Methodological Problems with Big Data When Conducting Financial Crime

Dr. Lokanan used an Internal Grant for Research for a project involving predictor variables with financial crimes data and the issues researchers have in analyzing big data assemblies.

This project will give readers insights into some of the problems that researchers’ experienced when assembling big data for further empirical analysis. The methods to collate the data for further analysis are described as well as the modifications that need to be made in order to analyze the data. A common feature with financial crimes data is that predictor variables are typically serially correlated and can lead researchers to wonder beyond the data. This problem is addressed with reference to the assumptions and conditions of a multiple regression model using Python programing language.