Neural Network Classification and its Applications in Insurance Industry


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  • 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
  • Neural Network Classification and its Applications in Insurance Industry

    1. 1. Inderjeet Singh 7667292Department of Computer Science University of Manitoba
    2. 2.  Introduction Classification with Neural Networks a) Advantages b) Disadvantages Performance Studies Application (Insurance Industry) Conclusion References
    3. 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. 4. Advantages (motivations)1. Data driven and self-adaptive2. Universal function approximators3. Non-linear model making, flexible for real world applications4. High accuracy and noise tolerance
    5. 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. 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. 7. Accuracy of rules extracted from The number of rules extractedneural networks (NN) and C4.5 from neural networks (NN) andalgorithm (DT) C4.5 algorithm (DT)
    8. 8. The number of conditions perneural network rule (NN) andC4.5 rule (DT)
    9. 9.  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)
    10. 10. Total of 29 input attributes
    11. 11.  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
    12. 12. Lift Chart: Percentage of policy holders classified for likely termination vsPercentage of policy holders selected from the test dataset
    13. 13.  Scope of improvement in terms of speed of classification Suits the need of many business applications which have lots of data available
    14. 14. 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. 957-9612. David Scuse, Chapter 1 Intro, Class slides, University of Manitoba3. 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. 532-5415. Image: easy-vehicle-insurance-comparability-before-choosing-an- auto