An Adaptive Predictive Financial Fraud Detection Approach Using Deep Learning Methods on A Big Data Platform

Description

– An Adaptive Predictive Financial Fraud Detection Approach Using Deep Learning Methods on A Big Data Platform –

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Abstract

Fraud, waste, and abuse in many financial systems are estimated to result in sig- nificant losses annually. Predictive analytics offer government and private financial institutions the opportunity to identify, prevent or recover such losses.

This work proposed a novel Big Data driven approach for fraud detection based on Deep Learn- ing methods. A supervised Deep Learning solution leveraging Big Data was shown to be an effective Fraud predictor.

Additionally, an unsupervised method based on anomaly detection using deep autoencoders was proposed for when there is few or no labelled data.

The two methods presented offered adaptive and predictive Fraud de- tection through improved Analytics. Future work will look into how the two methods can be integrated into an effective tool for enhanced Fraud detection.

Introduction

Fraud refers to the intentional illegal exploitation of a system which results in injury of an oblivious entity. Financial fraud involves the exploitation of financial systems which results in the loss of financial resource, the most prominent being monetary although other damages such as loss of integrity are possible.

Fraud, waste, and abuse in many financial systems are estimated to result in significant losses annually running into billions of US dollars. Furthermore, the proliferation of the internet has exposed financial systems to diverse fraudsters using different mechanisms to exploit financial systems.

This pro- vided an explode in attack patterns which rendered the once effective case-based fraud detection solutionsno more effective as the computational complexity increases with each new detected fraud.

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