payment information from the payment device memory before they are being encrypted. Later on these malwares send the stolen details to its parent server. These malwares are capable of recording all the critical payment information like the card number, security number, owner etc. All these information are delivered in raw format. This Talk will cover the aspects of what happens after these details have been sent to the malware authors. The entire ecosystem of credit card frauds can be broadly classified into these three steps: • Purchase of raw details and dumps • Converting them to plastic cash/cards • Shop! Shop! Shop! The focus of this talk will be on the above mentioned points and how they form an organized network of cyber-crime. The first step involves buying and selling of the stolen details. The key point to emphasize are : • How is this raw information been sold in the underground market • The buyer and seller anatomy • Building your shopping cart and preferences • The importance of reputation and vouches • Customer support and replace/refunds These are some of the key points that will be discussed. But the story doesn’t end here. As of now the buyer only has the raw card information. How will this raw information be converted to plastic cash? Now comes in picture the second part of this underground economy where-in these raw details are converted into actual cards. There are well organized services running underground that can help you in converting these details into plastic cards. We will discuss about this technique in detail. At last, the final step involves shopping with the stolen cards. The cards generated with the stolen details can be easily used to swipe-and-pay for purchased goods at different retail shops. Usually these purchases are of expensive items that have good resale value. Apart from using the cards at stores, there are underground services that lets you deliver online orders to their dummy addresses. Once the package is received it will be delivered to the original buyer. These services charge based on the value of item that is being delivered. The overall underground ecosystem of credit card fraud works in a bulletproof way and it involves people working in close groups and making heavy profits. This is a brief summary of what I plan to present at the talk. I have done an extensive research and have collected good deal of material to present as samples. Some of them include: • List of underground forums • Credit card dumps • IRC chats among these groups • Personal chat with big card sellers • Inside view of these forum owners. The talk will be concluded by throwing light on how these breaches are being tracked during investigation. How are credit card breaches tracked down and what steps can financial institutions can build an incidence response over it.

Keywords: POS mawalre, credit card frauds, enterprise security, underground ecosystem

Procedia PDF Downloads 399
3896 Practical Limitations of the Fraud Triangle Framework in Fraud Prevention

Authors: Alexander Glebovskiy

Abstract:

Practitioners charged with fraud prevention and investigation strongly rely on the Fraud Triangle framework developed by Joseph T. Wells in 1997 while analyzing the causes of fraud at business organizations. The Fraud Triangle model explains fraud by elements such as pressure, opportunity, and rationalization. This view is not fully suitable for effective fraud prevention as the Fraud Triangle model provides limited insight into the causation of fraud. Fraud is a multifaceted phenomenon, the contextual factors of which may not fit into any framework. Employee criminal behavior in business organizations is influenced by environmental, individual, and organizational aspects. Therefore, further criminogenic factors and processes facilitating fraud in organizational settings need to be considered in the root-cause analysis: organizational culture, leadership style, groupthink effect, isomorphic behavior, crime of obedience, displacement of responsibility, lack of critical thinking and unquestioning conformity and loyalty.

Keywords: criminogenesis, fraud triangle, fraud prevention, organizational culture

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3895 Modulation of the Europay, MasterCard, and VisaCard Authentications by Using Avispa Tool

Authors: Ossama Al-Maliki

Abstract:

The Europay, MasterCard, and Visa (EMV) is the transaction protocol for most of the world and especially in Europe and the UK. EMV protocol consists of three main stages which are: card authentication, cardholder verification methods, and transaction authorization. This paper details in full the EMV card authentications. We have used AVISPA and SPAN tools to do our modulization for the EMV card authentications. The code for each type of the card authentication was written by using CAS+ language. The results showed that our modulations were successfully addressed all the steps of the EMV card authentications and the entire process of the EMV card authentication are secured. Also, our modulations were successfully addressed all the main goals behind the EMV card authentications according to the EMV specifications.

