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Nowadays, credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.

Nowadays, credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.

With the rapid expansion of online financial transactions, detecting fraudulent activity has become a significant concern. This study investigates the integration of scalable artificial intelligence AI technologies into fraud detection systems with a focus on maintaining a seamless and user friendly experience. By employing real time monitoring, intuitive alert systems, and machine learning algorithms, platforms can identify anomalous behaviors while minimizing disruption to users. The research emphasizes the need to balance robust security with usability, ensuring that fraud detection measures do not compromise transaction speed or user satisfaction. Additionally, the paper explores challenges such as alert fatigue, integration complexity, and privacy concerns, proposing solutions including adaptive learning models, blockchain integration, and collaborative frameworks with cybersecurity experts. The findings underscore the importance of designing fraud detection frameworks that are both scalable and responsive to evolving threats, without sacrificing the user experience. Dilip Kumar | Yashwant Kumar "Fraud Detection in Online Transactions: Enhancing User Experience with Scalable AI Solutions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd81165.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/81165/fraud-detection-in-online-transactions-enhancing-user-experience-with-scalable-ai-solutions/dilip-kumar

Priestcraft in perfection: or, a detection of the fraud of inserting and continuing this clause ... 1710.. Digitized from IA40310714-53 . Previous issue: bim_eighteenth-century_the-sacramental-part-of-_waterland-daniel_1739 . Next issue: bim_eighteenth-century_priestcraft-distinguish_dennis-john_1715 .

Nowadays the companies are growing around the world and a lot of data is also processing daily. This data helps the companies for future business related purposes for this they will store the data. Is the data is stolen the company will affects it. In this paper, we are discussing credit card fraud detection. Credit card fraud detection is of two types mainly first is through online and second is through the physical card. By stealing the information related to the credit card they can fraud large amounts of money transfer or a large amount of purchase before the cardholder finds out. For detecting the frauds, the companies are using many machine learning techniques for finding transactions that are fraudulent or not. This paper is a combined approach of genetic algorithm and random forest the genetic algorithm is used for feature selection and in the random forest, we used random forest classifiers by splitting the training and testing set. The combination of both gives good results then alone. M. Bhavana Lakshmi Priya | Dr. Jitendra Jaiswal "Credit Card Fraud Detection using a Combined Approach of Genetic Algorithm and Random Forest" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31774.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31774/credit-card-fraud-detection-using-a-combined-approach-of-genetic-algorithm-and-random-forest/m-bhavana-lakshmi-priya

Bibliography: p. 250-253

The potential for fraud increases as digital payments replace traditional paper methods.

The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new and innovative techniques for fraud detection. Their primary techniques for fraud detection are the data mining tools of classification trees and neural networks as well as methods for pooling the results of multiple model fits. In this thesis a new data mining methodology, Multiple Additive Regression Trees (MART) is applied to the problem of detecting potential fraudulent and suspect transactions (those with conditions needing improvement - CNI's). The new MART methodology is an automated method for pooling a \"forest\" of hundreds of classification trees. This study shows how MART can be applied to fraud data. In particular it shows how MART identified classes of important variables and that MART is as effective with raw input variables as it is with the categorical variables currently constructed individually by DFAS. MART is also used to explore the effects of the substantial amount of missing data in the historical fraud database. In general MART is as accurate as existing methods, requires much less effort to implement saving many man-days, handles missing values in a sensible and transparent way, and provides features such as identifying more important variables.

Intense global and local technological augment and diminishing trade barriers are making life difficult for businesses. At a time when customers have revolutionary expectations, sophisticated decision system modeling is pivotal to an organization's business strategy. This era is e-payment focused, and emphasizes enhanced operational efficiencies. Other necessary attributes needed to compete successfully include facilitating customer services and satisfaction, risk management, decision support systems, major breakdown of trade barriers, enhanced security and privacy, market forces, better technology, communication etc. Electronic payment system is essential for proper functioning of the electronic market on the Internet. The significance of electronic payment system is enhanced by the effective use of credit card system at the electronic market online. However, the time has come to break down the psychological, culture and traditional barriers and adapt the value-added component of the secure electronic payment tool. Neural Network (NN) is a revolutionary changing process for online business trading and should be adopted for safety and security of one’s own business, and to join the global bandwagon of technical trends.

