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 How did infinity P&C improve customer service with data mining?

They combated fraud resulting in much happier customers, contributing to a more efficient workflow with improved cycle times, and customer retention due to better claims experience.

 

What were the challenges, the proposed solution, and the obtain results?

Originally they used laminated cards and needed to use them red flag to develop rules quickly. The proposed solution was to create rules for applying claims of damages of at fault drivers. The rules based IBM SPSS solution is well suited to infinity P&C’s  business. The obtained results were the doubled accuracy of its fraud identification, contributing to a good return on investment. Also, created better claims the investment department to analyze.

 

What was their implementation strategy?

Why was it important to produce results as early as possible in data mining studies? The implementation process focused the power predictive analytics [data mining] system SPSS on key areas that could be better. It is important to produce results quickly because, it keeps the company from missing potential valuable subrogation opportunities.

 

The performance of ANN relies heavily on the summation and transformation functions. Explain the combined effects of the summation and transformation functions and how they differ from statistical regression analysis.

The performance of ANN relies heavily on the summation and transformation functions. Explain the combined effects of the summation and transformation functions and how they differ from statistical regression analysis.

 

The summation function computes the weighted sums of all the input elements entering each processing element. In transformational function, the summation function computes the internal stimulation, or activation level, of the neuron.  The transformation (transfer) function combines (i.e., adds up) the inputs coming into a neuron from other neurons/sources and then produces an output based on the choice of the transfer function.

ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.

The general structure of the common neuron is an aggregation function and its transformation through a filter.  ANNs can be universal function approximates for given input—output data. The common neuron structure has summation or product as the aggregation function with linear or nonlinear (sigmoid, radial basis, tangent hyperbolic, etc.) as the threshold function.

 

ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.

 

ANN learning paradigms can be classified as supervised and unsupervised learning. Supervised learning model assumes the availability of a teacher or supervisor who classifies the training examples into classes and utilizes the information on the class membership of each training instance, whereas, unsupervised learning model identify the pattern class information heuristically

A neural net is said to learn supervised, if the desired output is already known.

Example: pattern association

Neural nets that learn unsupervised have no such target outputs.

It can’t be determined what the result of the learning process will look like

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