Application Of Deep Learning For Fraud Detection In E-Payment System

Application Of Deep Learning For Fraud Detection In E-Payment System

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PROJECT TOPIC AND MATERIAL ON APPLICATION OF DEEP LEARNING FOR FRAUD DETECTION IN E-PAYMENT SYSTEM

The Project File Details

  • Name: Application Of Deep Learning For Fraud Detection In E-Payment System
  • Type: PDF and MS Word (DOC)
  • Size: [70 KB]
  • Length: [56] Pages

 

 

 

 

INTRODUCTION

1.1     BACKGROUND OF THE STUDY

The high rate of e-payment fraud has called for stronger measures to be applied in detecting fraud. Deep learning is considered to be one of the measure that can be successfully applied for the detecting of e-payment fraud, financial fraud detection and anti-money laundering. Deep learning is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input; learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners; learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts; Deng, L.; Yu, D. (2014).

Deep learn leverages both supervised learning techniques, such as the classification of suspicious transactions, and unsupervised learning, e.g. anomaly detection. The study seeks to appraise application of deep learning for fraud detection in e-payment system.

1.2     STATEMENT   OF   THE PROBLEM

The level of fraud emanating from e-payment transaction is at an alarming rate. A recent report shows that Credit card fraud resulted in the loss of $3 billion to North American financial institutions in 2017. The increasing use of digital payments systems such as Apple Pay, Android Pay, and Venmo has result to increases in fraudulent activity. Deep Learning presents a promising solution to the problem of credit card fraud detection by enabling institutions to make optimal use of their historic customer data as well as real-time transaction details that are recorded at the time of the transaction. “Deep anti-money laundering detection system is capable of spotting and recognizing relationships and similarities between data and also has the capacity to detect anomalies or classify and predict specific events”. Deep learn leverages both supervised learning techniques, such as the classification of suspicious transactions, and unsupervised learning, e.g. anomaly detection. The problem confronting the study is to appraise application of deep learning for fraud detection in e-payment system.

1.3 OBJECTIVES OF THE STUDY

The Main Objective of the study is to appraise application of deep learning for fraud detection in e-payment system; The specific objectives include:

  1. To determine the level of   fraud in e-payment system.
  2. To determine the nature and significance of deep learning.
  3. To determine the effect of the application of deep learning on fraud detection in e-payment system.

1.4     RESEARCH QUESTIONS

  1. What is the level of   fraud in e-payment system?
  2. What is the nature and significance of deep learning?
  3. What is the effect of the application of deep learning on fraud detection in e-payment system?

1.5     STATEMENT OF THE HYPOTHESES

The statement of the hypothesis for the study is stated in Null as follows:

Ho1: The level of fraud in the e-payment system is low.

Ho2: The effect of the application of deep learning on fraud detection in e-payment system is negative.

1.6 SIGNIFICANCE OF THE STUDY

The study calls on relevant stakeholders on the need to adopt stronger measure for the detecting of fraud in e-payment transactions. Consequently, the study proffers an appraisal of deep learning as an appropriate measure for the detection of fraud in e-payment system.

 1.7 LIMITATION OF THE STUDY

The study was confronted with logistics and geographical factors.

1.8 DEFINITION OF TERMS

DEEP LEARNING DEFINED

Deep learning is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input; learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners; learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts; Deng, L.; Yu, D. (2014).

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