Application of Artificial Neural Networks in Prediction: A Case Study in Consumer Propensity to Buy Insurance

Transcription

1 Application of Artificial Neural Networks in Prediction: A Case Study in Consumer Propensity to Buy Insurance Rosmayati Mohemad rosmayati@umtedumy Noor Azliza Che Mat azliza@umtedumy Zuriana Abu Bakar zuriana@umtedumy ABSTRACT Insurance is a policy to describe the terms, coverage, premiums and deductibles between two parties concern as individual or companies and insurance company Some amount of money will be paid to the insurance holders when they had faced some potential loss Considering not all people are interested to buy insurance policy, we proposed neural networks architecture to predict data insurance customers who are interested or not interested in buying insurance policy Neural networks model is well known as a flexible mathematical structure and has the ability to generalize patterns in imprecise or noisy and ambiguous input or output data sets In this paper, we proposed Multi- Perceptron (MLP) model for prediction data customers using condition attributes as input to the model Further, the results from the model of MLP neural networks will be pattern to graph actual (target) data and output data In this study, data analysis and usage of neural networks have been made under a Matlab software platform Keywords Artificial Neural Networks, Prediction 1 INTRODUCTION Artificial neural networks is a well known microprocessor architecture in machine learning that has been widely used in analysis, prediction, and classification for example to recognize diseases that involve the lack of iron in blood [1], bankruptcy forecasting [2] and sales forecasting [3] It is built to imitate a human brain s processing capability in solving problems This architecture basically consists of neurons that are aligned in layers, and these layers of neurons are the attribute that gave such networks the ability to learn from its input The result from learning session for a certain amount of time is capable of producing a solution for a given problem, which may be as good as the one produced by a human being Based on the neural networks forecasting capability, we proposed artificial neural networks in prediction of consumer propensity to buy insurance policy Regarding to [4][5], insurance policy is defined as a contract and agreement between two parties either individual or companies with insurance company in terms of equitable transfer of the risk of potential loss Insurance policy could be divided into car insurance, life insurance, health insurance and others According to the report by General Insurance Association of Malaysia, the statistic of consumer propensity in 2005 showed that the general insurance industry continued to register growth in gross direct premium (GDP) which recorded an increase of 99% to reach RM938 billion compared to year 2004 (RM853 billion) [6] Furthermore, research that has been done by [7] stated that the importance of using insurance in order to create trust on the Internet in terms of e-commerce market surveys However, not all people are aware of the insurance s importance Several different factors such as age, duration of the term, purpose of insurance, how much premium could be afforded and saving habit have been identified to influence human behaviour in buying insurance Main objective of this paper is to study on the current artificial neural networks techniques in forecasting Second objective of this paper is to develop artificial neural networks technique for predicting customer who would be interested in buying caravan insurance where we used Multi Perceptron (MLP) networks trained with back propagation algorithm According to [8] and [9], MLP is the most popular static network From 48 studies that examined the use artificial neural networks to produce forecasts, 44 studies (88%) chose to apply back propagation algorithm to train the networks [10] This approach can suffer from non-local weight distribution discussed by [11] Last objective of this paper is to test and evaluate the developed prototype by examining the pattern of customer propensity in buying insurance The multivariate dataset used has been retrieved from [12] where it contains information on customers of an insurance company The data was supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problemthe data consists of 86 variables including product usage data and socio-demographic data The rest of this paper is organized as follow Section 2 discusses related works on artificial neural networks in prediction and forecasting The formulas and algorithms for neural networks are presented in Section 3 and extensive performance evaluation is reported in section 4 Section 5 concludes with a summary of the neural networks technique and results 354

