Transactional Behavior Research Articles

FRAUD DETECTION IN FINANCIAL TRANSACTONS

Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Fraud has been a persistent challenge in the realm of financial transactions, posing significant threats to businesses, financial institutions, and individuals alike. As technology advances and financial systems become increasingly digitalized, the methods and sophistication of fraudulent activities also evolve. This paper explores cutting edge technologies such as machine learning and are revolutionizing fraud detection. By analyzing transaction patterns and employing anomaly detection algorithms, such as CNN organizations can identify and mitigate fraudulent activities in real time. In response to this ongoing threat, the field of fraud detection in financial transactions has emerged as a critical area of focus for organizations worldwide. In order to identify fraudulent behavior in financial transactions, this research paper suggests a unique technique. Key challenges include handling imbalanced datasets where fraudulent transactions are rare compared to legitimate ones, ensuring the privacy and security of sensitive financial information, and maintaining low latency to prevent delays in transaction processing.

Retraction Note: Navigating Criminal Responsibility in the Digital Marketplace: Implications of Network-Neutral Help Behavior and Beyond-5G Networks in E-Commerce Transactions

Retraction Note: Navigating Criminal Responsibility in the Digital Marketplace: Implications of Network-Neutral Help Behavior and Beyond-5G Networks in E-Commerce Transactions

The Likelihood of Consumer Transaction Behavior Patterns in Social Commerce

The Likelihood of Consumer Transaction Behavior Patterns in Social Commerce

Behavioral analytics in digital payments: A conceptual analysis of anti-money laundering techniques

The rise of digital payments has revolutionized financial transactions, yet it has also introduced significant challenges related to Anti-Money Laundering (AML) efforts. This paper provides a conceptual analysis of the role of behavioral analytics in enhancing AML techniques within digital payment systems. The primary objective is to explore how behavioral analytics can be employed to identify patterns of fraudulent activities, detect suspicious behavior, and mitigate risks associated with money laundering. By examining various behavioral analytics methodologies, such as machine learning, data mining, and anomaly detection, the study highlights their effectiveness in real-time monitoring and decision-making for AML compliance. Key findings suggest that behavioral analytics offers a more nuanced approach to AML by focusing on transactional behaviors rather than solely relying on static rules-based systems. This dynamic method not only improves detection accuracy but also reduces false positives, enhancing overall operational efficiency. The paper concludes that integrating behavioral analytics into AML frameworks is essential for financial institutions to stay ahead of evolving money laundering tactics, ensuring a proactive and adaptive approach to safeguarding digital payment ecosystems. The analysis underscores the need for continuous innovation in AML strategies, with behavioral analytics playing a central role in future-proofing digital payment security.

EVALUATING THE IMPACT OF MULTI FACTOR AUTHENTICATION ON CYBERSECURITY EFFECTIVENESS

In today's digital age, the convenience of online financial transactions is accompanied by rising cybersecurity risks. The shift to digital platforms for conducting daily financial activities has significantly exposed customers to cyber threats. These threats often result from unauthorized access, phishing attacks, and fraud attempts by cybercriminals. Ensuring the security of these transactions is crucial to maintain users' trust and to protect sensitive financial data. This study proposes a comprehensive security framework to strengthen the safety of online financial transactions, using a combination of multi-factor authentication (MFA) and a machine learning-based fraud detection system, termed "E-cyberboost." The framework operates on two primary layers, each designed to address a different aspect of security: identity verification and real-time fraud detection. The first layer involves multi-factor authentication, where users must verify their identity. This layer typically combines something the user knows (like a password) with something they have (such as a one-time password or OTP sent to their mobile device) or something they are (such as fingerprint or facial recognition). MFA significantly reduces unauthorized access by ensuring that even if one authentication factor is compromised, additional verification is required to gain access. The second layer introduces an advanced machine-learning component. Named "E-cyberboost," this machine learning model actively monitors ongoing transactions, detecting patterns that may indicate fraudulent activity. If the system identifies any unusual or suspicious transaction behaviour, it immediately triggers a security response, such as additional user verification or temporarily blocking the transaction. This proactive approach allows the system to address potential fraud in real time, adapting to evolving threats by learning from previous incidents and continuously improving its detection accuracy. By combining the preventive measures of MFA with the responsive capabilities of machine learning, this framework provides robust security against both common and sophisticated cyber threats. The study discusses the methodology, implementation, and effectiveness of this layered approach in reducing the risk of unauthorized transactions and protecting users' financial information. The proposed framework aims to enhance trust in online financial platforms and ensure a safer digital transaction experience for users. Index Terms—Multifactor authentication, machine learning, fraud detection, E-cyberboost

