Detection of Violations in Credit Cards of Banks and Financial Institutions based on Artificial Neural Network and Metaheuristic Optimization Algorithm
Due to popularity of the World Wide Web and e-commerce, electronic communications between people and different organizations through virtual world of the Internet have provided a good basis for commercial and economic relations. These developments, although occurring for less than a century, electro...
Saved in:
| Published in: | International journal of advanced computer science & applications Vol. 9; no. 1 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
West Yorkshire
Science and Information (SAI) Organization Limited
2018
|
| Subjects: | |
| ISSN: | 2158-107X, 2156-5570 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Due to popularity of the World Wide Web and e-commerce, electronic communications between people and different organizations through virtual world of the Internet have provided a good basis for commercial and economic relations. These developments, although occurring for less than a century, electronic communications have always been subject to interference, cheating, fraud, and other acts of sabotage. Along with this increase in trading volume, there is a huge increase in the number of online fraud which results in billions of dollars of losses annually worldwide; this has a direct effect on customer service of banking systems, particularly electronic banking systems, and survival as a reliable financial service provider. Therefore, attention to fraud detection techniques is essential to prevent fraudulent acts and is the motive for many scientific researches. For this reason, business intelligence is used to identify financial violations in various economic, banking and other fields. Here, the focus is on algorithms and methods presented in data mining to deal with fraud by using neural networks. The main objective is to improve these methods or present new algorithms by studying the behavioral patterns of customers and the combined use of genetic algorithm to improve the performance of neural network and find the appropriate models for better decision making by implementing and testing the performance of the suggested algorithms. The results show that more strength was given to neural network by using genetic algorithm. In fact, genetic algorithm can raise our ability to control the training process. Moreover, it was concluded that criteria such as age, gender, marital status were not effective on detection; in fact, the most important effective criteria are information related to transaction. |
|---|---|
| AbstractList | Due to popularity of the World Wide Web and e-commerce, electronic communications between people and different organizations through virtual world of the Internet have provided a good basis for commercial and economic relations. These developments, although occurring for less than a century, electronic communications have always been subject to interference, cheating, fraud, and other acts of sabotage. Along with this increase in trading volume, there is a huge increase in the number of online fraud which results in billions of dollars of losses annually worldwide; this has a direct effect on customer service of banking systems, particularly electronic banking systems, and survival as a reliable financial service provider. Therefore, attention to fraud detection techniques is essential to prevent fraudulent acts and is the motive for many scientific researches. For this reason, business intelligence is used to identify financial violations in various economic, banking and other fields. Here, the focus is on algorithms and methods presented in data mining to deal with fraud by using neural networks. The main objective is to improve these methods or present