Transaction Data Research Articles

A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering

To successfully market to automotive parts customers in the Industrial Internet era, parts agents need to perform effective customer analysis and management. Dynamic customer segmentation is an effective analytical tool that helps parts agents identify different customer groups. RFM model and time series clustering algorithms are commonly used analytical methods in dynamic customer segmentation. The original RFM model suffers from the problems of R index randomness and ignoring customers’ perceived value. For most existing studies on dynamic customer segmentation, time series clustering techniques largely focus on univariate clustering, with less research on multivariate clustering. To solve the above problems, this paper proposes a dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. Firstly, this method represents each customer behavior as a time series sequence of the Length, Recency, Frequency, Monetary and Satisfaction variables. And then, we apply a multi-dimensional time series clustering algorithm based on three distance measurement methods called DTW-D, SBD, and CID to carry out customer segmentation. Finally, an empirical study and comparative analyses are conducted using customer transaction data of parts agents to verify the effectiveness of the approach. Additionally, a detailed analysis of different customer groups is made, and corresponding marketing suggestions are provided.

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.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection

High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.

Biometric based payment system

The increasing need for secure and convenient payment methods has fueled the exploration of biometric authentication systems. This paper presents a Biometric Based Payment System designed to streamline transactions while prioritizing security. The system employs a fingerprint sensor (R307) to ensure robust user identification, complemented by a 4x4 keypad for versatile input and an I2C OLED display for clear feedback. At its core, the system leverages an ESP32 microcontroller and robust SHA-256 encryption to protect sensitive information during storage and transmission. Transaction data resides in a centralized MySQL database on an Apache server, and users manage payment history through a web interface. The system emphasizes security and ease of use, with the potential for future exploration of multi-factor authentication, offline capabilities, and blockchain integration to further enhance its robustness

Blockchain transaction data mining and its applications

Blockchain transaction data mining and its applications

Toward obsolete housing: A complementary explanation of increasing coastal vulnerability

This paper explains the process of increasing coastal vulnerability in response to coastal flooding, from the property market viewpoint. Rooted in knowledge about housing submarket-based neighborhood change, this study adds new understandings of how housing obsolesces influence vulnerability following rapid land use changes, which are currently studied as a new exposure by many researchers. We employ six years of house sales transaction data (n = 1,029) in a spatial data-driven delineation with k-means, through which the submarket shift can be visualized. This approach addresses weaknesses in the existing methods to measure neighborhood change. We find that second-hand houses purchased by low-income people tend to cluster close together in newly formed submarkets in at-risk areas creating submarkets for western-inner city and distressed properties. Our data series also show that the geographical growth of the submarket for distressed properties is expanding, creating further blight in the submarket for western-inner city. Consequently, the submarket for distressed properties tends to transform into an area characterized by substandard housing conditions or slum living. Bearing in mind that the consequences of a flood will cover a wide range of issues, such as the future of public services, and the welfare of citizens in general, disaster risk management is fundamental to socio-economic development of a country. This study can enrich discussions around housing vulnerability and neighborhood change and support the current debate over spatial adaptation and climate change.

Apriori Algorithm to Predict Availability of Beauty Products

This study introduces the Apriori algorithm in beauty product availability prediction system as a solution to enhance stock prediction accuracy and mitigate inventory risks in the beauty industry. By applying data mining technology, specifically the Apriori algorithm, Kazana Kosmetik aims to gain insights into consumer purchasing patterns to optimize operations. The research analyzes transaction data to identify key buying patterns and improve stock management strategies. The results reveal seven main purchasing patterns with an average confidence value of 0.414, offering valuable guidance for Kazana Kosmetik in inventory control and marketing tactics. By leveraging data mining techniques, companies like Kazana Kosmetik can streamline sales strategies and enhance customer satisfaction. This research underscores the effectiveness of the Apriori algorithm in predicting beauty product availability and its potential to revolutionize operational efficiency in the cosmetics market.

