Credit Card Fraud Detection Using Machine Learning Algorithms

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

The rise of financial fraud, not only in India but also around the world, is a major problem. Credit card transactions have been steadily increasing in recent years, as have internet payments like Unified Payments Interface and debit card transactions, which have been increasing every day. As a result, fraud is increasing, and it has become easier for fraudsters to commit fraud. Recent research indicates the practicality of machine learning algorithms for recognizing payment fraud. Since credit cards are a common target, online fraudsters simply commit fraud because of excess use of e-commerce and other platforms, which has led to an increase in online payment methods, resulting in a greater risk of online fraud. Many researchers have started using machine learning algorithms to detect fraud. The primary objective of this study is to create a better fraud detection algorithm by analyzing payment transactional data, focusing on customers' transactional data and their payment behavioral patterns. The study proposes a framework that groups cardholders based on the volume of their transactions. Financial fraud is the biggest threat to world economies in terms of financial losses each year. The task of accurately and efficiently detecting fraud is, therefore, very challenging due to the high volume of transaction data and its complex nature. This paper presents a hybrid machine learning approach using logistic regression, decision trees, and random forests that could help enhance the accuracy and reliability of fraud detection systems. The study used an available credit card fraud dataset, with a focus on feature engineering and model evaluation to compare individual algorithms versus their ensemble. Experimental results show that the ensemble outperformed the individual classifiers in terms of precision, recall, and overall accuracy. This paper underlines the potential of hybrid machine learning techniques in improving fraud detection systems and proposes avenues for further research.

Previous articleNext article No AccessNew Biological BooksThe Mammalian Radiations: An Analysis of Trends in Evoltion, Adaptation, and Behavior. John F. Eisenberg M. B. FentonM. B. Fenton Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 57, Number 4Dec., 1982 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/412995 Copyright 1981 Stony Brook Foundation, Inc.PDF download Crossref reports no articles citing this article.

The use of large and diverse data in real time (called Big Data) affects many business processes and models. The tourism industry, characterized by manifold sub-sectors and players, provides a variety of starting points for Big Data usage. Examples are the optimization of transport offers using transaction data or a comprehensive analysis of destination trends based on social media posts. Big Data is a trending topic, however, the general discourse centres around potential ideas but fewer practical solutions. Based on a systematic literature analysis of initially 148 peer-reviewed journal articles, this article evaluates the current state of Big Data research within tourism. For this purpose, research articles centering around tourism-related Big Data were investigated according to the actual state of implementation of an IT solution, whether they truly grasp or represent Big Data in technological terms, and which added value they create for the tourism industry and research community. One key finding is that traditional data analysis is often wrongfully subsumed under the Big Data label. Further, the scientific literature predominantly discusses ideas or theoretical considerations, fewer tangible Big Data implementations, and fails to address and/or meet all requirements to be classified as Big Data. Only a minority of the presented solutions processes data in real time, whereas many rely on only one data source or structured data. Furthermore, most articles revolve around post-trip data analyses and are set to a destination context. In contrast, other tourism sectors as well as data interpretation and usage in pre-trip and on-trip phases are less represented. Lastly, this literature analysis provides an overview of true Big Data solutions already in operation and enables researchers to validly classify their own research activities in order to plan initiatives more specifically.

Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann–Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.

The exponential rise in digital transactions has made credit card fraud a critical threat to financial institutions and consumers worldwide. Traditional rule-based fraud detection systems are increasingly ineffective against sophisticated and evolving fraudulent patterns due to their inability to adapt to new tactics and handle large-scale transaction data. This paper proposes an end-to-end FraudX that classifies transactions as legitimate or fraudulent using ensemble machine learning and real-time predictive analytics. The system utilizes historical transaction data in CSV format, applies advanced preprocessing and feature engineering techniques, and employs XGBoost as the primary classifier, alongside comparative models such as Logistic Regression and Random Forest]. Experimental results on a publicly available credit card fraud dataset demonstrate that XGBoost achieves superior performance with an accuracy of 99.8%, precision of 92.5%, and recall of 88.3%. The system is implemented as a modular web application with a Flask backend and SQLite database, offering an interactive dashboard for transaction screening, detailed reporting, and administrator-led model retraining. This work provides a scalable, efficient, and reliable solution for real-time fraud detection in financial ecosystems. Keywords: Credit card fraud detection, ensemble learning, XGBoost, machine learning, Flask web application, financial security, predictive analytics, imbalanced data.

