Proj Report

Credit Card Fraud Detection System

Chapter 1

Problem Definition

Department of Computer Engineering,Tathwade 1


1.1 Problem Definition

Design the software to implement computerized credit card fraud detection

system. It mainly consists of customer, credit card, system, administrator, message

sending system as mobile phones and computer.

There are two phases of system, one is online credit card transaction and the other

is credit card machine terminal transaction. The system must try to detect any kind of

fraud, for instance change of place, excessive amount, abnormal timing or behavior and

even the unusual frequency of transaction.

The system will develop an entity set using bank database. A credit card

transaction simulator is used to generate transactions randomly; these transactions are fed

to the system as an input. System will also have its own memory and will use A.I.

techniques and methodologies to prevent any misuse of the credit card.

Incase of any fraud the system will send an email or SMS whichever is feasible to

the owner of the credit card this will be considered as an output of the system.

An entity set is generated considering previous history of the customer stored in

the bank database; this is termed as profile of the customer. The profile of customer is

maintained by the bank and is assumed to be updated by the system regularly. The profile

of the customer will be generated by the system considering previous transactions of the

customer if present. The system will generate output to fraudulent transactions so as it

does not cause nuisance to the customer, for example if a customer is alerted for a

fraudulent transaction and the customer approves the transaction as non fraudulent, then

next time similar transaction will not generate fraud message. The entity set contains

variables which are having priorities associated with each of them. These will focus on

chances of detecting a transaction as a fraudulent transaction.



System administrator will assign priorities to each transaction parameter considering

customer requirements. E.g. User will give list of possible favorites such as grocery,

petrol, electronic goods etc.

AI technique in the system will see to it that the system does consider previously

detected fraudulent transactions which were approved by the customer to be non-

fraudulent.

1.2 Aim

1.3 Literature Servey:

Recent Developments in Fraud Management

The technology for detecting credit card frauds is advancing at a rapid pace –

rules based

Systems, neural networks, chip cards and biometrics are some of the popular

techniques Employed by Issuing and Acquiring banks these days. Apart from

technological advances, another trend which has emerged during the recent years is that

fraud prevention is moving from back-office transaction processing systems to front-

office authorization systems to prevent committing of potentially fraudulent transactions.

However, this is a challenging trade-off between the response time for processing an

authorization request and extent of screening that should be carried out.

SIMPLE RULE SYSTEMS:

Simple rule systems involve the creation of ‘if...then’ criteria to filter incoming

Authorizations/transactions. Rule-based systems rely on a set of expert rules designed to



identify specific types of high-risk transactions. Rules are created using the knowledge of

what characterizes fraudulent transactions. For instance, a rule could look like – If

transaction amount is > $5000 and card acceptance location = Casino and Country = ‘a

high-risk country’. Fraud rules enable to automate the screening processes leveraging the

knowledge gained over time regarding the characteristics of both fraudulent and

legitimate transactions. Typically, the effectiveness of a rule-based system will increase

over time, as more rules are added to the system. It should be clear, however, that

ultimately the effectiveness of the system depends on the knowledge and expertise of the

person designing the rules.

The disadvantage of this solution is that it can increase the probability of throwing

many Valid transactions as exceptions, however, there are ways by which this limitation

can be overcome to some extent by prioritizing the rules and fixing limits on number of

filtered Transactions.

RISK SCORING TECHNOLOGIES

Risk scoring tools are based on statistical models designed to recognize fraudulent

transactions, based on a number of indicators derived from the transaction characteristics.

Typically, these tools generate a numeric score indicating the likelihood of a transaction

being fraudulent: the higher the score, the more suspicious the order. Risk scoring

systems provide one of the most effective fraud prevention tools available.

The primary advantage of risk scoring is the comprehensive evaluation of a

transaction being captured by a single number. While individual fraud rules typically

evaluate a few simultaneous conditions, a risk-scoring system arrives at the final score by

weighting several dozens of fraud indicators, derived from the current transaction

attributes as well as cardholder historical activities. E.g., transaction amounts more that

three times the average transaction amount for the cardholder in the last one year.