Keywords: EMV, card authentication, contactless card, SDA, DDA, CDA AVISPA

Procedia PDF Downloads 140
3894 Financial Statement Fraud: The Need for a Paradigm Shift to Forensic Accounting

Authors: Ifedapo Francis Awolowo

Abstract:

The unrelenting series of embarrassing audit failures should stimulate a paradigm shift in accounting. And in this age of information revolution, there is need for a constant improvement on the products or services one offers to the market in order to be relevant. This study explores the perceptions of external auditors, forensic accountants and accounting academics on whether a paradigm shift to forensic accounting can reduce financial statement frauds. Through Neo-empiricism/inductive analytical approach, findings reveal that a paradigm shift to forensic accounting might be the right step in the right direction in order to increase the chances of fraud prevention and detection in the financial statement. This research has implication on accounting education on the need to incorporate forensic accounting into present day accounting curriculum. Accounting professional bodies, accounting standard setters and accounting firms all have roles to play in incorporating forensic accounting education into accounting curriculum. Particularly, there is need to alter the ISA 240 to make the prevention and detection of frauds the responsibilities of bot those charged with the management and governance of companies and statutory auditors.

Keywords: financial statement fraud, forensic accounting, fraud prevention and detection, auditing, audit expectation gap, corporate governance

Procedia PDF Downloads 317
3893 Optimize Data Evaluation Metrics for Fraud Detection Using Machine Learning

Authors: Jennifer Leach, Umashanger Thayasivam

Abstract:

The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

Procedia PDF Downloads 148
3892 Impact of Internal Control on Fraud Detection and Prevention: A Survey of Selected Organisations in Nigeria

Authors: Amos Olusola Akinola

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The aim of this study is to evaluate the internal control system on fraud prevention in Nigerian business organizations. A survey research was undertaken in five organizations from the banking and manufacturing sectors in Nigeria using the simple random sampling technique and primary data was obtained with the aid structured questionnaire drawn on five likert’s scale. Four Hypotheses were formulated and tested using the T-test Statistics, Correlation and Regression Analysis at 95% confidence interval. It was discovered that internal control has a significant positive relationship with fraud prevention and that a weak internal control system permits fraudulent activities among staff. Based on the findings, it was recommended that organizations should continually and methodically review and evaluate the components of its internal control system whether activities are working as planned or not and that every organization should have pre-determined guidelines for conducting its operations and ensures compliance with these set guidelines while proactive steps should be taken to establish the independence of the internal audit by making the audit reportable to the governing council of an organization and not the chief executive officer.

Keywords: internal control, internal system, internal audit, fraud prevention, fraud detection

Procedia PDF Downloads 341
3891 Detecting Model Financial Statement Fraud by Auditor Industry Specialization with Fraud Triangle Analysis

Authors: Reskino Resky

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This research purposes to create a model to detecting financial statement fraud. This research examines the variable of fraud triangle and auditor industry specialization with financial statement fraud. This research used sample of company which is listed in Indonesian Stock Exchange that have sanctions and cases by Financial Services Authority in 2011-2013. The number of company that were became in this research were 30 fraud company and 30 non-fraud company. The method of determining the sample is by using purposive sampling method with judgement sampling, while the data processing methods used by researcher are mann-whitney u and discriminants analysis. This research have two from five variable that can be process with discriminant analysis. The result shows the financial targets can be detect financial statement fraud, while financial stability can’t be detect financial statement fraud.

Keywords: fraud triangle analysis, financial targets, financial stability, auditor industry specialization, financial statement fraud

Procedia PDF Downloads 426
3890 Email Phishing Detection Using Natural Language Processing and Convolutional Neural Network

Authors: M. Hilani, B. Nassih

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Phishing is one of the oldest and best known scams on the Internet. It can be defined as any type of telecommunications fraud that uses social engineering tricks to obtain confidential data from its victims. It’s a cybercrime aimed at stealing your sensitive information. Phishing is generally done via private email, so scammers impersonate large companies or other trusted entities to encourage victims to voluntarily provide information such as login credentials or, worse yet, credit card numbers. The COVID-19 theme is used by cybercriminals in multiple malicious campaigns like phishing. In this environment, messaging filtering solutions have become essential to protect devices that will now be used outside of the secure perimeter. Despite constantly updating methods to avoid these cyberattacks, the end result is currently insufficient. Many researchers are looking for optimal solutions to filter phishing emails, but we still need good results. In this work, we concentrated on solving the problem of detecting phishing emails using the different steps of NLP preprocessing, and we proposed and trained a model using one-dimensional CNN. Our study results show that our model obtained an accuracy of 99.99%, which demonstrates how well our model is working.

