Transactional Behavior Research Articles

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.

To profit or not to profit: Founder identity at the intersection of religion and entrepreneurship

For more than a century, discussion of the connections between religion and entrepreneurship has pointed to what we would now label questions of identity. Our study of 25 participants in a program in Northern Kenya that aimed to introduce and stimulate capitalist entrepreneurship within extremely poor pastoralist communities shows that differences in participants' religious social identities strongly shaped whether or not they adopted new roles and role identities as capitalist entrepreneurs. This process also shifted the domains in which their religious and collectivist social identities were salient and helped to explain the emergence of important and contested changes in social and economic relations. We contribute to the development of founder identity theory by building research at the intersection of entrepreneurship and religion and at the intersection of entrepreneurship and poverty alleviation.

Systemic Importance and Risk Characteristics of Banks Based on a Multi-Layer Financial Network Analysis.

Domestic and international risk shocks have greatly increased the demand for systemic risk management in China. This paper estimates China's multi-layer financial network based on multiple financial relationships among banks, assets, and firms, using China's banking system data in 2021. An improved PageRank algorithm is proposed to identify systemically important banks and other economic sectors, and a stress test is conducted. This study finds that China's multi-layer financial network is sparse, and the distribution of transactions across financial markets is uneven. Regulatory authorities should support economic recovery and adjust the money supply, while banks should differentiate competition and manage risks better. Based on the PageRank index, this paper assesses the systemic importance of large commercial banks from the perspective of network structure, emphasizing the role of banks' transaction behavior and market participation. Different industries and asset classes are also assessed, suggesting that increased attention should be paid to industry risks and regulatory oversight of bank investments. Finally, stress tests confirm that the improved PageRank algorithm is applicable within the multi-layer financial network, reinforcing the need for prudential supervision of the banking system and revealing that the degree of transaction concentration will affect the systemic importance of financial institutions.

A Research on the Dual Value Model of Individual Transaction Decision in Ethnic Chinese Society

Abstract In ethnic Chinese society, interpersonal relationships significantly influence transaction decisions, contrasting with Western economic rationality which often neglects the role of "human relationships". This research introduces a Dual Value Model (DVM) to explore how Chinese individuals navigate these complex interpersonal dynamics in transaction decisions. It highlights the integration of Potential Social Value (PSV), stemming from the counterparty's social status (vertical relationship) and emotional closeness (horizontal relationship), with Potential Economic Value (PEV), focusing on the economic outcomes of the transaction. These two potential values are defined through expectancy theory to facilitate communal comparison. This duality necessitates a balance, demonstrating a unique economic-social tradeoff where individuals might accept lower economic benefits in favor of enhancing or maintaining favorable “Guanxi.” The concept of "guanxi mark-down (GMD)" and "Compensatory Mark-up (CMU)" are introduced as strategies employed to steer this balance, reflecting the social etiquette and economic pragmatism within Chinese transactional behavior. The research aims to provide a nuanced understanding of Chinese transaction decision-making, offering insights for guiding interpersonal relationships and economic considerations in a socio-cultural context deeply influenced by Confucian principles. Keywords: Transaction decision, Dual value model (DVM), Potential social value (PSV), Potential economic value (PEV), Guanxi; Expectancy theory.

DeFiTrust: A transformer-based framework for scam DeFi token detection using event logs and sentiment analysis

Decentralized finance (DeFi) token scams are rising, leading to significant losses for investors and users. Hence, it is vital to identify malicious DeFi tokens accurately and early to protect investor funds. Current Machine Learning models that detect scam tokens rely entirely on DeFi token transactional behavior and have overlooked the public opinion that can be derived from news and social media posts, which can provide helpful information for identifying scam tokens. Further, existing frameworks to detect scam tokens fail to explain why certain tokens are identified as scams, hindering stakeholder confidence in such systems. This paper proposes a new transformer-based framework called “DeFiTrust” to identify malicious DeFi tokens using transaction events and social media posts. DeFiTrust has two components: event log processing and sentiment analysis. The former creates a feature vector representing temporal variations in the event logs of a token. The latter analyzes social media posts to determine public opinion about the token. A fully-connected neural network then processes both pieces of information to determine whether the token is legitimate or a scam. We evaluated the proposed model against existing Machine Learning-based models using real-world data. The results demonstrate that DeFiTrust outperforms existing approaches and detects scam tokens earlier. Explanations generated using Integrated Gradients reveal that DeFiTrust identifies scam tokens based on valid reasons, enhancing stakeholder trust in the proposed model.

The privacy preserving auction mechanisms in IoT-based trading market: A survey

Nowadays, the Internet of Things (IoT) technology has greatly promoted the transaction behavior. It has led to a shift from the traditional face-to-face, bartering business to today’s online virtual goods business, such as energy trading, spectrum, and so on. However, there exist two critical challenges in IoT-based trading market, which include “efficiency” and “safety”. “Efficiency” indicates a fair-trading decision-making rule, and “safety” indicates that the bidders’ privacy must be protected. To address the above challenges, numerous researchers are working on privacy-preserving auction mechanisms for IoT-based transactional markets, drawing insights from electronic auction theory and information security theory. However, a predominant focus on specific scenarios characterizes most existing works, lacking a comprehensive synthesis of the collective contributions to date. Therefore, this paper systematically elucidates privacy-preserving auction mechanisms for IoT-based transactional markets. Firstly, we expound the foundational knowledge of IoT-based auction markets, delving into auction theory and privacy theory through concise categorization. Secondly, we address both theoretical considerations (from the integration perspective of various privacy protection methods and auction mechanisms) and practical aspects (evaluating various real-world application scenarios). Each aspect undergoes meticulous scrutiny, providing practical assessments and prospects. Lastly, we propose future research directions. Key challenges include the absence of inference attack models for differential privacy, a dearth of algorithmic designs with privacy-preserving capabilities at the auction mechanism level, and the intricate balance between privacy and efficiency. Proposed solutions for future research directions include leveraging Bayesian inference and neural networks for effective attacks, designing autonomous privacy protection mechanisms, and addressing the privacy-efficiency trade-off through the application of Markov decision processes.