The second advantage of risk scoring is that, while a fraud rule would either flag

or not flag a transaction, the actual score indicates the degree of suspicion on each



transaction.Thus, transactions can be prioritized based on the risk score and given a

limited capacity for manual review, only those with the highest score would be reviewed.

NEURAL NETWORK TECHNOLOGIES

Neural networks are an extension of risk scoring techniques. They are based on

the‘statistical knowledge’ contained in extensive databases of historical transactions, and

fraudulent ones in particular. These neural network models are basically ‘trained’ by

using examples of both legitimate and fraudulent transactions and are able to correlate

and weigh various fraud indicators (e.g., unusual transaction amount, card history, etc)

to the occurrence of fraud. A neural network is a computerized system that sorts data

logically by performing the following tasks:

1. Identifies cardholder’s buying and fraudulent activity patterns.

2. Processes data by trial and elimination (excluding data that is not relevant to the

pattern).

3. Finds relationships in the patterns and current transaction data.

The principles of neural networking are motivated by the functions of the brain –

especially pattern recognition and associative memory. The neural network recognizes

similar patterns, predicting future values or events based upon the associative memory of

the patterns it has learned. The advantages neural networks offer over other techniques

are that these models are able to learn from the past and thus, improve results as time

passes. They can also extract rules and predict future activity based on the current

situation. By employing neural networks effectively, banks can detect fraudulent use of a

card, faster and more efficiently.

BIOMETRICS

Biometrics is the name given to a fraud prevention technique that records a unique

characteristic of the cardholder like, a fingerprint or how he/she sign his/her name, so



Understanding Credit Card Frauds that it can be read by a computer. The computer can

then compare the stored characteristic with that of the person presenting the card to make

sure that the right person has the right card. Biometrics, which provides a means to

identify an individual through the verification of unique physical or behavioral

characteristics, seems to supercede PIN as a basis for the next generation of personal

identity verification systems.

There are many types of biometrics systems under development such as finger print

verification, hand based verification, retinal and iris scanning and dynamic signature

verification.

SMART CARDS

To define in the simplest terms, a smart card is a credit card with some intelligence in the

form of an embedded CPU. This card-computer can be programmed to perform tasks and

store information, but the intelligence is limited – meaning that the smart card's power

falls far short of a desktop computer.

Smart credit cards operate in the same way as their magnetic counterparts, the only

difference being that an electronic chip is embedded in the card. These smart chips add

extra security to the card. Smart credit cards contain 32-kilobyte microprocessors, which

is capable of generating 72 quadrillion or more possible encryption keys and thus making

it practically impossible to fraudulently decode information in the chip.

The smart chip has made credit cards a lot more secure; however, the technology is still

being run alongside the magnetic strip technology due to a slow uptake of smart card

reading terminals in the world market.

Smart cards have evolved significantly over the past decade and offer several advantages

compared to a general-purpose magnetic stripe card. The advantages are listed below:

Stores many times more information than a magnetic stripe card.

Reliable and harder to tamper with than a magnetic stripe card.

Performs multiple functions in a wide range of industries.

Compatible with portable electronic devices such as phones and personal digital

assistants (PDAs), and with PCs.

Stores highly sensitive data such as signing or encryption keys in a highly secure



Manner Performs certain sensitive operations using signing or encryption keys in a secure

fashion. A consortium of Europay MasterCard and Visa (EMV) recently issued a set of

specifications for embedding chips in credit cards and processing transactions from such

cards. MasterCard and Visa have also issued deadlines for compliance with these

specifications indicating that banks will have to bear a large portion of fraud losses if

they

do not comply with EMV specifications. However, the market response has been slow so

far due to large investments needed in implementing the EMV compliant programs.

1.4 Future scope:

Credit card fraud detection system developed has very wide range of applications.

