Employing Big Data Analytics’ Lifecycle in Money Laundering Detection

This paper examines one of the most important benefits of big data analytics: the quick detection of suspicious financial transactions such as money laundering. Besides being one of the essential characteristics of big data, velocity is needed today for analyzing data and identifying risks to make quick and appropriate decisions. Worthy of mentioning here that this paper presents a model for exploring financial transfers and discovering suspicious transactions by employing the six phases of the big data analytics lifecycle: discovery, data preparation, model planning, model building, communicating results, and operationalization. This is a simplified model. It paves the way for a more inclusive methodology for reducing financial crimes in future, employing the big data analysis lifecycle.