scholarly journals An approach to benchmark fraud detection algorithms in the COVID-19 era

2021 ◽  
Author(s):  
Miroslawa Alunowska Figueroa ◽  
Daniel Turner-Szymkiewicz ◽  
Edgar Alonso Lopez-Rojas ◽  
Juan Sebastián Cárdenas-Rodriguez ◽  
Ulf Norinder
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Financial Institutions ◽  
Detection System ◽  
Synthetic Data ◽  
Fraud Detection ◽  
Financial Crime ◽  
Innocent People ◽  
Detection Algorithms ◽  
Digital Payments

To address the challenges in the fight against financial crime, particularly in the COVID-19 pandemic context, this paper focuses on financial synthetic data and the use of a reliable benchmark tool to test fraud detection algorithms. Compliance departments at financial institutions face the challenge of reducing the number of innocent people erroneously accused of fraud. To cope with this problem financial institutions are exploring the application of machine learning fraud detection algorithms and data analysis technologies to develop a more accurate and precise fraud detection system. However, approaches to streamlining and automating banks’ monitoring and testing processes is challenging as there is no consensus on a benchmark. We explore the relevance of measuring the applicability of a financial crime benchmark in the presence of a growing digital financial sector, such as in the case of Mexico. This study is particularly important due to serious threats that are faced by a rapidly developing financial system (2019 Mexican Central Bank Report). These risks have been further exacerbated as a result of the COVID-19 pandemic accelerating the shift towards digital payments.

scholarly journals Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

2020 ◽  
Vol 14 (04) ◽  
pp. 565-589
Author(s):  
Eren Kurshan ◽  
Hongda Shen
Keyword(s):  
Fraud Detection ◽  
Transaction Processing ◽  
Financial Crime ◽  
Numerous Application ◽  
Detection Techniques ◽  
Significant Interest ◽  
Crime Detection ◽  
Digital Payments ◽  
Graph Computing ◽  
Transaction Processing Systems

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. Artificial intelligence (AI) and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

scholarly journals Credit Card Fraud Detection System

2020 ◽  
Vol 6 (12) ◽  
pp. 24-27
Author(s):  
Shashank Singh and Meenu Garg
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Detection System ◽  
Fraud Detection ◽  
Detection Problem ◽  
Credit Card Fraud ◽  
Local Outlier Factor ◽  
Local Outlier ◽  
Isolation Forest

It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't be more important. This undertaking expects to outline the demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations, for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction

scholarly journals Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning

2011 ◽  
Vol 19 (4) ◽  
pp. 409-433 ◽  
Author(s):  
Francisco Cantú ◽  
Sebastián M. Saiegh
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Synthetic Data ◽  
Fraud Detection ◽  
Supervised Machine Learning ◽  
Data Set ◽  
Electoral Fraud ◽  
History Of ◽  
Historical Accounts ◽  
Authentic Data

In this paper, we introduce an innovative method to diagnose electoral fraud using vote counts. Specifically, we use synthetic data to develop and train a fraud detection prototype. We employ a naive Bayes classifier as our learning algorithm and rely on digital analysis to identify the features that are most informative about class distinctions. To evaluate the detection capability of the classifier, we use authentic data drawn from a novel data set of district-level vote counts in the province of Buenos Aires (Argentina) between 1931 and 1941, a period with a checkered history of fraud. Our results corroborate the validity of our approach: The elections considered to be irregular (legitimate) by most historical accounts are unambiguously classified as fraudulent (clean) by the learner. More generally, our findings demonstrate the feasibility of generating and using synthetic data for training and testing an electoral fraud detection system.

scholarly journals Online Transaction Fraud Detection System Based on Machine Learning

2021 ◽  
Vol 2023 (1) ◽  
pp. 012054
Author(s):  
Bocheng Liu ◽  
Xiang Chen ◽  
Kaizhi Yu
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fraud Detection

A Fraud Detection System Using Machine Learning

2021 ◽  
Author(s):  
Dhananjay Kalbande ◽  
Pulin Prabhu ◽  
Anisha Gharat ◽  
Tania Rajabally
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fraud Detection

scholarly journals Synthetic Minority Oversampling and Smote Regularised Deep Autoencoders Neural Network Techniques for Fraud Prediction in Financial Payment Services

2019 ◽  
Vol 8 (12) ◽  
pp. 3908-3915
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Financial Institutions ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Decision Tree Classifier ◽  
Class Imbalance Problem ◽  
Good Recall ◽  
Tree Classifier ◽  
Payment Services

Frauds in Financial Payment Services are the most prevalent form of cybercrime. The increased growth in e-commerce and mobile payments in recent years is behind the rising incidence of fraud in financial payment services. According to "McKinsey, fraud losses throughout the world could be close to $44 billion by 2025." Every year, fraudulent card transactions causes billions of US Dollar of loss. To reduce these losses, designing effective fraud detection algorithms is essential, which depend on sophisticated machine learning methods to help investigators in fraud. For banks and financial institutions, therefore, fraud detection systems have gained excellent significance. Though the fake transactions are very low when compared to genuine transaction, care must be taken to predict it so that the financial institutions can maintain the customer integrity. As fraud is unlikely to occur compared to normal operations, we have the class imbalance problem. We applied Synthetic Minority Oversampling TEchnique (SMOTE) and the Ensemble of sampling methods(Balanced Random Forest Classifier, Balanced Bagging Classifier, Easy Ensemble Classifier, RUS Boost) to Ensemble machine learning algorithms Performance assessment using sensitivity, specificity, precision, ROC area. The purpose of this article is to analyze different predictive models to see how precise they are to detect whether a transaction is a standard payment or a fraud. Instead of misclassifying a real transaction as fraud, this model seeks to improve detection of fraud. We noted that the technique of Ensemble learning using Maximum voting detects the fraud better than other classifiers. Decision Tree Classifier, Logistic Regression, Balanced Bagging classifier is combined and the proposed algorithm is OptimizedEnsembleFD Algorithm. The sample size is increased and deep learning is applied .It is found that the proposed system Smote Regularised Deep Autoencoders (SRD Autoencoders) neural network performs better with good recall and accuracy for this large dataset.

scholarly journals Credit Card Fraud Detection System

2021 ◽  
Vol 10 (2) ◽  
pp. 158-162
Author(s):  
Kartik Madkaikar ◽  
◽  
Manthan Nagvekar ◽  
Preity Parab ◽  
Riya Raika ◽  
...  
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Credit Card ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Paper Machine ◽  
Credit Card Fraud

Credit card fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.

scholarly journals An Real Time E Commerce application to Find Credit Card Fraud

2021 ◽  
pp. 86-91
Author(s):  
G Yagnadatta ◽  
Nitesh N ◽  
Mohit S ◽  
Padmini M S
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Real Time ◽  
Credit Card ◽  
Detection System ◽  
Learning Algorithms ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Machine Learning Methods ◽  
Unethical Activity

Credit card fraud detection is one of the prominent problem in today's world. It is due to the extensive rise in both online and e-commerce transactions. The fraud happens when the users’ accessible card gets stolen from any unauthorized source or the use of credit card for fraudulent purposes. The present scenario is facing this kind of problem. So to detect the unethical activity, the credit card detection system was introduced. The main aim of this research is to focus on machine learning methods. So the algorithms used are unsupervised learning algorithms.

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