Java Abs Distributed Data Mining In Credit Card Fraud Detection

DISTRIBUTED DATA
MINING IN CREDIT CARD
FRAUD DETECTION
INTRODUCTION
Credit card transactions grow in number, taking a larger share of any country’s
payment system and this in turn has led to a higher rate of stolen account
numbers and subsequent losses by banks. Hence, improved fraud detection has
become essential to maintain the viability of the country’s payment system.
Banks have used early fraud warning system for some years. Large-scale data-
mining techniques can improve on the state of the art in commercial practice.
Scalable techniques to 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 distribution of training data and non-uniform cost
per error, both of which have not been widely studied in the knowledge-
discovery and data mining community.
In this project, a deep survey is made and evaluates 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 demonstrable useful; our empirical result
demonstrate that we can significantly reduce loss due to fraud through
distributed data mining of fraud models.

DATA MINING AND MACHINE LEARNING
The aim of data mining is to extract knowledge from large amounts of data. This
knowledge is nontrivial and hidden in the data. Machine learning is often used in
data mining.

DATA MINING - A DEFINITION
It is an Art/Science of uncovering non-trivial, valuable information from a large
database
Its Emphasis is on:
• Non-obvious (difficult)
• Useful (cost vs benefit)
• Large (automatic)

Yet, no rules, provided that the process is efficient in time, space and human
resources.
• Data mining is the process of finding interesting trends or patterns in large
datasets in order to guide future decisions.
• Related to exploratory data analysis (area of statistics) and knowledge
discovery (area in artificial intelligence, machine learning).
• Data mining is characterized by having VERY LARGE datasets.

DATA MINING VS MACHINE LEARNING:
• Size: Databases are usually very large so algorithms must scale well
• Design Purpose: Databases are not usually designed for data mining (but
for other purposes), and thus, may not have convenient attributes
• Errors and Noise: Databases almost always contain errors

The aim of machine learning is to adapt to new circumstances, to detect and
extrapolate. A distinction can be made between unsupervised and supervised
machine learning algorithms.

EXISTING SYSTEM
1) In the existing system, there is a lot of credit card fraud transactions
2) More over the attrition rate of the banking are very high wherein the
payments are not made.
3) There is no proper strategy to evaluate the system in normal database
systems
Hence we need to propose a solution for entrancing & determining a large
customer base, which is possible only through data mining.

PROPOSED SYSTEM
In today’s increasingly electronic society and with the rapid advances of
electronic commerce on the Internet, the use 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 card
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.
Hence, it is in both the bank’s and 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 system; recently, these models have been the subject of
academic research, especially with respect to e-commerce.
The credit card fraud-detection domain presents a number of challenging
issues for data mining:
• These are millions of credit card transactions processed each day. Mining
such massive amounts of data requires highly efficient techniques that scale.
• The data are highly skewed-many more transactions are legitimate than
fraudulent.
• Typical accuracy-based mining techniques can generate highly accurate
fraud detectors by simply predicting that all transactions are legitimate,
although this is equivalent to not detecting fraud at all.

Each transaction record has a different dollar amount and thus has a variable
potential loss, rather than a fixed misclassification cost per error type, as is
commonly assumed in cost-based mining techniques.

Our approach addresses the efficiency and scalability issues in several
ways. We divide large data set of labeled transactions (either fraudulent or
legitimate) into smaller subsets apply mining techniques to generate classifiers
In parallel, and combine the resultant base models by metalearning from the
classifiers behavior to generate a metaclassifier.
Our approach treats the classifiers as black boxes so that we can employ a
variety of learning algorithms. Besides extensibility, combining multiple models
computed over all available data produces metaclassifiers that can offset the
loss of predictive performance that usually occurs when mining from data
subsets or sampling.
Furthermore, when we use the learned classifiers (for example, during
transaction authorization), the base classifiers can execute in parallel, with the
metaclassifier then combining their results. So our approach is highly efficient in
generating these models and also relatively efficient in applying them.
Another parallel approach focuses on parallelizing a particular algorithm on a
particular parallel architecture. However, a new algorithm or architecture
requires a substantial amount of parallel-programming work.
Although our architecture- and algorithm-independent approach is not as
efficient as some fine-grained parallelization approaches, it lets users plug
different off-the-shelf learning programs into a parallel and distributed
environment with relation ease and eliminates the need for expensive parallel
hardware.
The proposed system uses a the data mining algorithms to determine the credit
card fraud detection systems

THE FOLLOWING ALGORITHMS ARE USED TO IMPLEMENT THE
CREDIT CARD FRAUD DETECTION USING DATA MINING
1) Clustering – ‘K’ Means Clustering Algorithm
2) Classification- decision trees
3) Cost estimation- ADA cost algorithm

PROPOSED SYSTEM HARDWARE REQUIREMENTS
HARDWARE
Processor - PIII or higher processor
RAM - 128 MB or higher
HDD - 40 GB or higher
FDD - 1.44 MB
MONITOR - LG/SAMSUNG colour
Keyboard
Mouse
ATX Cabinet

SOFTWARE
OPERATING SYSTEM : WIN 2000/WIN XP/WIN 98
SOFTWARE : JDK 1.3 OR HIGHER
DATABASE : Oracle 8i

MODULES
1) KNOWLEDGE BASE CREATION

2) DATA ANALYSIS

3) DATA QUERY ANALYSER

4) CLUSTERING – ‘K’ MEANS CLUSTERING

5) CLASSIFICATION – DECISION TREES

6) COST ESTIMATION- ADA COST ALGORITHM

7) RESULT ANALYSER

8) GRAPHS

Java Abs Distributed Data Mining In Credit Card Fraud Detection

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