Intense global and local technological augment and diminishing trade barriers are making life difficult for businesses. At a time when customers have revolutionary expectations, sophisticated decision system modeling is pivotal to an organization's business strategy. This era is e-payment focused, and emphasizes enhanced operational efficiencies. Other necessary attributes needed to compete successfully include facilitating customer services and satisfaction, risk management, decision support systems, major breakdown of trade barriers, enhanced security and privacy, market forces, better technology, communication etc. Electronic payment system is essential for proper functioning of the electronic market on the Internet. The significance of electronic payment system is enhanced by the effective use of credit card system at the electronic market online. However, the time has come to break down the psychological, culture and traditional barriers and adapt the value-added component of the secure electronic payment tool. Neural Network (NN) is a revolutionary changing process for online business trading and should be adopted for safety and security of one’s own business, and to join the global bandwagon of technical trends.

Intense global and local technological augment and diminishing trade barriers are making life difficult for businesses. At a time when customers have revolutionary expectations, sophisticated decision system modeling is pivotal to an organization's business strategy. This era is e-payment focused, and emphasizes enhanced operational efficiencies. Other necessary attributes needed to compete successfully include facilitating customer services and satisfaction, risk management, decision support systems, major breakdown of trade barriers, enhanced security and privacy, market forces, better technology, communication etc. Electronic payment system is essential for proper functioning of the electronic market on the Internet. The significance of electronic payment system is enhanced by the effective use of credit card system at the electronic market online. However, the time has come to break down the psychological, culture and traditional barriers and adapt the value-added component of the secure electronic payment tool. Neural Network (NN) is a revolutionary changing process for online business trading and should be adopted for safety and security of one’s own business, and to join the global bandwagon of technical trends.

Intense global and local technological augment and diminishing trade barriers are making life difficult for businesses. At a time when customers have revolutionary expectations, sophisticated decision system modeling is pivotal to an organization's business strategy. This era is e-payment focused, and emphasizes enhanced operational efficiencies. Other necessary attributes needed to compete successfully include facilitating customer services and satisfaction, risk management, decision support systems, major breakdown of trade barriers, enhanced security and privacy, market forces, better technology, communication etc. Electronic payment system is essential for proper functioning of the electronic market on the Internet. The significance of electronic payment system is enhanced by the effective use of credit card system at the electronic market online. However, the time has come to break down the psychological, culture and traditional barriers and adapt the value-added component of the secure electronic payment tool. Neural Network (NN) is a revolutionary changing process for online business trading and should be adopted for safety and security of one’s own business, and to join the global bandwagon of technical trends.

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Fraud Detection is a major concern these days because of digitalization. We are totally dependent on online transactions these days for even very small needs. There is no doubt that online transactions have made our life very easy but it has increased risk on other hand. And this risk can be very harmful one day. Confidential data is being stolen by the different apps and it is sold in international market. Which later on comes to us in totally different and very harmful way. So why not to use technology again to stop these risks and flaws. Various ML techniques has been observed by researchers but Auto ML is yet not discovered on a wider platform. Therefore, this paper at first aims to explore the trending technology Auto ML. Then a model for evaluating Auto ML is suggested and analysed with different classification algorithms. The experimental results ascertained the accuracy of Auto ML followed by a comparative analysis of ML and Auto ML.

Ranking fraud in the mobile App business propose to fraud exercises which have an inspiration self-motivated, bringing up the Apps up in the prevalent rundown. By and by days, number of shady means are used more much of the time by application developers, such expanding their Apps' business or posting fraud App appraisals, to give situating mutilation. There is a confined research for abstaining from ranking fraud. This paper gives a whole thought of situating double dealing and distinguishes the Ranking fraud unmistakable framework for mobile Apps. This work is gathering into three groupings. At first is web ranking spam detection, second is online review spam acknowledgment and last one is mobile application suggestion. The Web ranking spam incorporates to any ponder activities which pass on to choose Web pages a ridiculous ideal pertinence or centrality. Review spam is planned to give out of line perspective of a couple of items keeping in mind the end goal to affect the clients' perspective of the items by particularly or in a roundabout way influeating or harming the item's notoriety. In propose framework we additionally expel the fake reviews from the dataset utilizing comparability measure algorithm and after that identify the web rank spam. The trial result demonstrates that propose framework spare the time and additionally memory than the current framework. G. Mutyalamma | K. Komali | G. Pushpa"Ranking and Fraud Review Detection for Mobile Apps using KNN Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8232.pdf  http://www.ijtsrd.com/computer-science/data-miining/8232/ranking-and-fraud-review-detection-for--mobile-apps-using-knn-algorithm/g-mutyalamma