Keywords: phishing, e-mail, NLP preprocessing, CNN, e-mail filtering

Procedia PDF Downloads 68
3889 A Product-Specific/Unobservable Approach to Segmentation for a Value Expressive Credit Card Service

Authors: Manfred F. Maute, Olga Naumenko, Raymond T. Kong

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Using data from a nationally representative financial panel of Canadian households, this study develops a psychographic segmentation of the customers of a value-expressive credit card service and tests for effects on relational response differences. The variety of segments elicited by agglomerative and k means clustering and the familiar profiles of individual clusters suggest that the face validity of the psychographic segmentation was quite high. Segmentation had a significant effect on customer satisfaction and relationship depth. However, when socio-demographic characteristics like household size and income were accounted for in the psychographic segmentation, the effect on relational response differences was magnified threefold. Implications for the segmentation of financial services markets are considered.

Keywords: customer satisfaction, financial services, psychographics, response differences, segmentation

Procedia PDF Downloads 295
3888 Computer Fraud from the Perspective of Iran's Law and International Documents

Authors: Babak Pourghahramani

Abstract:

One of the modern crimes against property and ownership in the cyber-space is the computer fraud. Despite being modern, the aforementioned crime has its roots in the principles of religious jurisprudence. In some cases, this crime is compatible with the traditional regulations and that is when the computer is considered as a crime commitment device and also some computer frauds that take place in the context of electronic exchanges are considered as crime based on the E-commerce Law (approved in 2003) but the aforementioned regulations are flawed and until recent years there was no comprehensive law in this regard; yet after some years the Computer Crime Act was approved in 2009/26/5 and partly solved the problem of legal vacuum. The present study intends to investigate the computer fraud according to Iran's Computer Crime Act and by taking into consideration the international documents.

Keywords: fraud, cyber fraud, computer fraud, classic fraud, computer crime

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3887 Civil Liability for Digital Crimes

Authors: Pál Mészáros

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The aim of this research topic is to examine civil law consequences caused by crimes committed in the digital space. During the commission of certain crimes, not only the rights of one person are violated, but also the rights of an entire institution, for example, if the information system of a university is attacked. The consequences of these crimes committed in the digital space may also be that the victim himself is liable to other third parties, for example, in the event that health data comes into the possession of unauthorized persons, and it can be proved that the service provider's IT system was inadequate. An interesting question may also be the civil liability of credit institutions if someone becomes a victim of fraud but is not expected from him/her to notice the fraud. In such a case, the liability of the credit institution may arise if they do not respond in time in the case of unauthorized bank transactions. Based on the above, the main topic of the research is the civil liability of the victim, or another person or company related to the victim in the case of damages caused by crimes.

Keywords: civil liability, digital crimes, transfer of responsibility, civil law

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3886 The Application of Fuzzy Set Theory to Mobile Internet Advertisement Fraud Detection

Authors: Jinming Ma, Tianbing Xia, Janusz Getta

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This paper presents the application of fuzzy set theory to implement of mobile advertisement anti-fraud systems. Mobile anti-fraud is a method aiming to identify mobile advertisement fraudsters. One of the main problems of mobile anti-fraud is the lack of evidence to prove a user to be a fraudster. In this paper, we implement an application by using fuzzy set theory to demonstrate how to detect cheaters. The advantage of our method is that the hardship in detecting fraudsters in small data samples has been avoided. We achieved this by giving each user a suspicious degree showing how likely the user is cheating and decide whether a group of users (like all users of a certain APP) together to be fraudsters according to the average suspicious degree. This makes the process more accurate as the data of a single user is too small to be predictable.

Keywords: mobile internet, advertisement, anti-fraud, fuzzy set theory

Procedia PDF Downloads 136
3885 Use of Multistage Transition Regression Models for Credit Card Income Prediction

Authors: Denys Osipenko, Jonathan Crook

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Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models.

Keywords: multinomial regression, conditional logistic regression, credit account state, transition probability

Procedia PDF Downloads 447
3884 A Study of Management Principles Incorporating Corporate Governance and Advocating Ethics to Reduce Fraud at a South African Bank

Authors: Roshan Jelal, Charles Mbohwa

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In today’s world, internal fraud remains one of the most challenging problems within companies worldwide and despite investment in controls and attention given to the problem, the instances of internal fraud has not abated. To the contrary it appears that internal fraud is on the rise especially in the wake of the economic downturn. Leadership within companies believes that the more sophisticated the controls employed the less likely it would be for employees to pilfer. This is a very antiquated view as investment in controls may not be enough to curtail internal fraud; however, ensuring that a company drives the correct culture and behaviour within the organisation is likely to yield desired results. This research aims to understand how creating a strong ethical culture and embedding the principle of good corporate governance impacts on levels of internal fraud with an organization (a South African Bank).