The same system can be implemented for any kind of card such as petro cards, ATM

cards, and Security Cards etc. the system has a profound future scope the system

developed uses primitive Artificial Intelligence techniques. Additional artificial

intelligence techniques such as criminal mentality may be included in the system to make

it more foolproof. Also the parameters on which the transaction fraud is detected may be

further enhanced by consulting with the banking firms to increase the efficiency of the

system. The system could also be modified as per the requirements of the concerned bank

i.e. the system could use another mode of communication such as automated police

phone dialing, mailing etc.

1.5 System description :

Credit Card Fraud is one of the biggest threats to business establishments today.

However, to combat the fraud effectively, it is important to first understand the

mechanisms of executing a fraud. Credit card fraudsters employ a large number of modus

operandi to commit fraud. In simple terms, Credit Card Fraud is defined as:

When an individual uses another individuals’ credit card for personal reasons while the

owner of the card and the card issuer are not aware of the fact that the card is being used.



Further, the individual using the card has no connection with the cardholder or issuer, and

has no intention of either contacting the owner of the card or making repayments for the

purchases made.

Credit card frauds are committed in the following ways:

An act of criminal deception (mislead with intent) by use of unauthorized account and/or

personal information

Illegal or unauthorized use of account for personal gain

Misrepresentation of account information to obtain goods and/or services. Contrary to

popular belief, merchants are far more at risk from credit card fraud than the cardholders.

While consumers may face trouble trying to get a fraudulent charge reversed, merchants

lose the cost of the product sold, pay chargeback fees, and fear from the risk of having

their merchant account closed. Increasingly, the card not present scenario, such as

shopping on the internet poses a greater threat as the merchant (the web site) is no longer

protected with advantages of physical verification such as signature check, photo

identification, etc. In fact, it is almost impossible to perform any of the ‘physical world’

checks necessary to detect who is at the other end of the transaction. This makes the

internet extremely attractive to fraud

Perpetrators . According to a recent survey, the rate at which internet fraud occurs is 12 to

15 times higher than ‘physical world’ fraud.

IMPACT OF CREDIT CARD FRAUDS

Unfortunately, occurrences of credit card frauds have only shown an upward trend so far.

The fraudulent activity on a card affects everybody, i.e., the cardholder, the merchant,

the acquirer as well as the issuer. This section analyses the impact that credit card frauds

have on all the players involved in transacting business through credit cards.

Impact of Fraud on Cardholders

It's interesting to note that cardholders are the least impacted party due to fraud in credit



card transactions as consumer liability is limited for credit card transactions by the

legislation prevailing in most countries. This is true for both card-present as well as card-

not-present scenarios. Many banks even have their own standards that limit the

consumer's liability to a greater extent. They also have a cardholder protection policy in

place that covers for most losses of the cardholder. The cardholder has to just report

suspicious charges to the issuing bank, which in turn investigates the issue with the

acquirer and merchant, and processes chargeback for the disputed amount.

Impact of Fraud on Merchants

Merchants are the most affected party in a credit card fraud, particularly more in the

card-not-present transactions, as they have to accept full liability for losses due to fraud.

Whenever a legitimate cardholder disputes a credit card charge, the card-issuing bank

will send a chargeback to the merchant (through the acquirer), reversing the credit for

the transaction. In case, the merchant does not have any physical evidence (e.g. delivery

signature) available to challenge the cardholder’s dispute, it is almost impossible to

reverse the chargeback. Therefore, the merchant will have to completely absorb the cost

of the fraudulent transaction. In fact, this cost consists of several components, which

could add up to a significant amount. The cost of a fraudulent transaction consists of:

1. Cost of goods sold: Since it is unlikely that the merchandise will be recovered in a

case of fraud, the merchant will have to write off the value of goods involved in a

fraudulent transaction. The impact of this loss will be highest for low-margin

merchants.

2. Shipping cost: More relevant in a card-not-present scenario. Since the shipping cost

is usually bundled in the value of the order, the merchant will also need to absorb the

cost of shipping for goods sold in a fraudulent transaction. Furthermore, fraudsters

typically request high-priority shipping for their orders to enable rapid completion of

the fraud, resulting in high shipping costs.