2 2 RELATED RESEARCH In this section, we provide a brief overview of related researches on artificial neural networks in prediction and forecasting and how of these researches correlated in modelling artificial neural networks to predict potential customers to have insurance policy Artificial Neural Networks (ANN) is computational systems whose architecture and operation are inspired from our knowledge about biological neural cells (neurons) in the brain ANN exhibits many advantageous properties for solving complex problem where it can learn from examples and adapt to new situations, generalize from examples, robust and construct solutions quickly with no reliance on domain knowledge There are numerous types of procedures in neural networks applications According to [13], the most popular procedures for different neural networks applications are the back propagation learning algorithm The main idea of the algorithm is based upon the McCulloch-Pitts linear filters with sigmoid output signal function and Rosenblatt back-coupled error correction rules Back propagation algorithm learns by example where it allows the networks to adapt There are many neural networks applications covered in various areas [1] used artificial neural networks in assisting decisionmaking activities in recognizing diseases that involve the lack of iron in blood that can cause even severe damage if not treated appropriately The networks have been trained using sample of blood data with the feed forward algorithm Another research has been done by [2] where neural networks application has been used in bankruptcy forecasting From 24 experiments that have been studied, he summarized that majority of the studies conclude with the superiority of neural networks in forecasting bankruptcy with 14 researches comparing to the traditional statistics tools Feed forward neural networks has been modelled to forecast accuracy of sales data and compared to several Multiplicative Competitive Interaction (MCI) model formulations [3] From the result analysis, they reported that neural networks model has performed better than the MCI models Underpinning to these three related researches that have shown the important role of neural networks in prediction, we modelled neural networks architecture to predict customer who would be interested in buying insurance policy 3 RESEARCH METHODOLOGY In designing or modelling artificial neural networks (ANN), large number of different parameters should be considered, such as dataset, the number of layers, the number of processing elements in each layer, and the types of transfer functions as well as interconnections between networks either feed forward or back propagation [13] In this section, we focus on dataset and neural networks architecture 31 Data sets The dataset used in this paper consists of 5822 customer records Each record consists of 86 attributes with 83 numeric and 2 symbolic input features and the target The target contains the information of whether or not the customers have a caravan insurance policy The input is divided into socio-demographic (43 features) and product ownership data (42 features) There are no missing values in the dataset The dataset is randomly split in two segments, which is training dataset and testing dataset Four different experiments with different distribution ratios between training and testing datasets have been used as follow: i Experiment 1 [4:6]: Training dataset contains of 2329 records with 135 customers purchased a caravan insurance policy and 2194 customers who did not Testing dataset consists of 3493 records with 212 customers purchased a caravan insurance policy and the rest 3281 who did not purchase ii Experiment 2 [5:5]: Training dataset contains of 2911 records with 171 customers purchased a caravan insurance policy and 2740 customers who did not Testing dataset consists of 2911 records with 177 customers purchased a caravan insurance policy and the rest 2734 who did not purchase iii Experiment 3 [6:4]: Training dataset contains of 3493 records with 203 customers purchased a caravan insurance policy and 3290 customers who did not Testing dataset consists of 2329 records with 145 customers purchased a caravan insurance policy and the rest 2184 who did not purchase iv Experiment 4 [8:2]: Training dataset contains of 4658 records with 272 customers purchased a caravan insurance policy and 4386 customers who did not Testing dataset consists of 1164 records with 76 customers purchased a caravan insurance policy and the rest 1088 who did not purchase 32 Neural Networks Architecture Multi Perceptron (MLP) architecture with standard back propagation algorithm is used as depicted in Figure 1 In this research, we have created the neural network, which consists of three layers First layer is the input layer comprises a 1, a 2, a n attributes denoted the attributes or variables According to the structure of the information about the customers, 85 nodes have been used in input layer Second layer is the hidden layer contains 10 hidden nodes and third layer is the output layer where it has one node since we have only one parameter, which we have to determine The output is either 1 or 0 where 1 represents the customer who has purchased the insurance policy and 0 denotes the customer who hasn t The output that has the value equal or greater than 05 is considered as 1 and the value smaller than 05 is considered as 0 There are five transfer functions that have been stated by [13]: linear, step, ramp, sigmoid and Gaussian where each transfer functions maybe with or without bias For the nodes of the input, and hidden layers, two types of sigmoid activation function called logsig and tansig is applied in order to search the best networks For training the networks, five different types of learning algorithms are tested separately in order to find the best learning algorithm in this domain problem They are Gradient descent back propagation (Traingd), Gradient descent with adaptive learning rate back propagation (Traingda), Gradient descent with momentum backpropagation (Traingdm), Gradient descent with momentum and adaptive learning rate back propagation (Traingdx), and Levenberg-Marquardt back propagation (Trainlm) The performance function is mean square error (MSE), which has been used to calculate the differences between the target output and the network output 355

3 a 1 a 2 a n Input Figure 1 Multi Perceptron (MLP) neural networks architecture In MLP architecture that has been developed in the research, random weight is identified normally between range [-05, 05] The output produced after training the network is compared to the actual output The comparison is done by calculating the number of correct data denotes as C over the number of target data, T The formula is shown below: C CorrectPer centage= T Hidden Output X100 Error produced will be reduced by changing the weights The process is repeated until the threshold is met 4 RESULTS ANALYSIS This section discusses about results and the analysis on the experiments that have been tested The discussion will be on learning algorithms and neural networks performance analysis on caravan insurance policy data 41 Result I: Learning Algorithms The best learning algorithm could be found when it produces minimum mean square error (MSE) It also depends on the learning rate parameter (α) and momentum (β) where the best result detained when the value for learning rate is 05 and momentum is 09 respectively Due to the random choice of initial weights and biases that could affect the performance of algorithm, several different sets of initial weights, activation functions and learning algorithms have been tested The result for the best network training is shown in Table 1 where Levenberg-Marquardt back propagation (Trainlm) proved to be best learning algorithm for both tansig and logsig where it reaches the minimum MSE compared to other learning algorithms Table 1 Training result using various learning algorithms Learning Min Square Transfer 1 Transfer 2 Algorithm Error (MSE) Tansig Tansig Trainlm Tansig Tansig Traingd Tansig Tansig Traingda Tansig Tansig Traingdm Tansig Tansig Traingdx Logsig Logsig Trainlm Logsig Logsig Traingd Logsig Logsig Traingda Logsig Logsig Traingdm Logsig Logsig Traingdx Result II: Experiments Analysis As mentioned in the previous section, the experiments to predict the customers who would be interested in buying caravan insurance policy using neural networks have been divided into four different experiments with different distributions of training and testing dataset Since the dataset has been randomly split into training and testing dataset, three different ratios between training:testing (4:6, 5:5, 6:4, 8:2) has been used to strength the results Figure 2 shows the plotting graph for first experiment where the correct percentage data reaches up to 9138 % of accuracy with the numbers of data closer to 1 (purchased) is 130 and the numbers of data closer to 0 (did not) is 3363 Second experiment result is depicted in Figure 3 and the result produced 9052 % of accuracy with 145 data closer to 1 and 2766 data closer to 0 Meanwhile, the result for third experiment shows that the correct percentage data reaches up to 8862% with 156 numbers of data closer to 1 and the numbers of data closer to 0 is 2173 as shown in Figure 4 Figure 5 shows the plotting graph for fourth experiment where the correct percentage reaches up to 9175 % of accuracy with the numbers of data closer to 1 is 44 and the numbers of data closer to 0 is 1120 Figure 2 Plotting graph for experiment 1 356