Keywords: internal fraud, corporate governance, ethics, reserve bank, the King Code

Procedia PDF Downloads 365
3883 An Exploration of Why Insider Fraud Is the Biggest Threat to Your Business

Authors: Claire Norman-Maillet

Abstract:

Insider fraud, otherwise known as occupational, employee, or internal fraud, is a financial crime threat. Perpetrated by defrauding (or attempting to defraud) one’s current, prospective, or past employer, an ‘employee’ covers anyone employed by the company, including board members and contractors. The Coronavirus pandemic has forced insider fraud into the spotlight, and it isn’t dimming. As the focus of most academics and practitioners has historically been on that of ‘external fraud’, insider fraud is often overlooked or not considered to be a real threat. However, since COVID-19 changed the working world, pushing most of us into remote or hybrid working, employers cannot easily keep an eye on what their staff are doing, which has led to reliance on trust and transparency. This, therefore, brings about an increased risk of insider fraud perpetration. The objective of this paper is to explore why insider fraud is, therefore, now the biggest threat to a business. To achieve the research objective, participating individuals within the financial crime sector (either as a practitioner or consultants) attended semi-structured interviews with the researcher. The principal recruitment strategy for these individuals was via the researcher’s LinkedIn network. The main findings in the research suggest that insider fraud has been ignored and rejected as a threat to a business, owing to a reluctance to admit that a colleague may perpetrate. A positive of the Coronavirus pandemic is that it has forced insider fraud into a more prominent position and giving it more importance on a business’ agenda and risk register. Despite insider fraud always having been a possibility (and therefore a risk) within any business, it is very rare that a business has given it the attention it requires until now, if at all. The research concludes that insider fraud needs to prioritised by all businesses, and even ahead of external fraud. The research also provides advice on how a business can add new or enhance existing controls to mitigate the risk.

Keywords: insider fraud, occupational fraud, COVID-19, COVID, coronavirus, pandemic, internal fraud, financial crime, economic crime

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3882 E-Hailing Taxi Industry Management Mode Innovation Based on the Credit Evaluation

Authors: Yuan-lin Liu, Ye Li, Tian Xia

Abstract:

There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library.

Keywords: credit, mobile internet, e-hailing taxi, management mode, weighted cluster

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3881 Customer Experiences and Perspectives on Mobile Money Service Fraud: A Case Study of the University of Education, Winneba

Authors: Mavis Ofosuah Asante, Abena Abokoma Asemanyi, Belinda Osei-mensah, Stephen Osei Akyiaw

Abstract:

The study examined mobile money service fraud experiences and perspectives on control practices at University of Education, Winneba. The objectives of the study included to examine the forms of MoMo fraud strategies experienced by customers of MoMo on UEW Campus, to examine and classify the main perpetrators of the MoMo fraud among UEW students as well as the framework for fraud detection put together by the Telco’s and consumers on UEW Campus. The study adopted the case study research design. The purposive sampling technique was used to select the UEW Campus. Using the convenience sampling technique, five respondents were sampled for the study. The outcome of the in-depth interviews conducted revealed Mobile money fraud was committed in various forms, such as anonymous calls and text messages from scammers, fraudsters calling to deceive subscribers that they are to deliver goods from abroad or from a close relative under false pretexts. Finally, fraudsters sending false cash-out messages to merchants for authorization of which the physical cash is issued by the merchant to the fraudster without the equivalent e-cash. Mobile money fraud has been perpetuated in diverse forms such as mobile money network systems fraud, false promotion fraud, and reversal of erroneous transactions, fortuitous scams, and mobile money agents' fraud. Finally, the frameworks that have been used to detect mobile money fraud include the display of national identifies cards for the transaction, digital identification systems, the use of firewall to protect mobile money accounts, effective information technology architecture for mobile money services, reporting of mobile money fraud to telecoms and the sanctioning of mobile money fraudsters. The study suggested there should be public education and awareness creation on the activities of mobile money fraudsters in Ghana by telecommunication companies in conjunction with the National Communications Authority and the Bank of Ghana. The study, therefore, concluded that the menace of mobile money fraud threatens the integrity of the mobile money financial services.

Keywords: mobile money, fraud, telecommunication, merchant

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