Credit card fraud in industrial goods purchasing has become so sophisticated that detection mechanisms must also be highly advanced. This research provides an incomparably successful implementation of explainable AI, improving efficiency and understanding in credit card fraud detection. We carefully analyze transactional data and solve the class imbalance problem using SMOTE and ADASYN, which have shown promise for models. Our innovation combines ML models with optimization algorithms to develop new models capable of analyzing large-scale credit card transaction data, including fraud-related patterns. Models are refined via hyperparameter optimization and evaluated using ACC, F1-score, and AUC. While retaining interpretability, the new models incorporate Deep Learning for better detection of complex fraud patterns. Among the tested approaches, the best-performing model-XGBoost+DT-SHAP with the Gini criterion under ADASYN-achieved high precision ( ≈ 89 %), the strongest F1-score ( ≈ 81 %), and recall ( ≈ 74 %), offering the most balanced performance for large-scale credit card fraud detection. This work highlights the critical role of XAI in enhancing accuracy and transparency. Our improved hybrid models can detect fraudulent activity in real time, significantly reducing losses from credit card fraud in online industrial goods transactions.

The mammalian radiations: An analysis of trends in evolution, adaptation, and behavior : By John F. Eisenberg. Chicago: University of Chicago Press, 610 pp. +xx, with 157 figures, 61 tables and 6 appendices, $45.00

Objective: This study examines the dynamics of interfaith marriage in Bangladesh, many customs and rituals, different religious society views, and marriage reviews, offering a comparative analysis of past and present trends and exploring the sociocultural aspects surrounding these unions, with specific consideration given to the influence of the Marriage Act of 1872. The objectives of the study aim to investigate the historical evolution of interfaith marriages, analyze the sociocultural factors influencing their occurrence, and assess the impact of the Marriage Act of 1872 on regulating such unions. Methods and Materials: The research methodology involves a comprehensive review of the literature, an examination of historical data conducted with a qualitative approach. Findings: The findings suggest a noticeable increase in interfaith marriages, driven by urbanization, globalization, and changing religious perspectives. Despite legal provisions, societal and cultural barriers persist, presenting challenges for interfaith couples. Conclusion: The study concludes by emphasizing the importance of promoting awareness, understanding, and acceptance of interfaith marriages in Bangladesh and proposing legal reforms and educational initiatives to foster a more inclusive and harmonious society.

The increase in greenhouse gases in the atmosphere has worsened global warming, and marked changes have been observed in meteorological and climatic events, especially since the early 2000s. Trend analysis studies are important for determining changes in meteorological and climatic events over time. This study investigated the trends of maximum precipitation and minimum temperature in the Aegean Region and Eastern Anatolia Region of Türkiye by conducting an innovative trend analysis (ITA), the Mann–Kendall (MK) test, and linear regression analysis (LRA). As a method, ITA has been used together with traditional methods in the last decade, and its advantages have been demonstrated in comparative trend studies. An important contribution of ITA is that it can categorize datasets according to their size (low, medium, and high). The classification technique of the ITA method includes dividing the sorted dataset into three equal parts and separately examining the trends of low, medium, and high data values. This approach is reasonable for datasets with low skewness (or normally distributed series). However, the normal distribution acceptance of ITA data classification is insufficient for trend analysis of data series with extreme values. Therefore, we propose a modified data classification method to rationally examine skewed datasets with the use of quartiles. Our study was performed for the trend analysis of maximum rainfall and minimum temperature data in two regions located in the west and east of Türkiye showing different climatic characteristics. In the first part of the study in which the numerical trend analysis of ITA was evaluated, the MK and LRA methods showed similar results, whereas the ITA detected trends at a greater number of stations owing to its sensitivity feature in detecting trends. In the second part, which included data classification in trend analysis, the equal split data classification used in the ITA and the modified data classification proposed in the study were compared. The comparative results of the trend analysis of the maximum rainfall and minimum temperature data showed the superiority of the proposed data classification in examining the trend of extreme values, especially for maximum rainfall data with relatively high skewness.

