Credit Card Fraud Detection System using Genetic Algorithm

ABSTRACT:

Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. 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. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM

The Traditional detection method mainly depends on database system and the education of customers, which usually are delayed, inaccurate and not in-time.
After that methods based on discriminate analysis and regression analysis are widely used which can detect fraud by credit rate for cardholders and credit card transaction.
For a large amount of data it is not efficient.

PROBLEM RECOGNITION

The high amount of losses due to fraud and the awareness of the relation between loss and the available limit have to be reduced.
The fraud has to be deducted in real time and the number of false alert has to be minimized.

PROPOSED SYSTEM

The proposed system overcomes the above mentioned issue in an efficient way. Using genetic algorithm the fraud is detected and the false alert is minimized and it produces an optimized result.
The fraud is detected based on the customers behavior. A new classification problem which has a variable misclassification cost is introduced.
Here the genetic algorithms is made where a set of interval valued parameters are optimized.

MODULES

User GUI
Critical Value Identification
Fraud Detection using Genetic Algorithm

MODULES DESCRIPTION

User GUI

In this module, User Interface module is developed using Applet Viewer. This module is developed to user to identify the credit card fraud using genetic algorithm technique. So the user interface must be capable of providing the user to upload the dataset and make manipulations and finally must show the user whether fraud has been detected or not. Only final output will be in applet screen. All the generation details (crossover and mutation) will b in the console screen of eclipse.

Critical Value Identification

Based on CC usage Frequency

float ccfreq =Float.valueOf(temp[3])/Float.valueOf(temp[6]);

if(ccfreq>0.2)

{

if(Float.valueOf(temp[7])>(5*ccfreq))

{

res[0]=1;

res[1]=(Float.valueOf(temp[7])*ccfreq);

}

if(res[0]<1)

{

res[1]=(float)ccfreq;

}

Ccfreq = Total number card used (CU) / CC age

If ccfreq is less than 0.2 , it means this property is not applicable for fraud and
critical value =ccfreq

Otherwise, it check for condition of fraud (i.e) =

Fraud condition = number of time Card used Today (CUT) >( 5 * ccfreq)

If true, there may chance for fraud using this property and its critical value is CUT*ccfreq

If flase, no fraud occurance and critical value =ccfreq

Based on CC usage Location

int loc=Integer.valueOf(temp[8]);

if((loc<= 5) && (Integer.valueOf(temp[9])>( 2 * loc)))

{

res[0]=1;

res[1]=(Float.valueOf(loc)/ Float.valueOf(temp[9]));

}

if(res[0]<1)

{

res[1]=(float)0.01;

}

Number of locations CC used so far (loc) obtained from dataset(loc)

If loc is less than 5, it means this property is not applicable for fraud and critical value =0.01

Otherwise, it check for condition of fraud (i.e) =

Fraud condition = number of locations Card used Today (CUT) >( 5 * loc)

If true, there may chance for fraud using this property and its critical value is loc/CUT

If flase, no fraud occurance and critical value =0.01

Based on CC OverDraft

float od =Float.valueOf(temp[5])/Float.valueOf(temp[3]);

if(od<=0.2)

{

if(Float.valueOf(temp[10])>=1)

{

res[0]=1;

res[1]=(Float.valueOf(temp[10])*od);

}

if(res[0]<1)

{

res[1]=(float)od;

}

Number of times CC overdraft w.r.t CU occurred so far (od) can be found as,

Od w.r.t CU = OD/CU

If Od w.r.t CU is less than 0.02, it means this property is not applicable for fraud and critical value = Od w.r.t CU

Otherwise, it check for condition of fraud (i.e) =

Fraud condition = check whether overdraft condition occurred today from (ODT dataset)

If true, there may chance for fraud using this property and its critical value is ODT * Od w.r.t CU

If flase, no fraud occurance and critical value = Od w.r.t CU

Based on CC Book Balance

float bb =Float.valueOf(temp[2])/Float.valueOf(temp[4]);

if(bb<=0.25)

{

res[0]=1;

res[1]=(Float.valueOf(2)*bb);

}

if(res[0]<1)

{

res[1]=(float)bb;

}

Standard Book balance can be found as,

Bb = current BB / Avg. BB

If bb is less or equals than 0.25, it means this property is not applicable for fraud and critical value = bb

Otherwise, it check for condition of fraud (i.e) =

If true, there may chance for fraud using this property and its critical value is currBB * bb

If flase, no fraud occurance and critical value = bb

Based on CC Average Daily Spending

float mon= Float.valueOf(temp[6])/30;

float bal= 100000 – Float.valueOf(temp[4]);

float tot = mon*bal;

float ds =tot/Float.valueOf(temp[6]);

if((10*ds)<Float.valueOf(temp[11]))

{

res[0]=1;

if(Float.valueOf(temp[11])>0)

res[1]=(Float.valueOf(temp[11])/ (10*ds));

else

res[1]=(float) 0.0;

}

if(res[0]<1)

{

res[1]=(float)0.01;

}

Fraud Detection using Genetic Algorithm

In this module the system must detect whether any fraud has been occurred in the transaction or not. It must also display the user about the result. It is calculated based on following:

Age of CC in months can be calculated using CCage (from dataset) by,

Age of cc by month = CCage/30

Total money being spent from the available limit (1 lakh _ 100000)

Bal = 100000 – avg BB

So, total money spent can be found as,

Tot = Age of cc by month * Bal

Total money spent on each month can be calculated as,

Ds=tot* Age of cc by month

it check for condition of fraud (i.e) =

Fraud condition = (10 * ds) is amount spent today (AmtT in dataset)

If true, there may chance for fraud using this property and its critical value is AmtT/(10*ds)

If flase, no fraud occurance and critical value 0.01

HARDWARE REQUIREMENTS

SYSTEM : Pentium IV 2.4 GHz
HARD DISK : 40 GB
MONITOR : 15 VGA colour
MOUSE : Logitech.
RAM : 256 MB
KEYBOARD : 110 keys enhanced.

SOFTWARE REQUIREMENTS

Operating system : Windows XP Professional / 7 /10
Front End : JAVA
Tool : NetBeans IDE

Java Projects

Credit Card Fraud Detection System using Genetic Algorithm