A BTN-Based Method for Multi-Entity Bitcoin Transaction Analysis and Influence Assessment

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the Mt.Gox coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

Default Prediction for Wholesale and Retail Small and Medium-Sized Enterprises Using Loan Transaction and Market Evaluation Data

ABSTRACT Irregular loan transaction behaviors and poor market situation indicate potential risk for wholesale and retail small and medium-sized enterprises (SMEs) in loan use and operation, but the value of these risk information is rarely explored and utilized. This paper innovatively proposed to mine such information to solve the difficulty of default prediction for wholesale and retail SMEs and enrich the credit risk research of special groups. The experimental results showed that tracking loan transaction and market evaluation data can improve the accuracy and recall of the models by an average of 1.38% and 7.66%, respectively, and identify default samples 233.22 days in advance on average, which can provide a new perspective for financial institutions to predict the default risk.

Designing Strategies for Optimal ATM Placement Location Using Geospatial Methods and Consumer Transaction Behavior

Designing Strategies for Optimal ATM Placement Location Using Geospatial Methods and Consumer Transaction Behavior

Influence of perceived risk on consumers’ intention and behavior in cross-border e-commerce transactions: A case study of the Tmall Global platform

With the rapid development of economic integration and information technology, cross-border e-commerce has become the most important form of e-commerce. However, there are currently many uncertainties in cross-border online shopping, and the low physical proximity exacerbates the psychological distance, perceived risk, and lack of trust, all of which affect consumer decision-making to a considerable extent. This study adopts both quantitative and qualitative methods. A questionnaire survey was conducted among consumers using the Tmall Global platform in Chinese-speaking (433 individuals) and non-Chinese-speaking (379 individuals) regions, and structural equation modeling was used for data analysis. We also conducted a focus group discussion to interpret the quantitative results and provide recommendations for platform improvements. The results show that perceived risk has a negative effect on consumers’ intentions and behaviors to purchase and recommend the platform. Perceived risk can reduce the level of trust, whereas trust and intention can promote consumer behavior.

Bayesian optimization driven strategy for detecting credit card fraud with Extremely Randomized Trees

Credit card usage has surged, heightening concerns about fraud. To address this, advanced credit card fraud detection (CCFD) technology employs machine learning algorithms to analyze transaction behavior. Credit card data's complexity and imbalance can cause overfitting in conventional models. We propose a Bayesian-optimized Extremely Randomized Trees via Tree-structured Parzen Estimator (TP-ERT) to detect fraudulent transactions. TP-ERT uses higher randomness in split points and feature selection to capture diverse transaction patterns, improving model generalization. The performance of the model is assessed using real-world credit card transaction data. Experimental results demonstrate the superiority of TP-ERT over the other CCFD systems. Furthermore, our validation exhibits the effectiveness of TPE compared to other optimization techniques with higher F1 score.•The optimized Extremely Randomized Trees model is a viable artificial intelligence tool for detecting credit card fraud.•Model hyperparameter tuning is conducted using Tree-structured Parzen Estimator, a Bayesian optimization strategy, to efficiently explore the hyperparameter space and identify the best combination of hyperparameters. This facilitates the model to capture intricate patterns in the transactions, resulting in enhanced model performance.•The empirical findings exhibit that the proposed approach is superior to the other machine learning models on a real-world credit card transaction dataset.

Predicting patent transaction behaviour based on embedded features of knowledge search networks

Predicting patent transaction behaviour based on embedded features of knowledge search networks

Understanding Consumer Decision-Making in Real Estate: An Integrative Analysis Using the Theory of Planned Behavior

This paper explores consumer decision-making in the real estate market through the lens of the Theory of Planned Behavior (TPB), providing an integrative analysis that enhances our understanding of the psychological and social factors influencing property purchases. By examining the roles of attitudes, subjective norms, and perceived behavioral control, the study demonstrates how these components interact to shape consumer intentions and behaviors in real estate transactions. The findings suggest that TPB offers a robust framework for predicting consumer behavior and designing effective marketing strategies that align with consumer needs. The study also highlights the broader implications for real estate professionals and policymakers, advocating for the adoption of TPB as a guiding model to improve customer engagement and decision-making processes in the evolving real estate industry.