Topicality. In the current environment, optimizing foreign trade in the juice product market is a highly relevant issue. The war has caused significant imbalances in production, logistics, and trade, particularly due to the destruction of enterprises, disruption of supply chains, loss of traditional sales markets, and increased production costs. However, despite these challenges, the market demonstrates potential for recovery through alignment with international trends and new opportunities for cooperation. Key trends include the growing demand for organic products, the implementation of innovative technologies, and a reorientation toward European markets. At the same time, the development of trade is influenced by economic, political, and technological factors, as well as behavioral trends such as consumer demand for natural juices. Expanding distribution channels, improving production standards, and enhancing product certification will contribute to increasing Ukraine’s competitiveness in global trade. Effective monitoring and strategic planning will enable Ukrainian producers to strengthen their market positions even under conditions of uncertainty. Aim and tasks. The purpose of this study is to identify current trends and influencing factors in the development of Ukraine's foreign trade in juice products, as well as to formulate priority directions for its advancement during the wartime and post-war periods. The objectives of the study are as follows: to identify key factors affecting the competitiveness of Ukrainian producers based on the analysis of global juice market development trends; to outline priority directions for improving foreign trade in juice products, taking into account the need to adapt to changes in the global economy; and to develop recommendations for stimulating the export of Ukrainian juice products during wartime and in the post-war recovery phase. Materials and methods. The research materials include the works of domestic and foreign scholars on the development of juice product markets, as well as analytical reports from international organizations studying the agri-food sector. To conduct the analytical review and identify trends in foreign trade development, data from the State Statistics Service of Ukraine, the Ministry of Agrarian Policy and Food of Ukraine, and international trade organizations were used. The main methods employed in the study include induction, analysis and synthesis, the system-structural approach, and statistical methods. For data visualization, tabular and graphical methods were applied. Research results. The trends in the development of the juice product market in Ukraine and globally have been analyzed. It is shown that despite the challenges of war, Ukraine continues to maintain its position as one of the key exporters of apple concentrate, which remains the primary product in the juice segment. It has been identified that global juice producers are focusing on the production of organic juices, the share of which in EU and North American countries reaches 23–28%. The largest juice exporters in the world are Brazil, Spain, and the Netherlands, while Ukraine ranks among the top 20 exporters. The study revealed that the dynamics of juice exports from Ukraine are showing growth. In particular, during the first quarter of 2023, 29,000 tons of juices—mainly apple juice—were exported, totaling over USD 48 million. The main export markets include the United States, Poland, and Austria. At the same time, imported juice products from the EU, China, and Brazil continue to play a significant role in Ukraine’s domestic market, especially in the segment of exotic juices. Promising directions for development include improving production technologies, increasing the share of high value-added products, and forming an effective foreign trade policy to stimulate exports. The growing demand for organic products requires Ukrainian producers to actively adapt to global standards and trends. Conclusion. It has been established that the war has significantly altered the volumes of juice exports and imports from Ukraine, leading to a redistribution of sales markets and a decline in competitiveness in certain areas. Despite a reduction in domestic consumption caused by socio-economic transformations, the volume of Ukrainian juice exports remained stable in 2023, primarily due to continued shipments of apple juice to markets in the United States, Poland, and Austria. A global trend toward the increasing share of organic juices has been identified, requiring Ukrainian producers to adapt to international standards and trends. At the same time, the import of juice products—particularly exotic juices—remains substantial due to limited domestic production capabilities. Promising directions for further research include improving production technologies, increasing the share of high value-added products, ensuring high-quality packaging and labeling, as well as developing new logistics systems and global value chains to strengthen Ukraine’s position in international juice markets.

