Earticle

초록

영어

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Work
Ⅲ. Financial Fraud Detection using Azure ML and Spark ML
3.1. Method
3.2. Dataset
Ⅳ. Attributes of the Dataset
Ⅴ. Data Structure and Correlations
5.1. Experiments with the Traditional and Big Data Systems
5.2. Experiment with the Traditional Systems: Azure ML.
5.3. Experiment with the Big Data: Databricks with Spark ML.
Ⅵ. Conclusion
Acknowledgement

저자

• Priyanka Purushu [ Big Data Analyst, AT&T, USA ]
• Niklas Melcher [ Student, California State University, Los Angeles, USA ]
• Bhagyashree Bhagwat [ System Analyst, Los Angles Metro, USA ]
• Jongwook Woo [ Professor, California State University, Los Angeles, USA ] Corresponding Author

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