3. Card association fees: Visa and MasterCard have put in place fairly strict programs

that penalize merchants generating excessive chargebacks. Typically, if a merchant

exceeds established chargeback rates for any three-month period (e.g. 1% of all

transactions or 2.5% of the total dollar volume), the merchant could be penalized

with a fee for every chargeback. In extreme cases, the merchant’s contract to accept

cards could be terminated.

4. Merchant bank fees: In addition to the penalties charged by card associations, the

merchant has to pay an additional processing fee to the acquiring bank for every

chargeback.

5. Administrative cost: Every transaction that generates a chargeback requires

significant administrative costs for the merchant. On average, each chargeback

requires one to two hours to process. This is because processing a chargeback

requires the merchant to receive and research the claim, contact the consumer, and

respond to the acquiring bank or issuer with adequate documentation.

6. Loss of Reputation: Maintaining reputation and goodwill is very important for

merchants as excessive chargeback’s and fraud monitoring could both drive

cardholders away from transacting business with a merchant.

Impact of Fraud on Banks (Issuer/Acquirer)

Based on the scheme rules defined by both MasterCard and Visa, it is sometimes possible

that the Issuer/Acquirer bears the costs of fraud. Even in cases when the Issuer/Acquirer

is not bearing the direct cost of the fraud, there are some indirect costs that will finally be

borne by them. Like in the case of chargeback’s issued to the merchant, there are

administrative and manpower costs that the bank has to incur.

The issuers and acquirers also have to make huge investments in preventing frauds by

deploying sophisticated IT systems for detection of fraudulent transactions.



1.6 Working of the system:

Credit card fraud detection system comprises of mainly three modules the simulator, the

fraud detection system module and sms module.

The simulator module generates random transactions this module was designed to

simulate the working of credit card swiping machine also known as terminal device. This

terminal device is registered at the service provider bank. The Credit card fraud detection

system also has client form in order to maintain and create database of this service

provider bank. Client form maintains all the necessary information of the customer r the

credit card holder. Also we can create new credit card holder accounts. The simulator

generated transactions are fed to the fraud detection system module which checks for

fraudulent transaction. In doing so this module maintains track record of each and every

credit card holder. This is termed as profile of the customer. Various other vital customer

details are maintained and updated by this module. Profile of the customer is updated at

each new transaction. Profile is nothing but the track record of the credit card usage by

the credit card holder. Simply stated it is the history of the card. The system being self

learning does not generate fraud signal if the similar transaction has already been

approved by credit card holder. If the fraud detection system module detects fraud

transaction it generates fraud signal which is fed to the sms module. The sms module

simply accepts the signal and sends sms to the respective credit card holder.

Credit Card Snapshot:



Fig 1.Credit Card Snapshot

Here are what some of the numbers stand for:

The first digit in your credit-card number signifies the system:

3 - travel/entertainment cards (such as American Express and Diners

Club)

4 - Visa

5 - MasterCard

6 - Discover Card

The structure of the card number varies by system. For example, American

Express card numbers start with 37; Carte Blanche and Diners Club with 38.

American Express - Digits three and four are type and currency, digits

five through 11 are the account number, digits 12 through 14 are the card

number within the account and digit 15 is a check digit.

Visa - Digits two through six are the bank number, digits seven through 12

or seven through 15 are the account number and digit 13 or 16 is a check

digit.

MasterCard - Digits two and three, two through four, two through five or

two through six are the bank number (depending on whether digit two is a

1, 2, 3 or other). The digits after the bank number up through digit 15 are

the account number, and digit 16 is a check digit.



The stripe on the back of a credit card is a magnetic stripe, often called a

magstripe. The magstripe is made up of tiny iron-based magnetic particles in a

plastic-like film. Each particle is really a tiny bar magnet about 20-millionths of

an inch long.

The general functioning of the credit card transaction is as shown below:

Fig 2. The general functioning of the credit card transaction

The model for fraud detection system is represented in the following diagram.

It must be noted that this is just a model and by no means similar to the working of our

system. It is depicted just to understand the system working.