Ongoing loan fraud is a source of concern for financial institutions, as it has a direct financial impact and also scares off customers. This pattern, which can be traced to the development of modern technology, the introduction of novel ideas, and the quickening pace of international connections, makes the detection of fraud an expensive endeavour. This article proposes a novel framework for enhancing the fraud detection of loan banking using data mining algorithms. The framework extracts a number of predictive analysis techniques for identifying loan fraud. Several methods employing a wide range of pipeline architectures have been tried in order to select the optimal champion model. Autotuning has also been used to find the best possible setting for the model’s hyperparameters. The results of the evaluation show that autoencoder with gradient boosting outperformed the other classification algorithms with an accuracy of 98.62%. The proposed framework has the potential to significantly improve the fraud detection process of loan banking, which can ultimately lead to better faster fraud detects rates by combining data mining techniques with dimensionality reduction strategies in the feature space.

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on e platforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.

Investigation to quantify the likelihoods of outcomes of the 3-D Secure Fraud Detection backend, based on interventions on changing transaction value, machine data or the region.

convenience theory; financial crime; risk; crime prevention; fraud; organised crime; police investigation; social security; Shadow Economy; detection; investigation; convictions; prosecution; tip of the iceberg; police resources; crime detection theory

convenience theory; financial crime; risk; crime prevention; fraud; organised crime; police investigation; social security; Shadow Economy; detection; investigation; convictions; prosecution; tip of the iceberg; police resources; crime detection theory

Government Publishing Office U.S. Congress House of Representatives Committee on Agriculture HEARING TO REVIEW UPDATES ON USDA INSPECTOR GENERAL AUDITS, INCLUDING SNAP FRAUD DETECTION EFFORTS AND IT COMPLIANCE Date(s) Held: 2011-12-01 112th Congress, 1st Session GPO Document Source: CHRG-112hhrg71686 Superintendents of Documents ID: Y 4.AG 8/1 Witnesses: Fong, Hon. Phyllis K., Inspector General, U.S. Department of Agriculture, Washington, D.C.; accompanied by Karen Ellis, Assistant Inspector General for Investigations, OIG, USDA; Gil Harden, Assistant Inspector General for Audit, OIG, USDA Related Items: Congressional Serial No. 112-27

Government Publishing Office U.S. Congress House of Representatives Committee on Agriculture HEARING TO REVIEW UPDATES ON USDA INSPECTOR GENERAL AUDITS, INCLUDING SNAP FRAUD DETECTION EFFORTS AND IT COMPLIANCE Date(s) Held: 2011-12-01 112th Congress, 1st Session GPO Document Source: CHRG-112hhrg71686 Superintendents of Documents ID: Y 4.AG 8/1 Witnesses: Fong, Hon. Phyllis K., Inspector General, U.S. Department of Agriculture, Washington, D.C.; accompanied by Karen Ellis, Assistant Inspector General for Investigations, OIG, USDA; Gil Harden, Assistant Inspector General for Audit, OIG, USDA Related Items: Congressional Serial No. 112-27

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. This research project aims to develop a reliable credit card fraud detection system through a stacking ensemble method, integrating LSTM and Random Forest machine learning techniques. This approach aims to enhance fraud detection accuracy by leveraging the diverse strengths of both models. The system will undergo rigorous evaluation to ensure its efficiency in identifying fraudulent transactions while minimizing false positives. By combining the temporal sequencing capabilities of LSTM with the decision-making process of Random Forest, the proposed approach seeks to achieve heightened sensitivity to fraudulent patterns while maintaining computational efficiency. Ultimately, the objective is to bolster security measures and protect financial institutions and consumers from potential fraud risks.

