Hybrid Analysis on Credit Card Fraud Detection Using Machine Learning Techniques

Handa, Akansha; Dhawan, Yash; Semwal, Prabhat

doi:10.1007/978-3-030-74753-4_15

Cited by 3 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the number of people using social media continues to rise, customers expect banks to provide their services through these platforms. They anticipate banks to utilize social media channels for quicker and more effective service and product offerings, personalized financial advice, sharing financial proposals and future plans, addressing banking regulations, and establishing mechanisms for customer feedback on their banking products and services (10,25,26).…”

Section: Methodsmentioning

confidence: 99%

A Model for Analysis of Social Media in Adoption of Mobile Banking

Hamidi,

Mohammadi

2024

IJE

View full text Add to dashboard Cite

Banks can design more efficient methods for customer acquisition by utilizing social media platforms. By monitoring information from social media platforms, banks can analyze customers' reactions to offer by competitors and adopt appropriate strategies to increase customer satisfaction and attract new customers. Present research focuses on analyzing the role of social media in the acceptance of mobile banking at Bank Melli of Qazvin Province. This research is of a survey and applied nature. The data collection tool is a survey, and the data collection method is fieldwork. The study population includes all managers and deputies of Bank Melli of Qazvin Province in various positions. The sample size is calculated using the Cochran formula due to the limited population. In this study, the indicators were identified, and hypotheses were formulated based on these indicators. Finally, using statistical techniques, all the proposed hypotheses were proven and the impact of all indicators was confirmed. The results have confirmed the effect of social media performance on the individual recognition of mobile banking consumers.

show abstract

Section: Methodsmentioning

confidence: 99%

A Model for Analysis of Social Media in Adoption of Mobile Banking

Hamidi,

Mohammadi

2024

IJE

View full text Add to dashboard Cite

show abstract

“…As an intelligent system, these models are capable of identifying fraud patterns and recognizing suspicious transactions. Using advanced methods like machine learning and artificial intelligence can improve fraud detection and prevent financial and credit losses caused by fraud in e-commerce (24)(25)(26). The financial industry has been able to use better and more effective fraud detection methods; thanks to the development and advancement of various technologies and their combination with big data analysis and artificial intelligence.…”

Section: Literature Reviewmentioning

confidence: 99%

Presenting a Model to Detect the Fraud in Banking using Smart Enabling Tools

Hamidi,

Karbasiyan

2024

IJE

View full text Add to dashboard Cite

In recent years, with the increase of access to customer data and the improvement of data analysis capabilities through intelligent methods, various activities have been carried out to analyze customer behavior; it is in the detection of bank frauds. Currently, bank frauds have a wide range of results, other than material and financial losses to the bank, customers and banks. After using smart tools to use different algorithms, the two selected algorithms XGBoost and LightGBM, according to the high ROC in the obtained models were selected step by step. At the same time, it has been used in final tests with the reduction of false samples labeled as fraud (FP). This model is developed using real development data and gives very acceptable results in card-to-card fraud detection. This model can significantly improve the security of the banking system and be used as a tool to reduce financial crimes.

show abstract

“…Te efciency of four distinct approaches is evaluated by attaining scores of assessment metrics. Handa et al [14] introduced a hybrid analysis of distinct ML algorithms in detecting fraud transactions. Ten, discuss and compare the performances of DL, supervised, unsupervised, and hybrid methods executed by ensemble ML methods.…”

Section: Related Workmentioning

confidence: 99%

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Prabhakaran

Nedunchelian

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.

show abstract

Hybrid Analysis on Credit Card Fraud Detection Using Machine Learning Techniques

Cited by 3 publications

References 16 publications

A Model for Analysis of Social Media in Adoption of Mobile Banking

A Model for Analysis of Social Media in Adoption of Mobile Banking

Presenting a Model to Detect the Fraud in Banking using Smart Enabling Tools

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Contact Info

Product

Resources

About