DISTRIBUTED DATA MINING IN CREDIT CARD FRAUD DETECTION

CHAPTER ONE

INTRODUCTION

1.0 Introduction

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends. It is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Clifton, Christopher (2010); Hastie, Trevor et al., (2009). Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Fayyad, Usama et al., (1996). Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign, in contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data. Olson, D. L. (2007).

The term "data mining" is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. Han, Jiawei; Kamber, Micheline (2001). It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Bouckaert, Remco R. et al., (2010). Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

In this article, we survey and evaluate a number of techniques that address these three main issues concurrently. Our proposed methods of combining multiple learned fraud detectors under a “cost model” are general and demonstrably useful; our empirical results demonstrate that we can significantly reduce loss due to fraud through distributed data mining of fraud models.

Our approach in today’s increasingly electronic society and with the rapid advances of electronic commerce on the Internet, the use of credit cards for purchases has become convenient and necessary.

Credit card transactions have become the de facto standard for Internet and Web based e-commerce. The US government estimates that credit cards accounted for approximately US $13 billion in Internet sales during 1998.

This figure is expected to grow rapidly each year. However, the growing number of credit card transactions provides more opportunity for thieves to steal credit card numbers and subsequently commit fraud.

When banks lose money because of credit card fraud, cardholders pay for all of that loss through higher interest rates, higher fees, and reduced benefits. Cardholders interest to reduce illegitimate use of credit cards by early fraud detection. For many years, the credit card industry has studied computing models for automated detection systems; recently, these models have been the subject of academic research, especially with respect to ecommerce.

1.1 Background of the Study

The usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Many E-commerce website use the Credit card payment method for transaction or buying purpose. Credit card transactions take place over an Internet and hence the fraud and risks related to it is also increasing day by day. The organizations has to build systems which can detect the fraud before it takes place, as now the fraud is done and then the user understand that fraudulent has happened. In the credit card fraud, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The way to detect this kind of fraud is to analyse the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns.

1.2 Statement of Problem

Credit card transactions continue to grow in number, taking an ever-larger share of the payment system and leading to a higher rate of stolen account numbers and subsequent losses by banks. Improved fraud detection thus has become essential to maintain the viability of the payment system.

Banks have used early fraud warning systems for some years. Large-scale data-mining techniques can improve on the state of the art in commercial practice. Scalable techniques to analyze massive amounts of transaction data that efficiently compute fraud detectors in a timely manner is an important problem, especially for e-commerce. Besides scalability and efficiency, the fraud-detection task exhibits technical problems that include skewed distributions of training data and nonuniform cost per error, both of which have not been widely studied in the knowledge-discovery and datamining community.

1.3 Aim/Objectives of the Study

The aim of this study is using distributed data mining in credit card fraud detection. The objectives are:

i. To improve fraud detection.

ii. To exhibits technical problems that include skewed distributions of training data and nonuniform cost per error.

iii. To reduce loss due to fraud through distributed data mining of fraud models.

iv. To determine security measures needed to be taken to detect the Credit card fraud.

1.4 Scope of the Study

This project covers a range of measures that could be employed in securing and detecting card fraud through distributed data mining. In the credit card fraud, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone.

1.5 Significant of the Study

This study serves as a contribution towards improving information, data and information security on computerized information systems especially for organizations where data is transferred from one point to another. It will contribute in solving the problems of insecurity on both credit card for individual computers on a network. It also enhances the ability to support customers by providing secured and accurate access to all information. In the same manner, it will benefit those who transact with credit card.

Other significant of this work is to provide:

I. An effective and reliable level of security measures and ensuring a security system that varies cards.

ii. Another important significant is for the computer system and operations to carryout good and reliable security measures it is meant for.

1.9 Definition of Terms

Based on this research work, the following terms are used, and briefly defined bellow;

Data mining: Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.

Data Management: Data Management comprises all disciplines related to managing data as a valuable resource.

Clear-text/plaintext: original data/information before it is enciphered/scrambled.

Ciphered text/cryptogram: Information (multimedia) that has been converted into other symbols or form.

Ciphers: the secret code used to convert plain text message.

Hackers: people who steal information without permission.

Fraud: A false representation of a matter of fact—whether by words or by conduct, by false or misleading allegations, or by concealment of what should have been disclosed—that deceives and is intended to deceive another so that the individual will act upon it to her or his legal injury.

Cryptologist: scientist who study different ways to protect information or data.
ASCII: American Standard Codes for Information Interchange.

BASIC: Beginners All-Purpose Symbols Instruction Codes.

DES: Data Encryption Standard.