Distributed Data Engineering Models for Real-Time Fraud Monitoring in FinTech Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I6P104Keywords:
Fraud detection, Data flow, Data lake, Semantic segmentation, Architectural model, FinTech systemsAbstract
Fraud in FinTech systems, namely in-banking and insurances represents a complex and multifactorial phenomenon that has been further amplified by social distancing and confinement measures to cope with the COVID-19 crisis. According to selected reports, fraud in Europe alone is estimated at 2.2 billion Euros for 20224 and is expected to continue its otherwise upward trend in different markets. To mitigate risks and minimize losses, private and public organizations invest continuously on fraud monitoring systems. However, fraud crime continuously adapts its detection methods and hence, systems are still required to evolve in the fraud investigation and detection area. To facilitate benchmark and comparison of different approaches, the application of an open-source distributed and scalable data engineering model is applied to create fraud monitoring systems in a FinTech environment. Fraud monitoring systems in distributed environments require different and specific execution models in an integrated and open-source environment. Data engineering platforms allow integration of different data sources and types with the possibility of executing distributed data engineering tasks in batch, streaming, micro-batch and real-time modes. An integrated analytical environment is used to support fraud monitoring in enterprises outside the banking sector. GDPR and data anonymization measures are used to guarantee the data privacy with a clear impact of the climate change index in a fraud model. Detection of anti-money laundering events related to COVID-19 is also presented and discussed. Finally, a case study in the European Banking Federation area is further analyzed.
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