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

Area of Science:

• Computer Science
• Data Science
• Financial Technology (Fintech)

Background:

• The global financial technology (Fintech) market is rapidly expanding, driven by the increasing prevalence of online transactions and the demand for 24/7 accessible financial services.
• Ensuring the security of Fintech applications is critical due to the inherent risks of fraudulent activities associated with digital financial data.
• Machine Learning (ML) offers promising solutions for anomaly detection to combat fraud in Fintech.

Purpose of the Study:

• To evaluate the effectiveness of various Machine Learning (ML) anomaly detection methods for identifying fraudulent activities in Fintech.
• To analyze the performance of different ML techniques based on detection rates and the influence of selected features.
• To provide insights into the future security of Fintech applications based on ML-driven fraud detection.

Main Methods:

• Conducted experiments using multiple Machine Learning (ML) anomaly detection techniques.
• Utilized both real-world and synthetic datasets containing fraudulent financial transactions.
• Analyzed the detection rate and performance impact of selected features for each ML method.

Main Results:

• Machine Learning (ML) methods demonstrate varying degrees of success in detecting financial fraud.
• Specific ML techniques show different effectiveness in identifying anomalies within financial datasets.
• Feature selection significantly influences the performance of ML-based fraud detection systems.

Conclusions:

• Machine Learning (ML) is a valuable tool for enhancing security in Fintech applications by detecting fraudulent transactions.
• The choice of ML method and feature engineering are critical factors for optimizing fraud detection performance.
• Continued research and application of ML techniques are essential for safeguarding the future of Fintech.