An Abnormal Account Identification Method by Topology Feature Analysis for Blockchain-Based Transaction Network

Cryptocurrency, as one of the most successful applications of blockchain technology, has played a vital role in promoting the development of the digital economy. However, its anonymity, large scale of cryptographic transactions, and decentralization have also brought new challenges in identifying abnormal accounts and preventing abnormal transaction behaviors, such as money laundering, extortion, and market manipulation. Recently, some researchers have proposed efficient and accurate abnormal transaction detection based on machine learning. However, in reality, abnormal accounts and transactions are far less common than normal accounts and transactions, so it is difficult for the previous methods to detect abnormal accounts by training with such an imbalance in abnormal/normal accounts. To address the issues, in this paper, we propose a method for identifying abnormal accounts using topology analysis of cryptographic transactions. We consider the accounts and transactions in the blockchain as graph nodes and edges. Since the abnormal accounts may have special topology features, we extract topology features from the transaction graph. By analyzing the topology features of transactions, we discover that the high-dimensional sparse topology features can be compressed by using the singular value decomposition method for feature dimension reduction. Subsequently, we use the generative adversarial network to generate samples like abnormal accounts, which will be sent to the training dataset to produce an equilibrium of abnormal/normal accounts. Finally, we utilize several machine learning techniques to detect abnormal accounts in the blockchain. Our experimental results demonstrate that our method significantly improves the accuracy and recall rate for detecting abnormal accounts in blockchain compared with the state-of-the-art methods.

Spatio-Temporal Network-based Bank Transactional Behavior Analysis to Detect Suspicious Activities

Money laundering refers to the act of disguising the proceeds of illegal activities as legitimate funds. This is a significant problem as it enables criminals to profit from illegal activities and finance further criminal endeavors. Money laundering is also linked to other crimes, such as drug trafficking, terrorism financing, and corruption. To combat money laundering, governments and financial institutions have implemented various measures, such as Know Your Customer (KYC) regulations, Anti-Money Laundering (AML) laws, and the use of financial intelligence units. However, the existing money laundering system is complex, making it difficult to detect and prevent money laundering activities. Many current systems rely on outdated technology and manual processes, which can be time-consuming and prone to error. Therefore, there is a need for effective detection and prevention systems that can identify suspicious transactions and patterns of behavior. This project aims to prevent and detect money laundering activities by identifying suspicious transactions and monitoring the movement of funds through the financial system. The proposed system uses Long Short-Term Memory (LSTM) to detect and prevent money laundering activities. By analyzing the data in a time-series format, LSTM can identify unusual patterns of transactions and flag them for further investigation. LSTM can also predict future trends in financial data, allowing for the detection and prevention of potential money laundering activities before they occur. The proposed transactional network and behavior analysis system provides a more efficient and accurate method for identifying potential money laundering activities. This system can ultimately lead to a more effective and efficient anti-money laundering system. Keywords: Anti-Money laundering, Long short-term memory, Legitimate funds, Transaction network, behavior analysis, Time series format

Learning Transactional Behavioral Representations for Credit Card Fraud Detection.

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus, automatically detect a fraudulent transaction. We propose a novel model by improving long short-term memory with a time-aware gate that can capture the behavioral changes caused by consecutive transactions of users. A current-historical attention module is designed to build up connections between current and historical transactional behaviors, which enables the model to capture behavioral periodicity. An interaction module is designed to learn comprehensive and rational behavioral representations. To validate the effectiveness of the learned behavioral representations, experiments are conducted on a large real-world transaction dataset provided to us by a financial company in China, as well as a public dataset. Experimental results and the visualization of the learned representations illustrate that our method delivers a clear distinction between legitimate behaviors and fraudulent ones, and achieves better fraud detection performance compared with the state-of-the-art methods.

UBRMTC: User Behavior Recognition Model With Transaction Character

Behavior analysis has been used widely in antifraud transactions. However, existing methods of behavior analysis mainly focus on behavior patterns and do not fully consider the behavior psychology of users in the transaction process, which affects the precise recognition of fraudulent behaviors. It is difficult to recognize fraudulent transactions precisely how to describe the user’s behavioral psychology and integrate the behavioral psychology into the transaction behavior. Thus, this article first proposes the transaction character model based on the user’s cautiousness to reflect the user’s behavioral psychology. This model is built from the user’s historical normal interaction behavior data. Then, a user behavior benchmark is established to reflect the user’s behavior pattern from the user’s historical normal transaction behavior data. To integrate the user’s transaction character and user behavior, the mapping relationship model is built by using the least-squares generalized inverse method. This model is the core of the fraudulent behavior recognition method with transaction characters. Experiments in fraud detection scenarios show that the new method improved the average recognition performance of four fraud detection indicators (recall rate, precision rate, accuracy rate, and F1 value) by 23%. The method also shows that individual psychological character has a great influence on user behavior.

Senior Consumers’ Use of Digital Supporters and their Digital Transaction Behavior in the Mobile Environment

Senior Consumers’ Use of Digital Supporters and their Digital Transaction Behavior in the Mobile Environment