Network Based Feature Extraction Method for Fraud Detection Using Label Propagation

Abstract: Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, it will result in higher misjudgment for some users. Aiming at the above problems, this paper proposes an individual behavior transaction detection method based on hypersphere model. In this model, considering multiple dimensions of normal historical transaction records, the characteristics of user’s transaction behavior is generated with the trend of transaction. Then, the user optimal risk threshold algorithm is proposed to determine the optimal risk threshold for each user. Finally combining the transaction behavior and the optimal risk threshold, the user behavior benchmark is formed, which is used to construct the multidimensional hypersphere model. On this basis, a mapping method for transforming transaction detection into midpoint in multidimensional space is proposed. The experiment proves that the proposed method is superior to other models, and it is found that the characterization effect of user behavior is related to the frequency of users’ transactions. Applied computing → Secure online transactions; Digital cash; Computing methodologies → Instance-based learning; Rule learning

Predictive analytics for financial compliance: Machine learning concepts for fraudulent transaction identification

Predictive analytics has emerged as a pivotal tool in financial compliance, offering sophisticated methods for identifying fraudulent transactions through the application of machine learning (ML) concepts. As financial institutions grapple with increasingly complex fraud schemes and stringent regulatory requirements, the integration of predictive analytics with ML provides a proactive approach to fraud detection and prevention. Machine learning algorithms excel in analyzing vast datasets, identifying hidden patterns, and making real-time predictions. In the realm of financial compliance, supervised learning models such as logistic regression, decision trees, and random forests are commonly used to classify transactions as legitimate or fraudulent. These models are trained on historical transaction data, learning to recognize the subtle indicators of fraud by identifying correlations between various features and fraudulent outcomes. This allows for high-accuracy predictions on new, unseen data. Unsupervised learning techniques, such as clustering and anomaly detection, are equally critical in predictive analytics for financial compliance. These methods do not require labeled data and are adept at uncovering novel fraud patterns by detecting outliers and irregularities that deviate from normal transactional behavior. Anomaly detection algorithms, including k-means clustering and isolation forests, can identify transactions that exhibit unusual characteristics, flagging them for further investigation. The integration of predictive analytics with real-time data processing capabilities enhances the agility of fraud detection systems. Streaming analytics and real-time scoring enable the continuous monitoring of transactions, ensuring that suspicious activities are identified and addressed promptly. This real-time aspect is crucial for minimizing the impact of fraudulent transactions and ensuring compliance with regulatory standards. Despite the advancements, implementing predictive analytics for financial compliance involves challenges such as ensuring data quality, addressing privacy concerns, and maintaining model transparency. Financial institutions must navigate these challenges by employing robust data governance practices, leveraging secure data processing techniques, and adopting explainable AI models that provide insights into their decision-making processes. In conclusion, predictive analytics, powered by machine learning concepts, offers a robust framework for identifying fraudulent transactions and enhancing financial compliance. By leveraging advanced ML algorithms and real-time data processing, financial institutions can proactively detect and prevent fraud, thereby safeguarding their operations and ensuring adherence to regulatory mandates. This approach not only mitigates financial losses but also strengthens the overall integrity and trustworthiness of the financial system.

Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.

Skip-Gram and Transformer Model for Session-Based Recommendation

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user’s past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model’s success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%.

A Novel Financial Anti-fraud Method based on Machine Learning Algorithms

Digital finance is booming, financial technology is maturing, and the development of information technology has had a massively positive impact on society. However, such progress also introduces a new type of risk: the underground network industry is experiencing explosive growth, and telecommunications network fraud has caused enormous financial losses. In the age of digital finance, although commercial banks have ushered in new possibilities and created momentum, they must also confront new obstacles and needs for digital transformation. In this context, online financial services have emerged as the new primary battleground. In this study, based on the RFM high-dimensional derived features and machine learning techniques, a high-dimensional transaction behavior portraits-based anti-fraud machine learning model is created. Using big data, stream computing, and other technologies, as well as systematic deployment, application strategy, and iterative model optimization, we developed a set of machine learning-based in-event risk control solutions. Confirmed to have an AUC of 0.972, this model provides key insight into fraud risk, identifies fraudulent transactions in milliseconds, and has value and relevance for enhancing the in-transaction risk management capabilities of online digital financial organizations.