It briefly lays the foundation of where the terminal is located or how does the bank come

into picture.


http://money.howstuffworks.com/iron.htm


Credit card role is depicted in the figure below:

Fig 3. Credit card role



Authorization of transaction takes place as follows:

Fig 4. Authorization of transaction

1.3 Advantages of the system:

Credit card fraud detection will minimize economic losses of the credit card

holder.

The dealer suffers the most when credit card fraud occurs.

The dealer has to repay the customer or give him some refund.

The dealer has to pay the credit card bank the loss occurred or compensation.

The major risk of all is that the customer will never again deal with the dealer.

This may result in loss of credit in the market.

These losses may be avoided by use of this system.

The credit card company has to stop the card usage for which it needs to block

the card which adds to its expenses this may be avoided.

The credit card service provider bank needs to employ special staff to deal with

such situations hence causing unnecessary overhead. This may be avoided.



1.7 Applications of the system:

The credit card fraud detection is applicable to security needed for any kind of card.

Though it is mainly concerned with credit card it may be applied to smart card, petro

cards, security cards, ATM cards etc. also the system to detect fraud may be used to

monitor accounts of user. The account may be of any type.



Chapter 2

Requirement Analysis



2 Requirement Analysis

Requirement analysis bridges the gap between system engineering and software analysis

design.

Software requirement analysis involves requirement collection, classification, structuring,

prioritizing and validation. Requirement analysis consists of two parts:

1. User Requirements

2. System Requirements

a. Functional Requirements

b. Nonfunctional Requirements

Requirements analysis of the credit card fraud detection system is as follows:

2.1 User Requirements

User Requirements specify services provided by system and constraints under

which it must operate.

The system should not cause overhead cost for sending sms.

The system should try to avoid unnecessary faults in detecting fraud.

System should be available at not too high price.

System should be compatible on existing transaction system.

System should be self learning.

System should be user friendly.

System should be automated such that it automatically sends sms to the

customer having a fraud transaction.

2.2 System Requirements

System requirements describe the system services and constraints in detail. Two types

of system requirements are:

Functional requirements

Nonfunctional requirements



2.2.1 Functional Requirements

Functional requirements for the system describe the functionality or services that

should be provided by system functions in detail, its input and output expectation.

2.2.2 Nonfunctional Requirements

Requirements relate to whole system not to individual system feature. This means

that they are often critical than functional requirement.

2.3 Functional Requirements

Functional requirements for the system describe the functionality or services that

should be provided by system functions in detail, its input and output expectation.

Different functional requirements are listed below:

The system should support following facilities:

The system should give a pop up screen when fraud is detected.

The system should automatically send a sms to the faulty transaction credit

card holder

The transaction details of the faulty transaction should be visible.

The credit card holder must get the transaction details of the detected faulty

transaction.

2.4 Software & Hardware Requirements

2.4.1 Software Requirements

Language: c# , visual studio.net 2003

Operating System: Windows Xp

Data base : sql server.

2.4.2 Hardware Requirements

Processor: P4

RAM: 256MB

Hard Disk: 10 GB

Keyboard: 101 Keys Keyboard

Mouse: scroll mouse



Chapter 3

System Analysis

3.1 System Enginering:

3.1.1 System goals:



The main objectives of System analysis are-

1. Identify the customer’s needs

2. Evaluate feasibility study

3. Perform economic & technical analysis

4. Allocate functions to software, hardware, people, and database.

5. Establish cost & schedule constraints

3.1.1.1 Identify Customer’s Needs

The main purpose of this step is to identify system goals which are defined by using the

question like - what info is to be produced. What info should be provided? What

functions & performance are required?

The customer’s needs are identified to find out the features that are required for

system’s success.

The system should try to detect out of track transactions.

The response time of the system should be minimal.

Customer should not have too much overhead cost for operating the

system.

The system should be user friendly.

The system should be foolproof enough so as not to create nuisance to the

card holder.

The customer details like address, phone no., and other details are

required.

The system also needs various other data such as assets owned by the

customer in order to have a rough background of the economic condition

of the customer.