Association Relationship Analysis in Finding Sales of Goods With Apriori Algorithm

Technology can be designed to help human life from all aspects ranging from agriculture, health science, industry and daily life. Toko Intan, a business engaged in the sale of basic and daily necessities. Every day, Toko Intan records every sales transaction in an archive stored in Microsoft Excel, containing data on goods sold every day. The purpose of this research is to find out what items are bought simultaneously by consumers to manage inventory, with the data mining method used in this research is the Association Rules method. Association Rules is one of the data mining techniques from the a priori algorithm which functions to find a combination pattern of an item. Tests carried out to process data in this study using the RapidMiner application, from tests carried out with the specified parameters, namely minimum support 30% and minimum confidence 65%, resulted in a lift ratio validation rule of 1.206. Personal Care Biscuits with 30.8% support and 90.9% confidence with a validation lift ratio of 1.206. Sales transaction data analysis can be applied well, and is able to generate a new association rule from the sales transaction dataset. With this research, it is hoped that it can provide information to the owner of the Intan store in providing the stock of goods needed by consumers and to find out the combination of item sets from the sales transaction dataset.

Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Squeezer, Apriori dan FP-Growth Pada Toko Bangunan

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

The Impact of Cloud Computing, Quantum Computing, and Blockchain on Data Security and Business Efficiency

This study explores the impact of cloud computing, quantum computing, and blockchain on data security and business efficiency. The primary objective is to qualitatively analyze the literature to understand how these emerging technologies contribute to enhancing security measures and operational efficiency within businesses. The research employs a qualitative literature review methodology, synthesizing findings from academic articles, industry reports, case studies, and empirical studies to provide a comprehensive overview of current knowledge in this field. The literature review methodology involves systematically collecting and analyzing scholarly sources that discuss various aspects of cloud computing, quantum computing, and blockchain. The study categorizes the literature into key themes, such as the benefits of cloud computing in providing scalable and flexible data storage solutions, the potential of quantum computing to revolutionize encryption and problem-solving capabilities, and the role of blockchain in ensuring transparency and immutability of data transactions. Thematic analysis is used to identify patterns and trends in how these technologies influence data security and business efficiency. The findings indicate that cloud computing enhances business efficiency by enabling seamless data access, reducing IT costs, and improving collaboration. Quantum computing holds promise for significantly advancing data security through quantum encryption, which offers higher levels of security compared to classical encryption methods. Blockchain technology ensures data integrity and transparency, reducing the risk of fraud and enhancing trust in digital transactions.

LIO-PAY: Sustainable low-cost offline payment solution

Offline payment solutions exist today to enable customers to avail themselves of the privileges of the cashless economy. Financial inclusion is visualized as a key factor in encouraging digital transactions in rural and semi-urban regions. Recently, the Government of India, along with the Reserve Bank of India, announced a framework that allows offline payments with finite transaction limits. This aims at providing access to formal financial services in an affordable manner to deepen financial inclusion. However, the remote population rarely access their digital bank accounts due to intermittent network connectivity issues. Hence, the majority of these accounts are in a dormant state, which demeans the impact of financial inclusion. The aim of this research is to design and develop a sustainable, low-cost, highly efficient offline payment solution using a LoRa-enabled IoT ecosystem to connect with Space Information Network (SIN). A novel offline payment system is designed with features of a) recharge and b) payment with offline wallet technology, which can be availed of in the absence of Internet or telecommunication services. The proposed solution, LIO-PAY, allows merchants and end users across the ecosystem to run seamless transactions. We developed a proof of concept and tested it in a lab setup with volunteers to analyze the usability of the application for initiating transactions. We examined 46 customer-initiated transactions and found that 16 (around 34.8%) failed due to merchant data entry errors, likely typos. In a separate analysis of these transactions, 4 customers (approximately 11.5%) incorrectly entered their PIN. The average time to complete a transaction was 35.42 s. Bringing satellite payments to life faces hurdles but holds immense promise for the unbanked. Getting the green light from banking authorities and the satellite network provider is no easy feat. Even if approved, launching a whole new satellite network just for low-value transactions might be too expensive. A more realistic approach could be using existing telecom satellites to bounce transaction data to banks. Regulatory approval is needed from both banking authorities and the satellite network provider. Hence, we simulated the SIN environment with seven LEO (Low Earth Orbit) satellites using the Matlab tool. These simulations revealed a transmission time range of 2 min to 1 h, indicating a reasonable timeframe for delivering information to a ground station via SIN. Despite terrestrial networks expanding their reach, satellite-based solutions hold promise for areas with no or weak internet connectivity. This becomes even more attractive if launch costs go down or if banking authorities help subsidize the service to bring financial inclusion to remote regions. Thus, we hope this approach may help researchers and policymakers visualize the possibilities of bridging the gap in financial inclusion.

