Asia Pacific Journal of Information Systems (APJIS)

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Past Issue

Date	December 2018
Vol. No.	Vol. 28 No. 4
DOI	https://doi.org/10.14329/apjis.2018.28.4.308
Page	308~319
Title	Predictive Analysis of Financial Fraud Detection using Azure and Spark ML
Author	Priyanka Purushu, Niklas Melcher, Bhagyashree Bhagwat, Jongwook Woo
Keyword	Fraud Detection, Spark, Azure, Machine Learning, Hadoop, Big Data
Abstract	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.