Credit Card Fraud Detection Using AdaBoost and Majority Voting

ABSTRACT:

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• S. Panigrahi et.al. proposed a credit card fraud detection system, which consisted of a rule-based Filter, Dumpster_Shafer adder, transaction history database, and Bayesian learner.

• The Dempster_Shafer theory combined various evidential information and created an initial belief, which was used to classify a transaction as normal, suspicious, or abnormal. If a transaction was suspicious, the belief was further evaluated using transaction history from Bayesian learning

DISADVANTAGES OF EXISTING SYSTEM:

• Need a high Computational power

• Poor Classification performances.

• Not easy to process the results.

• Some models suffer a minor reduction in the fraud detection rate up to 1%.

PROPOSED SYSTEM:

• In this paper the AdaBoost and majority voting methods are applied for forming hybrid models. To further evaluate the robustness and reliability of the models, noise is added to the real-world data set. The key contribution of this paper is the evaluation of a variety of machine learning models with a real-world credit card data set for fraud detection.

• While other researchers have used various methods on publicly available data sets, the data set used in this paper are extracted from actual credit card transaction information.

ADVANTAGES OF PROPOSED SYSTEM:

• Easy to understand and implement.

• Efficient use of computational resources.

• Require low computational power.

• Provide optimal results.

MODULES:

• Bank Admin

• View and Authorize Users

• View Chart Results

• Ecommerce User

• End User

MODULES DESCRIPTION:

Bank Admin

In this module, the Admin has to login by using valid user name and password. After login successful he can do some operations such as Bank Admin’s Profile ,View Users and Authorize ,View Ecommerce Website Users and Authorize, Add Bank ,View Bank Details ,View Credit Card Requests, View all Products with rank ,View all Financial Frauds ,View all Financial Frauds with Random Forest Tree With wrong CVV ,View all Financial Frauds with Random Forest Tree with Expired Date Usage ,List Of all Users with Majority of Financial Fraud ,Show Product Rank In Chart ,Show Majority Voting With Wrong CVV Fraud in chart ,Show Majority Voting with Expiry date Usage in chart.

View and Authorize Users

In this module, the admin can view the list of users who all registered. In this, the admin can view the user’s details such as, user name, email, address and admin authorizes the users.

View Chart Results

Show Product Rank in Chart, Show Majority Voting with Wrong CVV Fraud in chart, Show Majority Voting with Expiry date Usage in chart.

Ecommerce User

In this module, there are n numbers of users are present. User should register before doing any operations. Once user registers, their details will be stored to the database. After registration successful, he has to login by using authorized user name and password. Once Login is successful user will do some operations like, Add Category, Add Products, View all Products with rank, and View all Purchased Products with total bill, View All Financial Frauds.

End User

In this module, there are n numbers of users are present. User should register before doing any operations. Once user registers, their details will be stored to the database. After registration successful, he has to login by using authorized user name and password. Once Login is successful user will do some operations like, View My Profile, Manage Bank Account, Request Credit Card, View Credit Card Details, Transfer Money to Your Credit Card Account, Search for Products by Keyword, View all Purchased Products with Total Bill.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System            : Pentium Dual Core.

• Hard Disk       : 120 GB.

• Monitor           : 15’’ LED

• Input Devices  : Keyboard, Mouse

• Ram                 : 1 GB

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7 / 10.

• Coding Language : Python

REFERENCE:

KULDEEP RANDHAWA, CHU KIONG LOO (Senior Member, IEEE), MANJEEVAN SEERA (Senior Member, IEEE), CHEE PENG LIM, AND ASOKE K. NANDI, “Credit Card Fraud Detection Using AdaBoost and Majority Voting”, IEEE Access, 2018.