The study examined The need for forensic auditing in public sector fraud Reduction in Nigerian public sector. To achieve this objective, data was collected from secondary source which include press reports, report of Economic and Financial Crime Commission EFCC , Report of Independent and Corrupt Practices Commission ICPC and Report from investigation committees. Two hypotheses were tested with the use of simple regression analysis. The results revealed that there is a significant relationship between forensic audit and fraud reduction also, that an increase in Forensic Audit significantly leads to a decrease in the occurrence of fraud cases in Nigerian public sector. On the basis of this finding, the study concludes that the services of Professional Forensic Auditors are needed to help reduce the occurrence of fraud in Nigerian public sector. Consequently, the study suggests among others that the Federal Government of Nigeria should adopt the services of professional forensic auditors to help reduce the occurrence of fraud in Nigerian public sector. Ekwe, Michael. C. | Azubike J. U. B. | Odogu, Laime Isaac "Forensic Auditing and Public Sector Fraud Detection in Rivers State, Nigeria" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28122.pdfPaper URL: https://www.ijtsrd.com/management/public-sector-management/28122/forensic-auditing-and-public-sector-fraud-detection-in-rivers-state-nigeria/ekwe-michael-c

Introduction Extra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc. Materials and Methods There are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive methods. Control sample (EVOO) was applied from "DANZEH food industry", Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in "Rahpooyan e danesh koolak Lab." Tehran, Iran; according to "Institute of standards and industrial research of Iran" ISIRI number: 4091 and INSO 13126-2. Eight semi-conductor gas sensors "FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822" applied in used olfaction machine. In this study there were 6 treatments: 1- Pure EVOO, 2- EVOO with 5% adulteration, 3- EVOO with 10% adulteration, 4- EVOO with 20% adulteration, 5- EVOO with 35% adulteration and 6- EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a non-destructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1- Sensor output (mV) in start of odor pulse (refer to fig. 3) 2- Sensor output at the end of odor pulse 3- Average of sensor output during odor pulse and 4- Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported. Results and Discussion Histogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time. Conclusion Due to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).

Introduction Extra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc. Materials and Methods There are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive methods. Control sample (EVOO) was applied from "DANZEH food industry", Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in "Rahpooyan e danesh koolak Lab." Tehran, Iran; according to "Institute of standards and industrial research of Iran" ISIRI number: 4091 and INSO 13126-2. Eight semi-conductor gas sensors "FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822" applied in used olfaction machine. In this study there were 6 treatments: 1- Pure EVOO, 2- EVOO with 5% adulteration, 3- EVOO with 10% adulteration, 4- EVOO with 20% adulteration, 5- EVOO with 35% adulteration and 6- EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a non-destructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1- Sensor output (mV) in start of odor pulse (refer to fig. 3) 2- Sensor output at the end of odor pulse 3- Average of sensor output during odor pulse and 4- Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported. Results and Discussion Histogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time. Conclusion Due to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).

Get a practical overview of Vertex AI capabilities by building a fraud detection system. Explore a guided tour of managed datasets, the feature store, pipelines, monitoring, workbench, and other Vertex AI Features. See a live demonstration of the resulting system, and reflect on some lessons learned while developing this app. Speaker: Ivan Nardini Watch more: All Google I/O 2022 Sessions → https://goo.gle/IO22_AllSessions Google Cloud at I/O 2022 playlist → https://goo.gle/IO22_GoogleCloud All Google I/O 2022 technical sessions → https://goo.gle/IO22_Sessions Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #GoogleIO Source: https://www.youtube.com/watch?v=5kEhkKfs4TI Uploader: Google Cloud Tech

Online exchanges have picked up ubiquity in the ongoing years with an effect of expanding fraud cases related with it. Fraud increments as new advances and shortcomings are found, bringing about huge misfortunes each year. Credit card fraud occasions occur as often as possible and afterward bring about colossal money related misfortunes. In this manner, banks and monetary establishments offer Visa fraud identification applications much worth and request. False exchanges can happen in different manners and can be placed into various classes. This paper centres on fraud events in certifiable exchanges. Dean Osmond Iangrai | Vignesh. S "Online Shopping with Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31028.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31028/online-shopping-with-fraud-detection/dean-osmond-iangrai

Occupational fraud affects many companies worldwide causing them economic loss and liability issues towards their customers and other involved entities. Detecting internal fraud in a company requires significant effort and, unfortunately cannot be entirely prevented. The internal auditors have to process a huge amount of data produced by diverse systems, which are in most cases in textual form, with little automated support. In this paper, we exploit the advantages of information visualization and present a system that aims to detect occupational fraud in systems which involve a pair of entities (e.g., an employee and a client) and periodic activity. The main visualization is based on a spiral system on which the events are drawn appropriately according to their time-stamp. Suspicious events are considered those which appear along the same radius or on close radii of the spiral. Before producing the visualization, the system ranks both involved entities according to the specifications of the internal auditor and generates a video file of the activity such that events with strong evidence of fraud appear first in the video. The system is also equipped with several different visualizations and mechanisms in order to meet the requirements of an internal fraud detection system.

Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy

Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy

Fraudulent behaviors in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. . Fair Play discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. A. Brahma Reddy | K. V. Ranga Rao | V. Vinay Kumar "Fraud and Malware Detection in Google Play by using Search Rank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35728.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35728/fraud-and-malware-detection-in-google-play-by-using-search-rank/a-brahma-reddy

Credit card fraud avoidance has been the most popular problem in the developed world. In this case, credit card fraud is identified by fraudulent transactions. Since e-commerce sites are becoming more popular, credit card fraud is becoming more common. When a credit card is stolen it is used for dishonest reasons, a fraudster uses the credit card information for his own purposes, and it is called credit card theft. In order to track online fraud transactions, the new technology employs a variety of methods. To increase the consistency of the proposed scheme, we used a random forest algorithm to find suspicious transactions. It is built on supervise learning algorithm, which classifies the dataset using decision trees. After the dataset has been categorized, a confusion matrix is established. The confusion matrix is used to test the Random Forest Algorithm's accuracy.

 Digitization has led to the emergence of increasingly sophisticated forms of financial fraud, necessitating more advanced and integrated approaches for their rapid detection and prevention. This challenge prompted the authors to examine relevant literature and analyze current policies and measures for detecting financial fraud within the digital environments of organizations, with the aim of enhancing proactive prevention strategies. To this end, an online empirical survey was conducted with 118 executives and managers from Serbia during the first half of 2024, supported by the Association of Employers of Serbia and the Association of Managers. The research focused on the impact of new technologies, particularly AI, on the regulations and organizational policies related to financial fraud detection. Qualitative research, which utilized 12 predefined statements within each impact group using a five-point Likert scale, provided insights into the actual experiences and perspectives of participants concerning financial fraud as a distinct business, social, and economic issue. Multiple correlation approaches were employed to analyze the data. The outcomes suggest that all analyzed factors contribute to addressing financial fraud, with new technologies – especially those based on artificial intelligence – and corporate policies and strategies playing significant roles. Conversely, regulations have a lesser impact, attributed to their correctness, implementation, and enforcement. These findings enhance the understanding of the significance of taking a comprehensive approach to combating fraud, corruption, and financial crime, and highlight the roles of continuous technological advancements, employee digital education, and enhanced communication with the public and investors in building trust and maintaining a company’s reputation.

 Digitization has led to the emergence of increasingly sophisticated forms of financial fraud, necessitating more advanced and integrated approaches for their rapid detection and prevention. This challenge prompted the authors to examine relevant literature and analyze current policies and measures for detecting financial fraud within the digital environments of organizations, with the aim of enhancing proactive prevention strategies. To this end, an online empirical survey was conducted with 118 executives and managers from Serbia during the first half of 2024, supported by the Association of Employers of Serbia and the Association of Managers. The research focused on the impact of new technologies, particularly AI, on the regulations and organizational policies related to financial fraud detection. Qualitative research, which utilized 12 predefined statements within each impact group using a five-point Likert scale, provided insights into the actual experiences and perspectives of participants concerning financial fraud as a distinct business, social, and economic issue. Multiple correlation approaches were employed to analyze the data. The outcomes suggest that all analyzed factors contribute to addressing financial fraud, with new technologies – especially those based on artificial intelligence – and corporate policies and strategies playing significant roles. Conversely, regulations have a lesser impact, attributed to their correctness, implementation, and enforcement. These findings enhance the understanding of the significance of taking a comprehensive approach to combating fraud, corruption, and financial crime, and highlight the roles of continuous technological advancements, employee digital education, and enhanced communication with the public and investors in building trust and maintaining a company’s reputation.

