fraud detection using anneecs.csuohio.edu/~sschung/cis601/frauddetectionnn...with simulated...
TRANSCRIPT
FRAUD DETECTION USING
NEURAL NETWORKS
SAIYAM
KOHLI
(2669077)
AGENDA
Types of Credit Card Fraud?
What is Artificial neural network?
SIMULATED ANNEALING
TRAINING OF ANN
RESULTS
CONCLUSION
What is Credit Card Fraud?
TECHNIQUE FOR FRAUD DETECTION
1)SUPERVISED: Use training data to build the models which we have attribute
of class label.
2)UNSPERVISED: Training data does not have class label.
WHAT IS NEURAL NETWORK?
WHAT IS NEURAL NETWORK?
Similar functionality like human brain.
Consist of artificial neurons which can be viewed as set of nodes in a network.
Application in business failure prediction, stock price prediction , credit card
fraud detection and many more area.
FEED FORWARD NEURAL NETWORK
SIMPLE FEED-FORWARD NETWORK
PERCEPTRON FUNCTION IN NEURAL
NETWORK
INPUT FUNCTIONS: Collects all input and perform summation and transfer to
activation function.
ACTIVATION FUNCTIONS : Perform some operation on the result after
summation and transfer to the next level.
VISUALIZATION OF FUNCTIONS
EVALUATION OF SUMMATION
FUNCTIONS
ACTIVATION FUNCTIONS
Result of summation function is passed to activation function ,which will scale
the value of S in a proper range.
Two types of Activation Function:
1)Sigmond Function: Works on threshold ,if the value of S crosses the threshold
then the node is pass as an output.
2)Hyberbolic Tangent Function:Next version of sigmoid function
SIGMOND FUNCTION
REPRESENTATION:
Hyperbolic Tangent activation function
REPRESENTATION:
ANNEALING
Annealing is a thermal process for obtaining low energy states of a solid in a heat bath.
The process contains three steps:
1. Heat the system at high temperature T and generate a random solution.
2. As the algorithm progress, T decreases at each iteration and each iteration
forms a nearby model.
3. Then cool the system slowly until the minimum value of T is reached and
generate a model at each iteration, which takes the system towards global
minima.
PROCEDURE OF SIMULATED ANNEALING
The main definitions which is needed for this algorithm are:
a method is to generate initial solution, by generating worst solution at the
beginning helps to avoid converging to local minimum
Perturbation Function to find a next solution with whom the current solution is
compared.
an Objective Function is to be defined to evaluate and rate the current solution
on the basis of performance,
an Acceptance Function, which is used to check whether the current solution is
good or not in comparison with the current one, a very basic one is
exp((currentSol-nextSol)/currentTemp).
the last one is stopping criteria, there are many stopping criteria’s, in this paper
we have used an threshold value of objective function as an stopping criteria.
TRAINING OF ANN
CALCULATIONS
ANNEALING ALGORITHM
RANDOMIZATION OF WEIGHTS
RESULTS SET
PARAMETERS OF ARTIFICIAL NEURAL
NETWORKS
PARAMETERS OF SIMULATED ANNEALING
ALGORITHM
RESULT OF TRAINED NEURAL NETWORKS
CONCLUSION
In this paper we showed that better result is achieved with ANN when trained
with simulated annealing algorithm. As the result shows that the training time
is high but the fraud detection in real time is considerably low and the
probability of predicting the fraud case correctly in online transaction is high,
which is a main measure to evaluate any ANN.
The main problem in credit card fraud detection is the availability of real
world data for the experiment.
This approach can also be used in other applications which require
classification task [20] e.g. software failure prediction, etc
THANK YOU