3.1.1.2 Feasibility Study

It is necessary to evaluate feasibility of a project. Four primary areas for feasibility study

are:

a. Economic Feasibility

b. Technical Feasibility



c. Legal Feasibility

d. Alternatives

3.1.3.Project Cost & Performance

Hardware Requirement:

P4 (Pentium IV) or higher processor

RAM - minimum 256MB RAM

HDD - minimum 10 GB or more of free Hard-disk space

Cell phone – Nokia with data cable compatible

Software Requirement:

Operating System - WINDOWS Xp

Database - SQL SERVER

Visual Studio .NET

3.1.4 Alternatives:

Such kind of system is not available in Indian Software Market.

3.3 System Analysis

Feasibility Study

It is necessary to evaluate feasibility of a project. Four primary areas for feasibility study

are:



c. Legal Feasibility

d. Alternatives


This involves the study of cost benefit analysis.

The system will reside on the already existing bank server which will trace on

every incoming fraud. Hence operation cost of the system will be negligible

compared with the benefits as only RAM of the server will have to be enhanced to



higher power in proportion of the traffic of transactions. Also development cost of

the system is bearable by the bank compared to the losses it has to suffer.

This system will save vital customer money. Also the dealers will not get affected

adding to the economic value of the system.


During technical feasibility analyst evaluates technical merits of the system

concept, and at the same time collects information about performance, reliability,

maintainability.

The system is also technically feasible as variety of languages are available for

development like VC++, VB ,ASP ,C,C++,JAVA etc. databases such as DB2,

oracle could also have been used.

c. Allocation and Trade-offs

Each system function with its performance and interface characteristics is

allocated to one or more system elements.

The user interface is required to deal with entry and maintain accounts of the card

holders.

The sms module requires data cable enabled cell phone connected to the USB of

the computer in use.



Chapter 4

System Design

4.System Design:System design can be classified into two parts namely:

Data Flow Diagrams UML(Unified Modeling Language) Diagrams



4.1 Data Flow Diagram (DFD)

This section of report gives the dataflow of the system with the help of dataflow

diagrams as given below. Thus helping us to get knowledge of how the data in the

system flows and how interaction between them takes place.

4.1.1 DFD Level ‘0’:

Fig 5. DFD Level ‘0’

5.1.2 DFD Level ‘1’:


Bank Server SMS System

System Db Display

C.C.F.D. System


Fig 6. DFD Level ‘1’

4.2 Unified Modeling Language:



The Unified Modeling Language (UML) is a graphical language for visualizing,

specifying, constructing, and documenting the artifacts of a software intensive system. It

is very expressive language, addressing all the views needed to develop and then deploy

such systems. Following section shows different UML diagrams for Magic Image

Processor system itself, and different modules of system such as Image Resize, Image

Transformation, Image Enhancement, and Color Space Conversion.

4.2.1 UML Diagrams for Magic Image Processor System

4.2.1 Use Case Diagram

A use case diagram is a graph of actors, a set of use cases enclosed by a system boundary,

communication (participation) associations between the actors and the use cases, and

generalizations among the use cases.

Use case diagrams allow you to capture business events by analyzing how objects

external to the system interact with the system. A use case diagram represents a particular

sequence of transactions between the system and an actor (an end user or system external

to the system being analyzed). You can use case diagrams to analyze system

requirements and to help you define system boundaries.

Use case diagram for simulator:

simulator

simulates transaction

generates random place/terminal id

<<extern>>

generates random card no

<<extern>>

generates item according to terminal id

amount according to item

<<extend>>

<<extend>>



Figno 11. Use case diagram for simulator

Use case diagram for fraud detection system:

validate transaction check card limit & balance

<<extend>>

fraud alert evaluate fraud meter

<<extend>>

Fraud detection sys

initiate SMS system send SMS

<<extern>>

Figno 12. Use case diagram for fraud detection system

Use case diagram for administrator:



create place table

create profile table

create item & amount table

Admincreate new account

<<extern>>

<<extern>>

<<extern>>

Figno 13. Use case diagram for administrator:

4.2.2 Component Diagram

It shows the organizations and dependencies among a set of components.

Component is a replaceable part of a system.

Components can be packed logically.