Abstract: Browser fingerprinting and user behavior tracking are powerful techniques used by most fraud and bot detection solutions. These are implemented as JavaScript snippets running the user browser. In this presentation, we’ll demystify what kind of signals these snippets collect. We'll then describe why these signals are unreliable, propose attacks against defenses relying on them and finally show demos of POC attacks. by Mayank Dhiman, Principal Security Researcher of Stealth Security Mayank Dhiman serves as Stealth Security’s Principal Security Researcher. His primary interests include solving problems related to online fraud and internet abuse. His current focus lies in detecting and mitigating malicious automation attacks. Previously, he had worked on fraud and abuse related solutions at Facebook and PayPal. He is the co-author of a number of research papers and book chapters and his work has been presented at USENIX HotSec, NDSS USEC, APWG eCrime, RSA, Botconf, Hack.lu and GreHack. He holds an MS in Computer Science from UC San Diego. Managed by the official OWASP Media Project https://www.owasp.org/index.php/OWASP_Media_Project Source: https://www.youtube.com/watch?v=7GFMm2ngm_Y Uploader: OWASP

Abstract: Browser fingerprinting and user behavior tracking are powerful techniques used by most fraud and bot detection solutions. These are implemented as JavaScript snippets running the user browser. In this presentation, we’ll demystify what kind of signals these snippets collect. We'll then describe why these signals are unreliable, propose attacks against defenses relying on them and finally show demos of POC attacks. by Mayank Dhiman, Principal Security Researcher of Stealth Security Mayank Dhiman serves as Stealth Security’s Principal Security Researcher. His primary interests include solving problems related to online fraud and internet abuse. His current focus lies in detecting and mitigating malicious automation attacks. Previously, he had worked on fraud and abuse related solutions at Facebook and PayPal. He is the co-author of a number of research papers and book chapters and his work has been presented at USENIX HotSec, NDSS USEC, APWG eCrime, RSA, Botconf, Hack.lu and GreHack. He holds an MS in Computer Science from UC San Diego. Managed by the official OWASP Media Project https://www.owasp.org/index.php/OWASP_Media_Project Source: https://www.youtube.com/watch?v=7GFMm2ngm_Y Uploader: OWASP

As we know and living in the era of digital world, Credit card fraud is increasing rapidly by transactions of unauthorized or any fraudulent use of someone else information of credit card to purchase and obtain benefits of financial. The victims of credit card fraud may have severe repercussions. Financial losses, harm to credit scores, and the trouble of dealing with unauthorized transactions can all arise from it. Secure your card information, keep a close eye on your account activity, and alert your card issuer right away to any odd transactions if you want to prevent credit card theft. To help combat fraud, many financial institutions additionally provide extra security features like two factor authentication and fraud detection systems. To resolve these problem we developed a system of Credit Card Fraud detection by hybrid techniques of machine learning which combines supervised and unsupervised methods to improve the system of fraud detection. In this paper we are using machine learning algorithms like K Nearest Neighbor, Logistic Regression and XGBoost model and we had made a comparison of accuracy score with other different models by using the data of European Cardholders 2013, by that data we had make comparison and decided that which model is best for defining the fraud system of credit card. Tripti Gautam | Ghanshyam Sahu | Lalit Kumar P. Bhiaya "Credit Card Fraud Detection Using Hybrid Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60102.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/60102/credit-card-fraud-detection-using-hybrid-machine-learning-algorithm/tripti-gautam

Credit card fraud is a serious and growing problem. Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with a sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge

A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge

A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge

The research evaluates all recent applications of machine learning (ML) and deep learning (DL) for detecting credit card fraud. The study details multiple approaches to develop fraud detection systems by exploring both data quality enhancement methods along with feature selection approaches and modeling strategies.  The implementation of advanced deep learning approaches LSTM together with CNNs leads to high real-time detection of fraud because they excel at detecting sophisticated temporal sequences. XGBoost ensemble methods used with AdaBoost and SMOTE methods make great strides in improving fraud dataset handling of class imbalance issues. The method which is known as federated learning currently attracts attention because it helps institutions to collaborate on separate model training without exposing their actual data. Problems persist with the current development of fraud detection models since they need adaptable models for various datasets while also requiring interpretation capabilities and functionality that adapts to changing deceit patterns. The development of privacy-preserving methods for fraud detection must continue because they need to achieve sufficient efficiency and security standards for real-time applications. Problems with scalability in addition to unclear detection methods and adaptability require new research directions that fill existing knowledge gaps.

