Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications
Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/5/1594 |
| _version_ | 1797395276475924480 |
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| author | Branka Stojanović Josip Božić Katharina Hofer-Schmitz Kai Nahrgang Andreas Weber Atta Badii Maheshkumar Sundaram Elliot Jordan Joel Runevic |
| author_facet | Branka Stojanović Josip Božić Katharina Hofer-Schmitz Kai Nahrgang Andreas Weber Atta Badii Maheshkumar Sundaram Elliot Jordan Joel Runevic |
| author_sort | Branka Stojanović |
| collection | DOAJ |
| description | Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future. |
| first_indexed | 2024-03-09T00:33:07Z |
| format | Article |
| id | doaj.art-0c55212714fc482da2dc8cc404afd1b9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:33:07Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0c55212714fc482da2dc8cc404afd1b92023-12-11T18:21:16ZengMDPI AGSensors1424-82202021-02-01215159410.3390/s21051594Follow the Trail: Machine Learning for Fraud Detection in Fintech ApplicationsBranka Stojanović0Josip Božić1Katharina Hofer-Schmitz2Kai Nahrgang3Andreas Weber4Atta Badii5Maheshkumar Sundaram6Elliot Jordan7Joel Runevic8Joanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, AustriaJoanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, AustriaJoanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, AustriaJoanneum Research, DIGITAL—Institute for Information and Communication Technologies, A-8010 Graz, AustriaFraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut, EMI, D-79588 Efringen-Kirchen, GermanyDepartment of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UKDepartment of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UKDepartment of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UKDepartment of Computer Science, School of Mathematical, Physical and Computational Sciences, University of Reading, Reading RG6 6AH, UKFinancial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.https://www.mdpi.com/1424-8220/21/5/1594fraud detectionmachine learninganomaly detectionFintechcybercrime |
| spellingShingle | Branka Stojanović Josip Božić Katharina Hofer-Schmitz Kai Nahrgang Andreas Weber Atta Badii Maheshkumar Sundaram Elliot Jordan Joel Runevic Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications Sensors fraud detection machine learning anomaly detection Fintech cybercrime |
| title | Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications |
| title_full | Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications |
| title_fullStr | Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications |
| title_full_unstemmed | Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications |
| title_short | Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications |
| title_sort | follow the trail machine learning for fraud detection in fintech applications |
| topic | fraud detection machine learning anomaly detection Fintech cybercrime |
| url | https://www.mdpi.com/1424-8220/21/5/1594 |
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