It conforms to a set of interfaces.

It provides the realization of an interface

It represents a physical module of code.

Component diagram for Credit Card Fraud Detection System:



Figno 17. Component diagram for Credit Card Fraud Detection System

4.2.3 Class Diagram

A class diagram shows the existence of classes and their relations in the logical view of a

system. It shows

a) UML modeling elements in class diagram

b) Classes and their structure and behavior

c) Associations, aggregation, dependency, and inheritance relationships

d) Multiplicity and navigation indicators

e) Role names


credit card.data

credit card.log

simulator.cs

client.cs

fraud_pop_up.cs

SMS_send.cs

main.cs

generate transaction

display fraudulent transaction

create new account

send SMS to card owner


Validation

user_name : charPassword : sp.char

validate_user()grant_access_authority()

provides terminal id of particular place

place table

place_name : charterminal_id : int

get_palce()Display_place()display_terminal_id()modify()provide_info()

provides avilabe info on item to particuular terminal

item table

terminal_id : intitem_no : intitem : chardealer_type : char

get_item()get_dealer_type()provide_info()

provides the ammount of particular item

amount table

item_no : intamount : double

get_amount()provide_info()

simulate transaction

credit_card_no : intplace : charitem : charamount : doubledate_time : datetime

genrate_trans()display_trans()save_trans()get_msguser()

<<amount of item>>

<<item & dealer type>>

<<teminal id & place>>

simulates real world transaction for random credit card

provides credit card holders info

client info

credit_card_no : intname : charphone_no : intaddress : chare-mail_add : charoccupation : charnationality : charincome : intgender : charmarital_st : charassets : charfev : charcard_type : charcredit_limit : int

get_info()provid_info()manage_limit()create_place_table()create_item_table()create_ammount_table()

<<client info>>

Figno 7 - Class diagram for Simulator



Class diagram for Fraud Detection System :



genrates transaction


credit_card_no : intplace : charitem : charamount : doubledate_time : datetime

genrate_trans()display_trans()save_trans()get_msguser() provides client info

client info


get_info()provid_info()manage_limit()create_place_table()create_item_table()create_amount_table()

maintains most probable transaction record for each client

entity set

palce_table_card_no : tableitem_table_card_no : tableamont_avg : double

create_tables()update_entity_set()delete_entity_set()

+1

+1

detects fraudulent transaction

fraud detection system

trans_palce : chartrans_ammount : doubletrans_item : chartrans_datetime : datetimecard_no : intdealertype : char

detect_fradulent_trans()inform_sms_sys()unsave_fraudulent_trans()upgrade_AI_for_faulty_fraud_detection()

<<transaction>><<client info(card no,add)>>

<<probablistic trans detail...

informs SMS system

keeps latest transaction as profile

profile

trans_palce : chartrans_item : chartrans_ammount : doubletrans_datetime : datetimebalance_limit : double

maintain_latest_transaction()

<<profile>>

Figno 8. Class diagram for Fraud Detection System

Class diagram for Messaging System :



informs SMS systemdetects fraudulent transaction


trans_palce : chartrans_ammount : doubletrans_item : chartrans_datetime : datetimecard_no : intdealertype : char

detect_fradulent_trans()inform_sms_sys()unsave_fraudulent_trans()upgrade_AI_for_faulty_fraud_detection()

sends sms to appropriate card owner

SMS system

fraudulent_trans_palce : charfraudulent_trans_item : charfraudulent_trans_ammount : doublefraudulent_trans_datetime : datetimeclient_phone_no : intmessage_format : char

send_sms_client_phone_no()

<<fraudulent transaction>>

Figno 9. Class diagram for Messaging System

Class Diagram For Client Information:



provides client info

creates place info tablecreates item info table

client info


get_info()provid_info()manage_limit()create_place_table()create_item_table()create_amount_table()

creates ammount info table

Figno10. Class Diagram For Client Information



4.2.4 Sequence Diagram

Interaction Diagram

A pattern of interaction among objects is shown on an interaction diagram. Interaction

diagrams come in two forms based on the same underlying information but each

emphasizing a particular aspect of it:

1. Sequence diagrams

2. Collaboration diagrams.

A sequence diagram shows an interaction arranged in time sequence. In particular, it

shows the objects participating in the interaction by their "lifelines" and the messages that

they exchanged arranged in time sequence. It does not show the associations among the

objects.

