Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems
Recent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01116.pdf |
| _version_ | 1827311609585860608 |
|---|---|
| author | Maithili K. Sathish Kumar T. Subha R. Srinivasa Murthy P.L. Sharath M.N. Gurnadha Gupta Koppuravuri Ravuri Praseeda Madhuri T.N.P. Verma Vikas |
| author_facet | Maithili K. Sathish Kumar T. Subha R. Srinivasa Murthy P.L. Sharath M.N. Gurnadha Gupta Koppuravuri Ravuri Praseeda Madhuri T.N.P. Verma Vikas |
| author_sort | Maithili K. |
| collection | DOAJ |
| description | Recent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit card fraud identification and must be selected carefully. This study suggests a An Efficient Machine Learning Algorithm for Reliable Credit Card Fraud Identification (EMLA-RCCFI) was constructed using ML, which utilizes the Genetic Algorithm (GA) to select features. Once the optimum characteristics are determined, the suggested detecting module utilizes the subsequent ML-based classifications. The proposed EMLA-RCCFI system is assessed using a dataset produced by European cardholders to confirm its efficacy. Based on the results, the suggested EMLA-RCCFI method surpassed existing systems regarding accuracy, precision, and F score. |
| first_indexed | 2024-04-24T20:21:45Z |
| format | Article |
| id | doaj.art-74776da3577f45d1a903cc92791b11c7 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:45Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-74776da3577f45d1a903cc92791b11c72024-03-22T08:05:18ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920111610.1051/matecconf/202439201116matecconf_icmed2024_01116Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systemsMaithili K.0Sathish Kumar T.1Subha R.2Srinivasa Murthy P.L.3Sharath M.N.4Gurnadha Gupta Koppuravuri5Ravuri Praseeda6Madhuri T.N.P.7Verma Vikas8Associate Professor, Department of CSE – AIML, KG Reddy College of Engineering & TechnologyAssociate Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and ManagementAssistant Professor, Department of Computer Science, Karpagam Academy of Higher EducationProfessor, Department of Computer Science and Engineering, IAREAssociate Professor, Rajeev Institute of TechnologyDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationComputer Science Engineer, Oregon State UniversityDepartment of IT, GRIETLovely Professional UniversityRecent developments in e-commerce and e-payment systems have led to a rise in financial fraud incidents, particularly credit card fraud. Software tools to identify credit card theft are essential. Critical characteristics of credit card fraud are crucial in utilizing Machine Learning (ML) for credit card fraud identification and must be selected carefully. This study suggests a An Efficient Machine Learning Algorithm for Reliable Credit Card Fraud Identification (EMLA-RCCFI) was constructed using ML, which utilizes the Genetic Algorithm (GA) to select features. Once the optimum characteristics are determined, the suggested detecting module utilizes the subsequent ML-based classifications. The proposed EMLA-RCCFI system is assessed using a dataset produced by European cardholders to confirm its efficacy. Based on the results, the suggested EMLA-RCCFI method surpassed existing systems regarding accuracy, precision, and F score.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01116.pdf |
| spellingShingle | Maithili K. Sathish Kumar T. Subha R. Srinivasa Murthy P.L. Sharath M.N. Gurnadha Gupta Koppuravuri Ravuri Praseeda Madhuri T.N.P. Verma Vikas Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems MATEC Web of Conferences |
| title | Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems |
| title_full | Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems |
| title_fullStr | Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems |
| title_full_unstemmed | Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems |
| title_short | Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems |
| title_sort | development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01116.pdf |
| work_keys_str_mv | AT maithilik developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT sathishkumart developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT subhar developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT srinivasamurthypl developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT sharathmn developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT gurnadhaguptakoppuravuri developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT ravuripraseeda developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT madhuritnp developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems AT vermavikas developmentofanefficientmachinelearningalgorithmforreliablecreditcardfraudidentificationandprotectionsystems |