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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.


  • 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.



  • 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.




  • 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 customer's 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.
  • SYSTEM                    : Pentium IV 2.4 GHz
  • HARD DISK              : 40 GB
  • RAM                           : 256 MB
  • KEYBOARD             : 110 keys enhanced.
  • Operating system        :           Windows 8
  • Front End                    :           JAVA (Applet Viewer)
  • Tool                             :           Eclipse Standard Kepler SR2


    The project titled "CREDIT CARD FRAUD DETECTION USING GENETIC ALGORITHM" detects the fraudulent card during transactions and alerts the customer regarding the fraud. This project also aims in minimizing the number of false alerts. The concept of genetic algorithm is a novel one in this application domain.

    The algorithm begins with multi-population of randomly generated chromosomes. These chromosomes undergo the operations of selection, crossover and mutation. Crossover combines the information from two parent chromosomes to produce new individuals, exploiting the best of the current generation, while mutation or randomly changing some of the parameters allows exploration into other regions of the solution space. Natural selection via a problem specific cost function assures that only the best fit chromosomes remain in the population to mate and produce the next generation. Upon iteration, the genetic algorithm converges to a global solution.


    Student Name


    Ankit Saxena

    Project Leader,Report Making, Data Collection.

    Anjali Kataria

    Critical Value Identification,User GUI.

    Himanshi Jhamb

    Genetic Algorithm, Analysis.

    Mr. Ashish Kumar

    Assistant Professor