dissertation topics it   Credit Card Fraud Detection System in UK: A Case Study on Royal Bank of Scotland Group

1. Introduction

The research study is having in depth discussion about the credit card fraud system regarding the Royal bank of Scotland (Duman and Elikucuk, 2013). There is also a discussion about the reason behind credit card frauds, as well as the complete process of detection.


1.1 Aim of research study

The main aim of this research study is to analyze the complete process of credit card fraud system. The technology used regarding credit card fraud detection is also analyzed in this study.


1.2 Objective of research study

The objective of research study is as –

  • To determine and analysis of credit card fraud detection system used by Royal bank of Scotland.
  • The analyze the complete credit card fraud system and detection process
  • The analysis of different technology used for detection of credit card fraud.
  • To analyze the importance of credit card detection methods implemented by royal bank of Scotland.

1.3 Hypothesis

H0 – Credit card fraud detection system is beneficial for royal bank of Scotland.

H1 – Credit card fraud detection system is not beneficial for royal bank of Scotland.

2. Literature review

2.2 Credit card fraud detection system

As mentioned by Sharma and Panigrahi (2013), with the increase of credit card usage fraud chance is also increased. It was found in a survey that people are tending more toward credit card payment. In context to this Wei et al. (2013) stated, royal bank of Scotland is trying to reduce the number of credit card fraud. This is the main reason behind implementation of credit card fraud detection system in bank. On the other hand, the credit card detection system is based on different technology and algorithm. In context to this Woźniak et al. (2014) commented, credit card payments in this e commerce dependent world have increased the number of credit card users.


2.3 Analysis of credit card fraud detection system with the help of different technologies and tools

In context to this Akhilomen (2013) stated, the main technology on which credit card fraud detection system is dependent are large scale data mining, artificial intelligence. Some other technologies used in this system are fuzzy algorithm, genetic programming and others. As mentioned by Gaber et al. (2013), the main reason behind credit card fraud system is, leakage of information of credit card and other details. With the increase of credit card frauds royal bank of Scotland customers number of reduced few years back. And this reduction in number of customers reduced their market share and also affected their name. Then taking into consideration these all factors, experts of bank decided to implement credit card fraud detection system. As mentioned by Bahnsen et al. (2014), it helped them to detect the main reason behind credit card fraud and other.


2.4 Importance of credit card fraud detection system

In context to this Duman and Elikucuk (2013) stated, credit card fraud detection system helped royal bank experts to detect and analyze the reason behind fraud. It also helped in keeping proper record of their customers and specially credit card users. In addition to this, bank also motivated their customers to protect their passwords and other card details.


2.5 Summary

The credit card fraud detection system helps in determining the main reason behind credit card fraud. It also helps in reducing the number of credit card fraud of royal bank of Scotland. The system is implemented with the help of different technology and algorithms.

3. Research methodology

3.1 Research approach

The research approach helps in analyzing the relation between different theories and assumptions (Akhilomen, 2013). In this research study the inductive research method is used, that is, top to bottom research method.


3.2 Research design

The design stage is most important stage in this research study. That is, the presentation style is very important. The research design regarding this study is descriptive design and experimental design.


3.3 Research philosophy

The data collection is done with the help of different philosophy related to topic. In this research study different principles, assumption and theories are taken into account.


3.4 Data collection approach

The research is completed with the help of primary and secondary data collection method. Primary data is collected with the help of employees and secondary data is collected with the help of different online sources.


3.5 Sample Size

The sample size regarding this research study is, 5 middle levels of employees and 5 top level employees of bank.


3.6 Research ethics

The research study is completed taking into account all codes of conduct, rules and regulations.


3.7 Limitations

The main limitation regarding this research study is time and budget limitation.


Duman, E., and Elikucuk, I. (2013). Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization. In Advances in Computational Intelligence (pp. 62-71). Springer Berlin Heidelberg.

Sharma, A., and Panigrahi, P. K. (2013). A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944.

Woźniak, M., Graña, M., and Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3-17.

Wei, W., Li, J., Cao, L., Ou, Y., and Chen, J. (2013). Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web, 16(4), 449-475.

Akhilomen, J. (2013). Data mining application for cyber credit-card fraud detection system. In Advances in Data Mining. Applications and Theoretical Aspects (pp. 218-228). Springer Berlin Heidelberg.

Gaber, C., Hemery, B., Achemlal, M., Pasquet, M., and Urien, P. (2013). Synthetic logs generator for fraud detection in mobile transfer services. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 174-179). IEEE.

Bahnsen, A. C., Stojanovic, A., Aouada, D., and Ottersten, B. (2014). Improving credit card fraud detection with calibrated probabilities. In Proceedings of the fourteenth SIAM International Conference on Data Mining (pp. 677-685).