ANALYSIS KEJADIAN FRAUD PADA SEBUAH FINTECH
DOI:
https://doi.org/10.56127/jts.v3i3.1912Keywords:
Predictive Analytics, Fintech, Fraud, PhytonAbstract
Fraud detection in technology-based financial services (Fintech) is one of the main challenges in the digital era. This research aims to develop a productive fraud detection system using data analytics and machine learning techniques. This project utilizes Python for algorithm implementation, accompanied by process visualization through flowcharts and analysis of related datasets. The resulting system is expected to be able to improve accuracy in detecting suspicious activities.
References
Aggarwal, C. C. (2015). "Data Mining: The Textbook." Springer.
Kotu, V., & Deshpande, B. (2019). "Data Science: Concepts and Practice." Morgan Kaufmann.
Zhang, J., et al. (2023). "Fraud Detection in Fintech: Machine Learning Approaches." Journal of Financial Technology.
Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD.
Dataset: Kaggle - Fintech Fraud Detection Dataset.