E-commerce Platform in Digital Economy: Analysis and Optimization of Amazon's Footwear Store

With the continuous deepening of globalization, cross-border e-commerce has become an important part of global trade. Especially driven by digital payment technology, network technology, industrial upgrading and policy support, China's cross-border e-commerce industry has developed rapidly. This research aims to analyze the sales data of men's footwear on the Amazon platform, understand market trends, user behavior patterns, and product characteristics, and then provide data-supported marketing strategies and operational decisions for e-commerce platforms. We have adopted a variety of data analysis techniques, including data cleaning, ARIMA model prediction, price band analysis, repeat purchase rate analysis, user profiling (RFM model), sentiment analysis and cluster analysis. Through comprehensive analysis of multi-dimensional information such as store sales data, product attributes, market hot words, and user evaluations, market dynamics and user preferences are revealed. We have comprehensively applied a variety of data analysis models and techniques, such as using the ARIMA model for sales prediction, the LDA model for sentiment analysis, and the RFM model combined with cluster analysis to construct a refined user profile. Through in-depth analysis of user transaction behaviors and product attributes, accurate marketing strategies can be provided for e-commerce platforms, enhance user experience, increase user stickiness, and thus achieve business growth and improvement of profitability. At the same time, the research also points out the importance of brand building, supply chain management, risk assessment and other aspects, providing strategic suggestions for the long-term stable development of cross-border e-commerce.

A Critical Analysis of Machine Learning Techniques for Online Transaction Security

The widespread adoption of Unified Payments Interface (UPI) as the preferred digital transaction method has led to a sharp increase in fraudulent activities, presenting a considerable challenge to the security and reliability of online payments.Top of Form To address this pressing concern, our project focuses on developing a sophisticated fraud detection system tailored specifically for UPI transactions. We utilize Convolutional Neural Networks (CNNs), an advanced deep learning architecture, to intricately capture the sequence of operations involved in UPI transaction processing. Through extensive data collection and preprocessing, we curate a diverse dataset comprising various transaction types, amounts, and temporal patterns, ensuring comprehensive coverage of transaction behaviour. The CNN is meticulously trained on this dataset, leveraging its innate ability to discern subtle patterns and dependencies within transaction sequences. Notably, our approach emphasizes the importance of minimizing false positives to prevent genuine transactions from being erroneously flagged as fraudulent. In real-time, incoming UPI transactions are subjected to scrutiny by the trained CNN, which evaluates their conformity to learned normal behaviour patterns. Transactions exhibiting deviations indicative of potential fraud are promptly flagged for further investigation, thus enabling proactive mitigation of fraudulent activities. Most importantly, our study contains a comprehensive comparative analysis that compares the efficacy of conventional machine learning methods such as Support Vector Machines (SVM), Random Forests, and Logistic Regression with CNN-based fraud detection. Through meticulous evaluation, we demonstrate the superior efficacy of CNNs in detecting fraudulent UPI transactions, owing to their inherent capacity to capture intricate spatial and temporal dependencies. Our findings underscore the transformative potential of deep learning techniques in bolstering the security and resilience of digital payment ecosystems, thereby safeguarding users and fostering trust in online transactions.

Analysis and mining of user behavior on e-commerce platforms based on decision trees and RFM models

User transaction behavior has driven the development of e-commerce. This article reveals the key role of the development of the e-commerce industry and user behavior in driving it through in-depth research on e-commerce sales data. Firstly, by grouping and summarizing user IDs through Pandas, a comprehensive user consumption situation was obtained, including consumption frequency, amount, etc., and user consumption behavior was visualized through visual means. On the basis of constructing effective features, Matplotlib was used to depict the number of users who receive coupons every week and day, providing a basis for the establishment of subsequent coupon distribution prediction models. By splitting the payment dates, an RFM model was constructed to analyze the purchase time period, frequency, consumption amount, and the proportion of each user's time behavior, forming a detailed user profile. Finally, the decision tree model is applied to predict the probability of coupon usage.