new algorithms by studying the behavioral patterns of customers and the combined use of genetic algorithm to improve the performance of neural network and find the appropriate models for better decision making by implementing and testing the performance of the suggested algorithms. The results show that more strength was given to neural network by using genetic algorithm. In fact, genetic algorithm can raise our ability to control the training process. Moreover, it was concluded that criteria such as age, gender, marital status were not effective on detection; in fact, the most important effective criteria are information related to transaction. |
| Author | Farajian, Nima Habibzadeh, Mehdi Monirzadeh, Zarrin |
| Author_xml | – sequence: 1 givenname: Zarrin surname: Monirzadeh fullname: Monirzadeh, Zarrin – sequence: 2 givenname: Mehdi surname: Habibzadeh fullname: Habibzadeh, Mehdi – sequence: 3 givenname: Nima surname: Farajian fullname: Farajian, Nima |
| BookMark | eNotUclOwzAUtBBIlNIv4GKJc4pjO4lzDIFCUaEHFnGLXC_UXexiu0LwI_wuacK7zFtm3hzmDBxbZxUAFykapzTLy6vpQ1U_V2OMUjZGJUoxPQIDnGZ5kmUFOu56lqSoeD8FoxBWqC1S4pyRAfi9UVGJaJyFTsM34zb8MARoLKy9kibCmnsZDtdrbtcBcivhxFhuheEbOLUhmrjvNQselITtq8pHo01HeFJ730H8cn7dqR9V5Mt2bVqpgPNdNFvz09nCavPhvInL7Tk40XwT1Ogfh-B1cvtS3yez-d20rmaJwAWOCeFaZ0hoJmmuGaIKsVSzElGkFoQSxgWRrNQLSbHWxYJnMqdUcFZmXCJBGBmCy_7vzrvPvQqxWbm9t61lg_Msp7goGWlZpGcJ70LwSjc7b7bcfzcparoQmj6E5hBC04dA_gCztX9L |
| ContentType | Journal Article |
| Copyright | 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7XB 8FE 8FG 8FK 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ GUQSH HCIFZ JQ2 K7- M2O MBDVC P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U |
| DOI | 10.14569/IJACSA.2018.090124 |
| DatabaseName | CrossRef ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection ProQuest One ProQuest Central ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Research Library Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic |
| DatabaseTitle | CrossRef Publicly Available Content Database Research Library Prep Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies & Aerospace Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2156-5570 |
| ExternalDocumentID | 10_14569_IJACSA_2018_090124 |
| GroupedDBID | .DC 5VS 8G5 AAYXX ABUWG ADMLS AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BENPR BGLVJ CCPQU CITATION DWQXO EBS EJD GNUQQ GUQSH HCIFZ K7- KQ8 M2O OK1 PHGZM PHGZT PIMPY PQGLB RNS 3V. 7XB 8FE 8FG 8FK JQ2 MBDVC P62 PKEHL PQEST PQQKQ PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c272t-3aff50cf8d46f804e081f89040eb3438ac3d89fbd42ff7ba5d644ca895ad0c383 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000426979200024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2158-107X |
| IngestDate | Fri Jul 25 22:12:23 EDT 2025 Sat Nov 29 06:19:56 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c272t-3aff50cf8d46f804e081f89040eb3438ac3d89fbd42ff7ba5d644ca895ad0c383 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/2656427983?pq-origsite=%requestingapplication% |
| PQID | 2656427983 |
| PQPubID | 5444811 |
| ParticipantIDs | proquest_journals_2656427983 crossref_primary_10_14569_IJACSA_2018_090124 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-00-00 20180101 |
| PublicationDateYYYYMMDD | 2018-01-01 |
| PublicationDate_xml | – year: 2018 text: 2018-00-00 |
| PublicationDecade | 2010 |
| PublicationPlace | West Yorkshire |
| PublicationPlace_xml | – name: West Yorkshire |
| PublicationTitle | International journal of advanced computer science & applications |
| PublicationYear | 2018 |
| Publisher | Science and Information (SAI) Organization Limited |
| Publisher_xml | – name: Science and Information (SAI) Organization Limited |
| SSID | ssj0000392683 |
| Score | 2.0570047 |
