Neural Network Classification and its Applications in Insurance Industry
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This document summarizes the use of neural networks for classification tasks. It discusses the advantages and disadvantages of neural networks for classification. It also presents a case study on using a neural network to classify insurance customers as likely to renew or terminate their policies based on attributes like age and zip code. The neural network achieved higher accuracy than decision trees and regression analysis on the insurance data set.
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Neural Network Classification and its Applications in Insurance Industry
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
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Neural networks are a type of data mining technique inspired by biological neural systems. They are composed of interconnected nodes similar to neurons in the brain. Neural networks can learn patterns from complex data through supervised or unsupervised learning methods. They are widely used for applications like fraud detection, risk assessment, image recognition, and stock market prediction due to their ability to learn from examples without being explicitly programmed.
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Neural network
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each link has a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell : • DENDRITES: Accepts the inputs • SOMA : Process the inputs • AXON : Turns the processed inputs into outputs. • SYNAPSES : The electrochemical contact between the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network are represented by the x1 mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): input signal. Weights (Dendrites): determines the importance of incoming value. Output (Axon): output to other neuron or of NN .
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Neural networks
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Neural network
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each link has a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell : • DENDRITES: Accepts the inputs • SOMA : Process the inputs • AXON : Turns the processed inputs into outputs. • SYNAPSES : The electrochemical contact between the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network are represented by the x1 mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): input signal. Weights (Dendrites): determines the importance of incoming value. Output (Axon): output to other neuron or of NN .
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possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
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Neural Network Classification and its Applications in Insurance Industry
- 1. Inderjeet Singh 7667292 Department of Computer Science University of Manitoba
- 2. Introduction Classification with Neural Networks a) Advantages b) Disadvantages Performance Studies Application (Insurance Industry) Conclusion References
- 3. Neural Networks are models of intelligence that consist of large numbers of simple processing units that collectively are able to perform very complex pattern matching tasks. These models perform stimulus response mapping Classification is the process of learning rules or models from training data to generalize the known structure and then to classify new data with these rules
- 4. Advantages (motivations) 1. Data driven and self-adaptive 2. Universal function approximators 3. Non-linear model making, flexible for real world applications 4. High accuracy and noise tolerance
- 5. Disadvantages (problems) 1. Lack of transparency (black box) 2. Learning time is long (trail and error) 3. Defining classification rules (rule extraction) is difficult
- 6. Comparison of Neural classifier [Lu et al.] and decision tree classifier People database consisting of 9 attributes (age, elevel, zipcode .etc.) and 1 output (Group A or Group B) 3 layer feed forward neural network (38 input units, 6 hidden units and 1 output unit) Tested and compared their approach on 8 classification problems used in earlier researches Func 3
- 7. Accuracy of rules extracted from The number of rules extracted neural networks (NN) and C4.5 from neural networks (NN) and algorithm (DT) C4.5 algorithm (DT)
- 8. The number of conditions per neural network rule (NN) and C4.5 rule (DT)
- 10. Profit and growth Neural networks: Understanding customer retention patterns (renewal or termination) Helps in Predicting likely terminations Direct marketing campaigns Misclassification costs Accuracy is important Helps in Price setting (balanced profit and growth)
- 11. Total of 29 input attributes
- 12. 3 layer feed forward neural network, with hyperbolic tangent activation function and conjugate gradient technique to minimize the error 29 input nodes (attributes), 25 hidden nodes and 1 output node, dataset-20914 motor vehicle policy holders Neural classifier outperformed regression analysis and decision trees
- 13. Lift Chart: Percentage of policy holders classified for likely termination vs Percentage of policy holders selected from the test dataset
- 14. Scope of improvement in terms of speed of classification Suits the need of many business applications which have lots of data available
- 15. 1. Hongjun Lu, Rudy Setiono and, Huan Liu, Effective Data Mining Using Neural Networks, Vol 8, IEEE Transactions on Knowledge and Data Engineering,1996, pp. 957-961 2. David Scuse, Chapter 1 Intro, Class slides, University of Manitoba 3. Wikipedia.com: http://en.wikipedia.org/wiki/Data_mining 4. K.A. Smith, R.J. Willis and M. Brooks, An Analysis of Customer Retention and Insurance Claim Patterns Using Data Mining: A Case Study, The Journal of the Operational Research Society, Vol. 51, May 2000, pp. 532-541 5. Image: http://www.genevievecharest.com/2011/09/26/do-a- easy-vehicle-insurance-comparability-before-choosing-an- auto
Editor's Notes
- conclusion
- Learning takes a lot of passes over the training data, so training time is long (trail and error)Defining classification rules (rule extraction) is difficult due to the complex structure of network and weights learned by branches between nodes