A collaboration diagram shows an interaction organized around the objects in the

interaction and their links to each other. Unlike a sequence diagram, a collaboration

diagram shows the relationships among the objects. On the other hand, a collaboration

diagram does not show time as a separate dimension, so the sequence of messages and

the concurrent threads must be determined using sequence numbers.



Sequence diagram for credit card fraud detection system:

simulator fraud detection system

Message sending system

generate new transaction

check for fraudulent transaction

if non-fraudulent - save transaction

inform simulator

display transaction

if fraudulent initiate SMS system

send SMS to appropriate card owner

alert fraud

display fraudulent transaction

Figno 14.Sequence diagram for credit card fraud detection system

Collaboration diagram for credit card fraud detection system:

Message sending system

simulator


2: check for fraudulent transaction3: if non-fraudulent - save transaction

5: display transaction8: display fraudulent transaction 9: send SMS to appropriate card owner

1: generate new transaction

4: inform simulator7: alert fraud

6: if fraudulent initiate SMS system



Figno 15. Collaboration diagram for credit card fraud detection system

5.2.5 Activity Diagram

An activity diagram is a special case of a state diagram in which all of the states are

action states and most of the transitions are triggered by completion of the actions in the

source states. The purpose of this diagram is to focus on flows driven by internal

processing. They are useful for showing workflow and parallel processing.

Activity diagram for Credit Card Fraud Detection System:


update entire entity set for card no

display transaction

dislpay transaction information

check for fraud

save transaction

if non-frudulent

send SMS

get Phone no from client information

send sms to the received no

get Phone no from client information

send sms to the received no

If fraudulent

SMS systemfraud detection systemsimulator

Figno 16. Activity diagram for Credit Card Fraud Detection System



5.2.1.6 Deployment Diagram

Deployment diagrams show the configuration of run-time processing elements and the

software components, processes, and objects that live on them. Software component

instances represent run-time manifestations of code units. Components that do not exist

as run-time entities (because they have been compiled away) do not appear on these

diagrams; they should be shown on component diagrams.

Deployment diagram for Credit Card Fraud Detection System:

sql server data base

simulator


message sending system

GSM mobile device

Figno18. Deployment diagram for Credit Card Fraud Detection System



Chapter 3

References



References:

Books:

1) C# programming – Wrox publication

2) C# in 21 days – SAMS publication.

3) Learning C# – by Maurch

4) S. Ghosh and D. L. Reilly "Credit card fraud detection with a neural network", in Proc. 27th Hawaii Int. Conf. Syst. Sci., pp. 621-630. 1994.

Websites:

Credit / Debt Managementhttp://credit.about.com/cs/fraud/

Duncan M D G. 1995. The Future Threat of Credit Card Crime, RCMP Gazette, 57 (10): 25–26.P Chan, W Fan, A Prodromidis & S Stolfo. 1999. Distributed data mining in credit card fraud detection, IEEE Intelligent Systems, 14(6): 67–74. 2001. Fraud Prevention Reference Guide, Anonymous, Certegy, September 2001. Bill Rini. 2002.White Paper on Controlling Online Credit Card Fraud, Window Six, January 2002. http://www.windowsix.com

www.mastercsharp.com

www.wrox.com

www.americanexpress.com

www.mastercard.com

www.visa.com

www.activexperts.com


http://www.activexperts.com/

http://www.visa.com/

http://www.mastercard.com/

http://www.americanexpress.com/

http://www.wrox.com/

http://www.mastercsharp.com/

http://credit.about.com/cs/fraud/


www.microsoft.com

www.forum.nokia.com

www.developer’shome.com

www.orkut.com


http://www.developer'shome.com

http://www.orkut.com/

http://www.forum.nokia.com/

http://www.microsoft.com/

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