The research evaluates all recent applications of machine learning (ML) and deep learning (DL) for detecting credit card fraud. The study details multiple approaches to develop fraud detection systems by exploring both data quality enhancement methods along with feature selection approaches and modeling strategies.  The implementation of advanced deep learning approaches LSTM together with CNNs leads to high real-time detection of fraud because they excel at detecting sophisticated temporal sequences. XGBoost ensemble methods used with AdaBoost and SMOTE methods make great strides in improving fraud dataset handling of class imbalance issues. The method which is known as federated learning currently attracts attention because it helps institutions to collaborate on separate model training without exposing their actual data. Problems persist with the current development of fraud detection models since they need adaptable models for various datasets while also requiring interpretation capabilities and functionality that adapts to changing deceit patterns. The development of privacy-preserving methods for fraud detection must continue because they need to achieve sufficient efficiency and security standards for real-time applications. Problems with scalability in addition to unclear detection methods and adaptability require new research directions that fill existing knowledge gaps.

Ranking fraud in the mobile App business propose to fraud exercises which have an inspiration self-motivated, bringing up the Apps up in the prevalent rundown. By and by days, number of shady means are used more much of the time by application developers, such expanding their Apps' business or posting fraud App appraisals, to give situating mutilation. There is a confined research for abstaining from ranking fraud. This paper gives a whole thought of situating double dealing and distinguishes the Ranking fraud unmistakable framework for mobile Apps. This work is gathering into three groupings. At first is web ranking spam detection, second is online review spam acknowledgment and last one is mobile application suggestion. The Web ranking spam incorporates to any ponder activities which pass on to choose Web pages a ridiculous ideal pertinence or centrality. Review spam is planned to give out of line perspective of a couple of items keeping in mind the end goal to affect the clients' perspective of the items by particularly or in a roundabout way influeating or harming the item's notoriety. In propose framework we additionally expel the fake reviews from the dataset utilizing comparability measure algorithm and after that identify the web rank spam. The trial result demonstrates that propose framework spare the time and additionally memory than the current framework. G. Mutyalamma | K. Komali | G. Pushpa"Ranking and Fraud Review Detection for Mobile Apps using KNN Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8232.pdf Article URL: http://www.ijtsrd.com/computer-science/data-miining/8232/ranking-and-fraud-review-detection-for--mobile-apps-using-knn-algorithm/g-mutyalamma

Ranking fraud in the mobile App business propose to fraud exercises which have an inspiration self-motivated, bringing up the Apps up in the prevalent rundown. By and by days, number of shady means are used more much of the time by application developers, such expanding their Apps' business or posting fraud App appraisals, to give situating mutilation. There is a confined research for abstaining from ranking fraud. This paper gives a whole thought of situating double dealing and distinguishes the Ranking fraud unmistakable framework for mobile Apps. This work is gathering into three groupings. At first is web ranking spam detection, second is online review spam acknowledgment and last one is mobile application suggestion. The Web ranking spam incorporates to any ponder activities which pass on to choose Web pages a ridiculous ideal pertinence or centrality. Review spam is planned to give out of line perspective of a couple of items keeping in mind the end goal to affect the clients' perspective of the items by particularly or in a roundabout way influeating or harming the item's notoriety. In propose framework we additionally expel the fake reviews from the dataset utilizing comparability measure algorithm and after that identify the web rank spam. The trial result demonstrates that propose framework spare the time and additionally memory than the current framework. G. Mutyalamma | K. Komali | G. Pushpa"Ranking and Fraud Review Detection for Mobile Apps using KNN Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8232.pdf Article URL: http://www.ijtsrd.com/computer-science/data-miining/8232/ranking-and-fraud-review-detection-for--mobile-apps-using-knn-algorithm/g-mutyalamma

Due to fast growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an increase in the credit card fraud. As credit card has became the most popular mode of payment for online and regular purchase, frauds associated with it are rising. In real life, fraudulent transactions are scattered with real transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of recent fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Sequence Alignment, Genetic Programming, Machine learning has evolved in detecting various credit card fraudulent transactions. This paper represents genetic algorithm used for credit card fraud detection mechanism which will detect the fraudulent transactions based upon credit card user behavior.