Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 5; p. 1594
• Main Authors: Stojanović, Branka, Božić, Josip, Hofer-Schmitz, Katharina, Nahrgang, Kai, Weber, Andreas, Badii, Atta, Sundaram, Maheshkumar, Jordan, Elliot, Runevic, Joel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.02.2021; MDPI
• Subjects: Algorithms; Anomalies; anomaly detection; Blockchain; Business models; Credit cards; Customers; cybercrime; Datasets; Financial institutions; Fintech; Fraud; fraud detection; Global marketing; Machine learning; Neural networks; fraud detection; cybercrime; anomaly detection; Fintech; machine learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21051594

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.