Exploring the Use of Graph Neural Networks for Blockchain Transaction Analysis and Fraud Detection

The digital system is increasing day by day while various organizations are facing problems during transactions and false activities. This research is investigating fraud detection in blockchain transactions- data used to focus on Ethereum_network. To implement the layers of Graph-Convolutional Networks (GCNs) that remain in the study, they convert blockchain transactional data into a graph structure with nodes representing addresses and edges representing transactions. The methodology includes data collection with preprocessing and graph representation in the implementation of GCN layers to analyze and detect deceitful activities. The outcomes illustration of the GNN model achieves a high accuracy score and precision with recall and F1-score. The analyses effectively identify fraudulent transactions while minimizing false positives. This work demonstrates the probability of GNNs to enhance fraud detection in blockchain systems and recommends future research directions convoluted in real-time data integration and advanced neural-network architectures toward advancing the toughness and effectiveness of fraud-detection mechanisms in trendy decentralized financial ecosystems.

Implementation of The Apriori Algorithm in Managing Stock Items at Drl.Rumahan Retail

Drl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence.

DyHDGE: Dynamic heterogeneous transaction graph embedding for safety-centric fraud detection in financial scenarios

Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network. However, when dealing with different financial fraud scenarios, existing methods face challenges, resulting in difficulty in effectively ensuring financial security. In fraud scenarios, transaction data are generated in real time, in which a strong temporal relationship between multiple fraudulent transactions is observed. Traditional dynamic graph models struggle to effectively balance the temporal features of nodes and spatial structural features, failing to handle different types of nodes in the graph network. In this study, to extract the temporal and structural information, we proposed a dynamic heterogeneous transaction graph embedding (DyHDGE) network based on a dynamic heterogeneous transaction graph, considering both temporal and structural information while incorporating heterogeneous data. To separately extract temporal relationships between transactions and spatial structural relationships between nodes, we used a heterogeneous temporal graph representation learning module and a temporal graph structure information extraction module. Additionally, we designed two loss functions to optimize node feature representations. Extensive experiments demonstrated that the proposed DyHDGE significantly outperformed previous state-of-the-art methods on two simulated datasets of financial fraud scenarios. This capability contributes to enhancing security in financial consumption scenarios.

A framework for modelling customer invoice payment predictions

By offering clients attractive credit terms on sales, a company may increase its turnover, but granting credit also incurs the cost of money tied up in accounts receivable (AR), increased administration and a heightened probability of incurring bad debt. The management of credit sales, although eminently important to any business, is often performed manually, which may be time-consuming, expensive and inaccurate. Such an administrative workload becomes increasingly cumbersome as the number of credit sales increases. As a result, a new approach towards proactively identifying invoices from AR accounts that are likely to be paid late, or not at all, has recently been proposed in the literature, with the aim of employing intervention strategies more effectively. Several computational techniques from the credit scoring literature and particularly techniques from the realms of survival analysis or machine learning have been embedded in the aforementioned approach. This body of work is, however, lacking due to the limited guidance provided during the data preparation phase of the model development process and because survival analytic and machine learning techniques have not yet been ensembled. In this paper, we propose a generic framework for modelling invoice payment predictions with the aim of facilitating the process of preparing transaction data for analysis, generating relevant features from past customer behaviours, and selecting and ensembling suitable models for predicting the time to payment associated with invoices. We also introduce a new sequential ensembling approach, called the Survival Boost algorithm. The rationale behind this method is that features generated by a survival analytic model can enhance the efficacy of a machine learning classification algorithm.

