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

Project and Seminar Material for Computer Science Education

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


Abstract


The study investigated the application of deep learning for fraud detection in e-payment system. The objectives of the study include among others to determine the level of fraud in e-payment system and to determine the effect of the application of deep learning on fraud detection in e-payment system; the researcher adopted survey research, in which a structured questionnaire was administered to e-payment experts. A total sample size of 100 was obtained from a total of e-payment experts in Uyo, Akwa Ibom state. The main instrument was questionnaire. The study used descriptive statistics to answer the questions posed for it. The non parametric test method (chi – square (x ) were used to test the two hypotheses that guided the study. The study revealed that that the level of fraud in the e-payment system is high; also, that the effect of the application of deep learning on fraud detection in e-payment system is positive. Against these background therefore, the researcher concluded and recommended that (1) Efforts should be made to enlighten the masses on the processes and working of deep learning (2) There should be proper orientation of the stakeholders on the need to apply deep learning for fraud detection in e-payment system.


Chapter One


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).


Chapter Five


Summary, Conclusion and Recommendations

5.1 Summary

This study focused on the application of Deep learning for fraud detection in e-payment system. The study was set to address three major research objectives. The objectives which 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.

Based on the above stated objectives and the study carried out, the following findings were made:

  1. That there is a high level of fraud in the e-payment system.
  2. That deep learning is very significant.
  3. That the effect of the application of deep learning on fraud detection in e-payment system include that it helps in easy detection of fraud in the e-payment system; it helps the AI system; it helps in beefing up security in the e-payment system; it helps in the acceptability of the e-payment system and it makes the payment system faster.

5.2 Conclusion

The main purpose of this study is to assess the application of Deep learning for fraud detection in e-payment system. The study was set to address three research objectives. Three research questions guided the study.

In this study, a survey research design was adopted, the population comprises all e-payment system experts in Uyo, Akwa Ibom state, a simple random sampling technique was used to select 100 respondents for the study and a questionnaire was the instrument for data collection. Relevant literatures were reviewed which guided the objectives and methodology of this study. As result of the field study and analysis of results, the following findings were made:

  1. That there is a high level of fraud in the e-payment system.
  2. That deep learning is very significant.
  3. That the effect of the application of deep learning on fraud detection in e-payment system include that it helps in easy detection of fraud in the e-payment system; it helps the AI system; it helps in beefing up security in the e-payment system; it helps in the acceptability of the e-payment system and it makes the payment system faster.

5.3 Recommendations

Based on the findings of this study, the following recommendations are made:

  1. Efforts should be made to enlighten the masses on the processes and working of deep learning.
  2. Government at all levels should ensure the adoption of deep learning in all e-payment systems.
  3. There should be proper orientation of the stakeholders on the need to apply deep learning for fraud detection in e-payment system.

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