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Credit card fraud detection using Markova model
Abstract:
Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. 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 this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.
Index Terms: internet, online shopping, credit card, e-commerce security, fraud detection, Hidden Markov Model.
Proposed System:
In this system ,we present a hidden morkov model(HMM) Which does not required fraud signatures and yet is able to detect frauds by considering a cardholder's spending habit. Card transaction processing sequence by the stochastic process of an HMM. The details of items purchased in individual transactions are usually not known to an FDS running at the bank that issues credit cards to the cardholder. Hence, we feel that HMM is an ideal choice for addressing this problem. Another important advantage of the HMM based approach is a drastic reduction is the number reduction in the number of false positives transactions identified a malicious by an FDS although they are actually genuine . An FDS runs at a credit card issuing bank. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and the values if purchases to verify, whether the transaction is genuine or not. The types of goods that are bought in that transaction are not known to the FDS. It tries to find nay anomaly in the transaction based on the spending profile of the cardholder, shipping address , shipping address, and billing addresses.
Advantage:
1. The detection of the fraud use of the card is found much faster that existing system.
2. In case of the existing system even the original card holder is also checked for fraud detection. But in this system no need check the original user as we maintain a log
3. The log which is maintained will also be a proof for the bank for the transaction made.
4. We can find the most accurate detection using this technique.
5. This reduce the tedious work of an employee in bank.
PROBLEM IN EXISTING SYSTEM
In case of the existing system the fraud is detected after the fraud is done that is, the fraud is detected after the complaint of the holder. And so the card holder faced a lot of trouble before the investigation finish. And also as all the transaction is maintained in a log, we need to maintain a huge data, and also now a day's lot of online purchase are made so we don't know the person how is using the card online, we just capture the ip address for verification purpose. So there need a help from the cyber crime to investigate the fraud. To avoid the entire above disadvantage we propose the system to detect the fraud in a best easy way.
Modules:
1. New card
2. Login
3. Security information
4. Transaction
5. Verification
Module Description
New Card:
In this module, the customer gives there information to enroll a new card. The information is all about
There contact details. They can create their own and password for their future use of the card.
Login:
In login form module presents site visitors with a form with username and password fields . if the user enters a valid username and password combination they will be granted access to additional resources on websites .Which additional resources they will have access to can be configured separately.
Security information:
In security information module it will get the information details and its store's in database. If the card lost then the information module form arise .it has a set of question where the user has to answer the correctly to move to the transaction section. It contain informational privacy and informational self-determination are addressed squarely by the invention affording persons and entities a trusted means to user, search, process and exchange personal and /or confidential information
Transaction:
The method and apparatus for pre-authorizing transactions includes providing a communications device to a vendor and credit card owner. The credit owner initiates a credit card transaction by communicating to a credit card number, and storing therein, a distinguishing piece of information that characterizes a specific transaction to be made by an authorized user of the credit card at a later time. The information is accepted as "network date" in the data in the date base only if a correct personal identification code (PIC) used with the communication . The "Network data "will serve to later authorize that specific transaction. The credit card owner or other authorized user can then only make that specific transaction with the credit card. Because transaction pre- authorized, the vendor does need to see or transmit a PIC.
Verification:
Verification information is provided with respect to a transaction between an initiating party and a verification-seeking party, the verification information being given by a third , verifying party, based on confidential information in the possession of the initiating party. In verification the process will seeks card number and if the card number is correct the relevant process will be executed. If the number is wrong, mail will be sent to the user saying the card no has been block and he can't do the further transaction.
Module Input and output:
New card:
Give input -Request from the user for the card.
Expected Output -Assigning an account to requested user.
Login :
Given input- give the security information by answering security questions.
Expected output- Updating of account with the security details.
Verification:
Given input-Checks with user's stored details like security answers or hidden details expected output- if the verification is success user and perform transaction , else blocks the card.
Hardware Requirements
• SYSTEM : Pentium IV 2.4 GHz
• HARD DISK : 40 GB
• FLOPPY DRIVE : 1.44 MB
• MONITOR : 15 VGA colour
• MOUSE : Logitech.
• RAM : 256 MB
• KEYBOARD : 110 keys enhanced.
Software Requirements
• Operating system :- Windows XP Professional
• Front End :- Microsoft Visual Studio .Net 2003
• Coding Language :- Visual C# .Net
• Webtechnology :asp.net
What else can I help you with?
Which hidden markov model algorithm is used for credit card fraud detection?
It's not the Markov algorithm, it is the Luhn Algorithm.
Where can one find more information about hidden Markov models?
The hidden Markov model, which is a statistical Markov model, was developed by L. E. Baum and coworkers. Information about it can be found either online on websites like Wikipedia or in books about mathematics.
What has the author Qiang Miao written?
Qiang Miao has written: 'Application of wavelets and hidden Markov model in condition-based maintenance'
What is h new ( hn)?
The variable "hn" is typically used to represent the new state in a Hidden Markov Model. This state depends on the previous state and the transition probabilities. The new state "hn" is calculated based on a combination of the previous state and the transition probabilities in the model.
What is the difference between artificial neural network and hidden Markov model methods?
An artificial neural network is a structure which will attempt to find a relationship i.e. a function between the inputs, and the provided output(s), in order that when the net be provided with unseen inputs, and according with the recorded internal data (named "weights"), will try to find a correct answer for the new inputs. Hidden Markov models, are used for find the states for which a given stochastic process went through. The main difference could be this: In order to use a markov chain, the process must depend only in it´s last state. For use a neural network, you need a lot of past data. After training process, neural networks are capable of predicting next states of the system based only on the last state. In addition, given the ability to measure the prediction error (for example, after actual event, signal or state has happend and was compared to prediction), the neural network is capable of adapting itself and capture online changes in the undergoing process to improve the model of prediction and decrease the estimation error for the next states. Theoretically such approach can eliminate the need in initial training, as the network started from some random model will eventually adapt itself to the actual process it tries to estimate given this feedback error loop and will start to make correct estimations / predictions after a certain amount of steps. In such setup one can assume that neural network can be used when no past data is available at all. In this case neural network build the model of the ongoing process "from scratch" based on the observations in the "online" mode.
What is the Gauss-Markov theory?
In statistics, the Gauss-Markov theorem states that in a linear regression model in which the errors have expectation zero and are uncorrelated and have equal variances, the best linear unbiased estimator (BLUE) of the coefficients is given by the ordinary least squares (OLS) estimator, provided it exists.
What layer of the OSI model is designed to perform error detection functions?
Data Link Layer
What has the author Richard Charles Curley written?
Richard Charles Curley has written: 'A sonar detection model'
According to the Jo hari window model what are hidden messages?
The Johari window is used as a tool to improve and illustrate self awareness. The hidden area of the model has messages that are things you know about yourself but others do not know.
In which discrete distributions does the probability of a success vary from one trial to the next?
In situations like this it's more common to model the systems as what are called stochastic processes, in this case probably Markov chains.
According to the Johari Window model of the self your hidden self contains?
The hidden self in the Johari Window model represents information about yourself that others are unaware of. This can include deep personal thoughts, feelings, or experiences that you choose not to share with others.
What is the minimum credit score that a person can have?
The minimum credit score a person can have is typically around 300, but this can vary depending on the credit scoring model being used.