4 dataset have been used The distribution of 40% of training dataset is sufficient enough to train the network and obtain a good result The extensive results produced as explained in Figure 2 to Figure 5 established the ability of neural network to learn from examples (training dataset) and approximate complex non-linear multivariate data that similar to the characteristics of this insurance policy purchasing problem Figure 3 Plotting graph for experiment 2 Figure 4 Plotting graph for experiment 3 Figure 5 Plotting graph for experiment 4 Based on the results produced for these four experiments, the accuracies vary from 8862% to 9175% The number of accuracy reached for these different experiments still in the range >88% even though different distribution ratios of training and testing 5 CONCLUSION The major conclusion that can be drawn from this research is that, an artificial neural network with back propagation algorithm is a feasible technique for forecasting a customer s propensity to purchase caravan insurance policy The multivariate caravan insurance dataset contains well provided attributes with no interconnection between these attributes Due to its learning ability, neural network is applicable in approximating the unknown pattern to make a future prediction about customer who would be interested in purchasing insurance This is strongly supported by [14] where they have found neural network are best applied to problems whose solution requires knowledge which is difficult to specify but for which there is an abundance of examples The research on customer data in purchasing a caravan insurance policy was completed successfully By showing the impressive results from the experiments, neural network model can be used as a very useful tool or in analyzing customer data in purchasing an insurance policy However, several constraints have been faced during the experimental design and it can be summarized into the number of processing elements in each layer, probabilistic behavior and inseparable training process 6 ACKNOWLEDGEMENT We thank Peter van der Putten and Maarten van Someren of Sentient Machine Research for provision of the Insurance Company Benchmark (COIL 2000) dataset used in this study 7 REFERENCES [1] Walid, S, 2001 Aplikasi rangkaian neural buatan: Pencaman Sel Darah Merah Hipokromik (in Malay) Universiti Malaya, Kuala Lumpur, Malaysia, pp 1-19 [2] Perez, M, 2006 Neural Networks Applications in Bankruptcy Forecasting: A State of the Art Journal Neural Computing and Applications, Vol 15, pp [3] Gruca, T S, Klemz, B R, & Petersen, E A F, 1999 Mining Sales Data Using a Neural Network Model of Market Response ACM SIGKDD Explorations Newsletter, Vol 1, pp [4] Investorwordcom, 2006 Retrieved from [October 2006] [5] Wikipedia, 2006 Retrieved from [October 2006] [6] Tang, F F, Thom, M G, Wang, L T, Tan, J C, Chow, W Y, & Tang, Z, 2003 Using Insurance to Create Trust on the Internet Communication of the ACM, Vol 46, pp

5 [7] General Insurance Association of Malaysia, 2006 Retrieved from [October 2006] [8] S Haykin, 1999 Neural network: A comprehensive foundation, Prentice Hall, Upper Saddle River, New Jersey [9] Principe, J, Euliano, N R, & Lefebvre, W C, 1999 Neural and Adaptive Systems: Fundamentals through Simulations, John Wiley & Sons, New York [10] Adya, M, & Collopy, F, 1998 How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation Journal of Forecasting, Vol 17, pp [11] Eldredge, J G, & Hutchings, B L, 1994 RRANN: A Hardware Implementation of the Back propagation Algorithm Using Reconfigurable FPGAs Custom Integrated Circuits Conference, pp 77-80, San Diego, California [12] The Insurance Company Benchmark (COIL 2000), 2006 Retrieved from [October 2006] [13] Erenshteyn, R, Foulds, R, & Galuska, S, 1994 Is Designing a Neural Network Application an Art or a Science? ACM SIGCHI Bulletin, Vol 26, pp [14] Sheppard, C P, & Gent, C R, 1991 A Neural Network Based Sonar Classification System Proceeding of the Europe 1991 MILCOMP Conference: Military Computer Systems and Software, pp