Sriwedari Village known as a producer of superior plant seeds and fruits in the Salaman District area. The Sriwedari Seed Shop is a form of business from the village government that sells various kinds of plant seeds and fruits from nurseries by the local community. However, in its application the sales and transaction system still uses a fairly conventional method. By opening an offline store and marketing products through social media networks, the Sriwedari Seed Shop has less regular customers. Not to mention the risk of fraud from customers who order seeds through social media is a concern for sellers Therefore, this research was conducted with the intention of answering the problems above by creating an e-marketplace system in the form of an online store that will be integrated with a payment gateway that has various online payment methods. In addition, data security will also be applied to the payment system using the Advanced Encryption Standard (AES) cryptographic algorithm which functions to encrypt payment transaction data so that data records stored in the database are not easy to read and prevent misuse of data by parties who do not have access rights. The method used in developing this system is Rapid Application Development (RAD). This method is very suitable to be applied in software development with minimal resources so that it can speed up the iteration process at each stage of development while still paying attention to input from potential system users. The results of this research are an integrated e-marketplace system with payment gateway services that have various payment methods with transaction data security using the AES cryptographic algorithm.

Surface ultraviolet (UV) radiation measurements from the Robertson‐Berger (RB) meter network and existing documentation of these data were examined to determine long‐term variations of UV. RB meter data from 14 sites in the United States were analyzed for trends over the period 1974–1991. A more in‐depth analysis of the RB meter data, including the use of supporting geophysical data, was carried out for four of the locations. Results based on analysis of data from the 14 sites show a significant negative trend of the order of −6% per decade overall, reasonably consistent with annual trends obtained by Scotto et al. [1988] using similar data for the period 1974–1985. However, when allowance is made for mean level shifts in the data for several of the stations around 1979, which may be due to calibration and other instrument‐related problems, the resulting overall trend is found to be of the order of +2% per decade and not statistically significant. An additional trend analysis using only RB meter data since 1979 at the 14 sites is also performed and leads to overall trend results similar to those from the analysis which allows for mean level shifts in the data. The more detailed analysis of data from four of the stations for the period 1979–1991 is performed to investigate the extent to which the trend behavior in the RB meter measurements can be explained by the behavior of other geophysical quantities such as cloudiness and total ozone. In particular, radiative transfer model‐based calculations of ultraviolet irradiance based on satellite data from the total ozone mapping spectrometer are compared with the RB meter measurements to help explain their behavior. Generally, inconsistencies are found between the trend behavior in RB meter measurements and radiative transfer calculations, with the RB data showing substantial downward movement relative to the calculations for three of the four sites. Significant evidence exists to indicate that problems with the network render the existing RB meter measurements unreliable for long‐term trend detection. Different reasonable treatments of the data result in dramatically different trend results. Without further information, the data, by themselves, do not allow for definitive trend analysis results.

Long-term trend analysis on total and extreme precipitation over Shasta Dam watershed

This study examines trends in adolescent health, well-being, risk behaviors, and digital media use among high school students in Finnmark, Arctic Norway. Using data from the Ungdata survey (2014-2023), trend analysis (N = 6,084 in Finnmark; N = 254,048 nationwide) revealed limited improvements in health indicators, with increases in digital media use, school-related stress, and depressive thoughts. Pairwise comparisons showed that Finnmark students had worse outcomes than the national average in most areas, except for substance use and antisocial behaviors. A deeper analysis of the 2021 Ungdata data among Finnmark students (N = 2,129), using PLS-SEM, identified peer interactions as a key driver of risk-taking behaviors, including substance use and rule-breaking. Family and school environments influenced rule-breaking but had limited effects on substance use. Digital media use was linked to drinking and rule-breaking behaviors, while experiences of bullying correlated with substance use and antisocial behaviors. Further investigation into digital and social media use (N = 2,086) revealed that digital media engagement was associated with family relationships, school connectedness, and local area satisfaction. In contrast, social media use was linked to peer dynamics, depression, and harassment. Excessive media use contributed to lower physical activity and reduced face-to-face interactions, exacerbating challenges related to geographic isolation and cultural diversity. The findings underline the need for public health interventions tailored to Finnmark's unique sociocultural context, emphasizing mental health support and addressing the role of digital and social media in adolescent development. Future studies should explore the behavioral dimensions of media use and incorporate ethnicity-related factors to gain a deeper understanding of these dynamics in the region.Key messages• Trend analysis shows limited health improvements among adolescents in Finnmark, with worse outcomes than the national average in most areas, highlighting the need to address regional disparities.• Internet use among adolescents in in Finnmark can improve social connections but is linked to increased risk-taking behaviors, substance use, and mental health issues like depression and bullying.