| Snippet | Due to popularity of the World Wide Web and e-commerce, electronic communications between people and different organizations through virtual world of the... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Index Database |
| SubjectTerms | Artificial neural networks Criteria Customer services Data mining Decision making Electronic banking Fraud Genetic algorithms Heuristic methods Information management Intelligence (information) Neural networks Optimization Performance enhancement Sabotage |
| Title | Detection of Violations in Credit Cards of Banks and Financial Institutions based on Artificial Neural Network and Metaheuristic Optimization Algorithm |
| URI | https://www.proquest.com/docview/2656427983 |
| Volume | 9 |
| WOSCitedRecordID | wos000426979200024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2156-5570 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392683 issn: 2158-107X databaseCode: P5Z dateStart: 20100101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2156-5570 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392683 issn: 2158-107X databaseCode: K7- dateStart: 20100101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Proquest Central customDbUrl: eissn: 2156-5570 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392683 issn: 2158-107X databaseCode: BENPR dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2156-5570 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392683 issn: 2158-107X databaseCode: PIMPY dateStart: 20100101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 2156-5570 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000392683 issn: 2158-107X databaseCode: M2O dateStart: 20100101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07b9swECZap0OXOukDSZMaHDpWjURJJjUFjmojaWFH6AtuF4HP2kgsu5bcv5K_2yNFNciSpQs1ECQlHHnf8XT3HUJvozhV3GX0kFgFiTLDgEsRBRZdRCZiynVbbILOZmw-zwrvcKt9WGWnE52iVmtpfeSnBAyPhNCMxWeb34GtGmX_rvoSGo_RXkRIZPf5Jxr887GEAP5Dx8QJS1sWUzr3xENgNmSnlx9H-ZeRDe9i70PARZLcB6f7utkBzqT_v6-6j555UxOP2r1xgB7p6jnqd2UcsD_VL9DtB924gKwKrw3-vlz78Di8rHC-BXBrcG5Ts2zvOa-ua8wrhScdVQfu4g3cGIuKCsNUdtmWnQJbAhD3cBHnbvRUN3yhdy1NNL4CvbXyCaF4dPMLPqZZrF6ib5Px1_wi8PUaAkkoaYKYG5OG0jCVDA0LEw3mhmEZqAm4sScx4zJWLDNCJcQYKniqwBiTnGUpV6GEq_Ir1KvWlT5EOJRgWERUxCZSiVaCEZGKoYiylCqlhDlC7zpBlZuWlqO01xkr17KVa2nlWrZyPUInnaRKf0br8k5Mrx_uPkZP7Vyt4-UE9ZrtTr9BT-SfZllvB2jvfDwrPg_c1oN2Sq6gLdKf0FNcTosffwFVtOY_ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NU9RAEO1CtEovICoFiDoHuBlJJsnO5EBR6-IWy8JqlWjtLcynbAFZ3A1Q_hL_hb_RnplEi4s3Dp5ymMykMnnVr3vS_RpgK0lzLXxFD011lGnbiYSSSeTYRRYyZcKEZhNsNOLjcfFpAX61tTAurbK1id5Q66lyZ-Q7FB2PjLKCp3tX3yPXNcr9XW1baARYDM2PWwzZ5ruDffy-25T2P5z0DqKmq0CkKKN1lApr81hZrrOO5XFmkBQtLxDMGFdmKRcq1bywUmfUWiZFrtFlUIIXudCxwoAO130AD_FO5rT6hyz6c6YTo7PR8cqf-KpONZWNG6EjdFOKncFht_e569LJ-LsYeZhmd8nwLhd4gusv_29b8xSWGleadAP2V2DBVM9guW1TQRqr9Rx-7pvaJ5xVZGrJ18m0Sf8jk4r0ZkjeNem50jM3-l5U53MiKk36rRQJafMp_BzH-prgUu6xQX2DOIETf_EZ9X72sanFmbkOMtjkI9rly6bglXQvvuHm1WeXL-DLvWzPKixW08qsAYkVOk4Jk6lNdGa05FTmsiOTImdaa2nX4W0LjPIqyI6ULlxzOCoDjkqHozLgaB02W2SUjQ2al39hsfHv4Tfw-ODk-Kg8GoyGL-GJWzccMm3CYj27Nq_gkbqpJ_PZaw93Aqf3DaLfLvw_iw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+of+Violations+in+Credit+Cards+of+Banks+and+Financial+Institutions+based+on+Artificial+Neural+Network+and+Metaheuristic+Optimization+Algorithm&rft.jtitle=International+journal+of+advanced+computer+science+%26+applications&rft.au=Monirzadeh%2C+Zarrin&rft.au=Habibzadeh%2C+Mehdi&rft.au=Farajian%2C+Nima&rft.date=2018&rft.issn=2158-107X&rft.eissn=2156-5570&rft.volume=9&rft.issue=1&rft_id=info:doi/10.14569%2FIJACSA.2018.090124&rft.externalDBID=n%2Fa&rft.externalDocID=10_14569_IJACSA_2018_090124 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2158-107X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2158-107X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2158-107X&client=summon |