fraud detection using anneecs.csuohio.edu/~sschung/cis601/frauddetectionnn...with simulated...

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