Machine Learning based Fraud Analysis and Detection System

Kousika, N.; Vishali, G.; Sunandhana, S; Vijay, M Arvind

doi:10.1088/1742-6596/1916/1/012115

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…Deep designs should be considered to be an achievement. They can theoretically address the optimisation struggle in a profound manner within the training parameters [17], [18].…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

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Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

et al. 2022

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People can make use of credit card for online transactions as it provides efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect such frauds which include the accessibility of public data, high-class imbalance data and fraud nature can be changed and the false alarm is in high rates. The relevant literature presents a number of machines learning based approaches for credit card detection.Such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and XG Boost. But due to low accuracy, there is still need to apply state of the art deep learning algorithms to reduce the fraud losses.The main focus has been to apply the recent development of deep learning algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes. The detail empirical analysis is carried out using European card benchmark dataset for fraud detection. Machine learning algorithm is first applied on the data set which showed improvement in the accuracy of detection of the frauds to some extent. Later, three architectures based on convolutional neural network are applied to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A comprehensive empirical analysis has been carried out by applying variations in number of hidden layers, epochs and applying the latest models. The evaluation of research work shows the improved results achieved such as accuracy, f1-score, precision and AUC Curves having optimized values 99.9%,85.71%,93%,98% respectively. The purposed model outperforms over state of art machine learning and deep learning algorithms for credit card detection problems.In addition, we have performed experiments by balancing the data and applying deep learning algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-world detection of credit card frauds.

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“…Deep designs should be considered to be an achievement. They can theoretically address the optimisation struggle in a profound manner within the training parameters [17], [18].…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

“…Natural language processing is a very popular example of a 1DCNN application where sequence classification becomes a problem. In a 1DCNN, the kernel filter moves top to bottom in a sequence of a data sample, rather than moving left to right and top to bottom in the 2DCNN [17], [18].…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

et al. 2022

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“…Every payment system for credit card transactions comprises four components: the merchant, cardholder, acquiring bank, and issuing bank. These four components communicate with each other using HTTP requests to make the transaction possible [11]. The transaction is handled and checked differently by the issuing bank.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

El Kafhali,

Tayebi,

Sulimani

2024

Information

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The proliferation of new technologies and advancements in existing ones are altering our perspective of the world. So, continuous improvements are needed. A connected world filled with a vast amount of data was created as a result of the integration of these advanced technologies in the financial sector. The advantages of this connection came at the cost of more sophisticated and advanced attacks, such as fraudulent transactions. To address these illegal transactions, researchers and engineers have created and implemented various systems and models to detect fraudulent transactions; many of them produce better results than others. On the other hand, criminals change their strategies and technologies to imitate legitimate transactions. In this article, the objective is to propose an intelligent system for detecting fraudulent transactions using various deep learning architectures, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). Furthermore, the Bayesian optimization algorithm is used for hyperparameter optimization. For the evaluation, a credit card fraudulent transaction dataset was used. Based on the many experiments conducted, the RNN architecture demonstrated better efficiency and yielded better results in a shorter computational time than the ANN LSTM architectures.

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“…Machine learning algorithms have transformed risk management and fraud detection in financial institutions by providing a more advanced and efficient approach to identifying and mitigating risks and detecting fraud (Sadineni, 2020). As financial transactions become increasingly complex and voluminous, traditional rule-based risk management and fraud detection methods are no longer sufficient (Kousika et al, 2021). Machine learning algorithms offer several advantages in risk management and fraud detection within financial institutions.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

Kumar,

Singh

2024

Int. J. Res. Publ. Rev

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This study examines the impact of machine learning algorithms on risk management and fraud detection in financial institutions. Machine learning has the potential to be integrated into various decision-making systems. In recent years, the ability of machine learning algorithms to analyze large datasets and provide precise predictions has attracted significant attention across multiple sectors. Accurate risk assessment and fraud prevention are crucial for financial institutions to avoid potential financial losses and damage to their reputation. Machine learning algorithms have enabled financial institutions to adopt more efficient and effective strategies to mitigate these risks. Financial institutions increasingly utilize machine learning algorithms to improve risk management and fraud detection. These algorithms employ advanced statistical techniques to analyze massive datasets and identify patterns and anomalies that may indicate potential risks or fraudulent activities. The most widely used machine learning algorithms in risk management and fraud detection are supervised learning algorithms like logistic regression and decision trees and unsupervised learning algorithms like clustering and anomaly detection. These algorithms can process and analyze vast amounts of data in real time, allowing financial institutions to detect and respond more effectively to potential risks and fraudulent activities.

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Machine Learning based Fraud Analysis and Detection System

Cited by 8 publications

References 3 publications

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

An Optimized Deep Learning Approach for Detecting Fraudulent Transactions

Analyzing the Impact of Machine Learning Algorithms on Risk Management and Fraud Detection in Financial Institution

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