Can Property Taxation Fix China’s Housing Market?: A Regression Discontinuity Analysis

How does the housing market respond to a newly imposed value-based property tax? Based on individual property transaction data spanning seven years, we investigate the repercussions of a new property tax on the local housing market in Chongqing, China. In particular, we adopt a fuzzy regression discontinuity approach to examine the impact of the tax policy on house prices, transaction volumes, and the broader social welfare. We find that the property tax in Chongqing has not caused a reduction in house prices, but it has made a non-trivial contribution to local public finance for public housing programs. Our findings suggest that the property-tax-capitalization hypothesis is not supported in Chongqing’s context, although the tax improves wealth redistribution.

Mobility on demand in the United States – Current state of integration and policy considerations for improved interoperability

The concept of Mobility on Demand (MOD) aims to create a safe and convenient ecosystem for transportation and goods delivery. Despite offering various services, the integration and seamless data exchange among different modes of transportation remain challenging. To address this, research was conducted to examine the current state of integration in the MOD ecosystem, identify gaps in data exchange, and propose policy considerations. The research highlighted the limited integration and inconsistent data structures in the United States (U.S.), attributed to the absence of policy directives and high costs of traditional standards development. Industry-led initiatives are driving progress in standards and specifications. Deep linking and deep integration were distinguished, emphasizing the importance of a truly user-centric MOD solution. Emerging mobility models leveraging blockchain technology show promise in facilitating efficient data sharing and collaboration among various operators. Transportation policy makers play a crucial role in supporting open-source standards and integration efforts. An initial framework utilizing open-source efforts such as Transport Operator Mobility-as-a-service Provider (TOMP), Mobility Data Specification (MDS), General On-demand Feed Specification (GOFS), Transactional Data Specification (TDS), Operational Data Standard (ODS), and City Data Standard for Mobility (CDS-M) was proposed to drive the advancement of scalable and user-centric MOD systems.

Impact of connected dispensing technology with advanced analytics in a multicenter health system.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This study was designed to evaluate the impact of enterprise inventory optimization (EIO) technology and analytics on pharmacy labor, costs, and medication availability within a large integrated delivery network (IDN). This article describes a mixed-methods, postmarket observational study assessing the impact of a solution of disparate technologies including automated dispensing cabinets (ADCs), centralized pharmacy inventory software, and controlled substance vaults connected by an inventory optimization analytics (IOA) tool. Four study modules were implemented over a 10-month period. The intervention consisted of implementation of the IOA software, linking the disparate automated technologies. Transactional data was collected and aggregated with user perception survey data in both the pre- and postintervention periods. Descriptive and comparative statistical testing was used to assess outcomes. A total of 11 facilities with bed counts ranging between 22 and 908 beds were included in this study. At an enterprise level, users were able to complete an average of 2.8 times more periodic automated replenishment (PAR) level changes post intervention, resulting in an estimated enterprise labor avoidance of over 1 full-time equivalent (2,099 labor hours) annually. Despite an enterprise decision to increase ADC inventory on hand from a 3-day supply to a 5-day supply, 5 sites (45%) had a decrease in total inventory, while 9 sites (82%) saw a decrease in ADC inventory costs. Additionally, 7 sites (64%) saw a reduction in the ADC stockout percentage and all 11 sites (100%) saw a decrease in the central pharmacy stockout percentage post intervention. Integration and optimization of connected inventory management technology was observed to have positive impacts on improving labor productivity, reducing ADC inventory carrying costs, and increasing medication availability.

The Right to Data Portability as a Personal Right

The right to the portability of personal data guarantees the interested party the right to receive personal data that concern themselves. Specifically, data which a person has provided to a ‘data collector’ in a structured format can currently be transmitted to another ‘data collector’ without any legal consequences as long as the original ‘collector’ has received consent either derived via a contract or other means. This data transaction from one ‘collector’ to another is often carried out by automated means; it is easily technically possible and is therefore considered to not negatively affect the rights and freedoms of others. This right to data transfer is guaranteed when it comes to data collected in the public interest or in the exercise of public powers conferred on the ‘collector’. The main precedent to the right to data portability is the EU Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016, regarding the protection of natural persons with respect to the processing of personal data and their free circulation, which repeals Directive 95/46/EC (General Data Protection Regulation). In Article 20, this regulation discusses the right to data portability, establishing the basic guidelines for this right. Thus, a new personal right is guaranteed, given the context of the general protection of data. In order to strengthen control over their own data, a natural person must also be allowed to receive data in the same structured way.