Ann Model for Effects of Socio-Metric-Scales on Life Insurance

Transcription

1 Available ONLINE VSRD-IJCSIT, Vol. 2 (6), 2012, R E S E A R C H A R T II C L E Ann Model for Effects of Socio-Metric-Scales on Life Insurance 1 Krishan Kumar* and 2 DK Pandey ABSTRACT Life never stops, it always goes on. A human being acquires lot of knowledge in his life time and uses it for better understanding of life which is always interesting because of its uncertainty. This uncertainty may be with the resources as well as with the life of a person too. Therefore, humans live in an environment which is probabilistic in nature leading to happening of events- good or bad. It is obvious that Science/Technology and management always try to transform uncertainty into certainty. In ancient days, people used to travel in ships through seas with an element of uncertainty that they would encounter pirates anywhere who would loot them of their all belongings and sometimes their lives were also at risk. Keeping in view all these uncertainties the ship owners had to safeguard their property from such accidents. These and other similar events led to the development of insurance system. In the beginning all goods used to be insured and as the years passed by the risk of human life was also covered by insuring (called life-insurance). A person may have lot of attributes (such as age, number of family members, marital status, responsibility, profession, annual income etc) which are very necessary to decide as to how much valuable his life is implying thereby the more valuable life is the more is the insurance amount needed to cover the uncertainty. Now-a-days there are lot many types of insurance schemes available which are based on different concepts and constraints to cover risks involved with ones children s education, his pension plan, his savings etc. With the advent of advanced computing technology, voluminous data generated by insurance companies, the computer professionals focused on the interpretation of personal and commercial data thus generated and tried to develop expert systems based on Artificial Neural Network (ANN) Technology. AI researchers went further on and used some personal attributes and their corresponding commercial data in training to supervise learning through artificial neural networks. This training was carried till the error (which the difference between evaluated output and the given actual output) became very small. In order to achieve better results AI professionals simulated the results using other sets of input data to find reasonably acceptable error. In the light of this an expert system thus developed is able to predict or simulate the 1 Research Scholar, Department of Computer Science & Engineering, CMJ University, Shillong, Meghalaya, INDIA. 2 Professor, Department of Computer Science & Engineering, UMIT, Ghaziabad, Uttar Pradesh, INDIA. *Correspondence : krishan.chhaya@gmail.com

2 amount of insurance (sum assured) based on given inputs like age, profession, salary etc. All these investigations enabled AI researchers to conclude the process and predict the future commercial aspect of this type of system. In the present investigation our aim is to develop a model, after selecting a combination of critical parameters, by using ANN technique. Keywords : Artificial Neural Network, Socio-Metric, Neurons, Topology. 1. INTRODUCTION Life insurance is a contract under which the insurer (Insurance Company) in consideration of a premium paid undertakes to pay a fixed sum of money on the death of the insured or on the expiry of a specified period of time, whichever is earlier. In case of life insurance, the payment for life insurance policy is certain. The event insured against is sure to happen only the time of its happening is not known. So life insurance is known as Life Assurance. The subject matter of insurance is life of human being. Life insurance provides risk coverage to the life of a person. On death of the person insurance offers protection against loss of income and compensates the titleholders of the policy. 2. RELEVANCY OF THE WORK This project is developed by using the Artificial Neural Network (ANN) technique to create the intelligent system which enables to predict the amount of insurance (sum assured) the people can take insurance for with respect to their personal aspects like age, profession, income group etc. This project is relevant to predict the sum assured after providing input to the AI system. Any insurance company can use it as a tool to get an estimate about the amount of insurance that the person actually deserves with respect to his/her socio-metric parameters like area, age, gender, profession, income group etc. The proposed ANN model provides a safe measure to the company for over-insurance factor which may turn the insurer into an insolvency situation. It in turn favors the customer in many different ways. 3. PREVIOUS WORK In what follow the related work done in the direction of insurance and prediction using neural network is presented. After intensive searching and findings, we found that not much work has been done in the lifeinsurance domain. Even though some research work is done by different scholars in the field of general insurance which is statistical, but less simulation work has been done using Artificial Neural Network Technology. Previous researchers confined the issues regarding insolvency, underwriting (Automated), company evaluation, claim process and insurance loss reserving. These investigations were not focused sharply in life-insurance sector but they gave most emphasis in general insurance in their research papers. 4. NEURAL NETWORK A neural network can be defined as a model of reasoning based on the human brain. The brain consists of a densely interconnected set of nerve cells, or basic information-processing units, called neurons. The human Page 447 of 453

3 brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses, between them. By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today. Although each neuron has a very simple structure, an army of such elements constitutes a tremendous processing power. A neuron consists of a cell body, soma, a number of fibres called dendrites, and a single long fibre called the axon. While dendrites branch into a network around the soma, the axon stretches out to the dendrites and somas of other neurons. Figure: 6.1 is a schematic drawing of a neural network. Biological Neural Network Soma Dendrite Axon Synapse Artificial Neural Network Neuron Input Output Weight 5. RESEARCH METHODOLOGY Methodology is originated from method and it means process or stepwise execution of a task. Any objective cannot be achieved without a good methodology. There may be more than one methodology to achieve the same objective. In this study we have opted for artificial neural network technique, therefore it is very important to describe the methodology used in this project. Raw data is the backbone of the project work because one cannot do anything without this. In addition to data it is also very important to select relevant attributes which play very important role in this investigation. Many of the attributes which are received in the data set may not be important. Some data may be missing in a given set of data; therefore we have to fill those entries in the database. Furthermore, the data set has to be divided into two sets of data; one is for training purpose and the other for simulation. There are three ways through which one can train the network. We have opted the method which takes less time to train and hence save the time. Finally, simulation is the process which concludes the whole work. Simulation tests the trained network against the actual output and shows the network is trained and ready for prediction in respect of the intended objective. Broadly speaking we can categories the methodology in following steps: Page 448 of 453

4 6. DATA ANALYSIS Page 449 of 453

5 7. SELECT THE TOPOLOGY Selection of topology plays very important role in such a project since training is based on network response and response is based on topology. Selection of topology gives impact on fast learning of network to achieve the objective/ goal of error. There may be possibility of long period of learning time taken by network. So in this way we can decide two issues regarding performance of neural network. 8. EXPERIMENTAL RESULTS Page 450 of 453

6 9. NETWORK CONFIGURATION 10. CONCLUSION Fig shows the distribution of absolute errors encountered in simulated process which is the second part of the whole process. As we can see from figure, most of the errors are grouped in a particular area in error distribution pattern, which shows centralized tendency of the network. Clustering or grouping of errors in as small areas as possible is a good characteristic for a predicable network. Thus percentage of grouped members is to play a crucial role in such kind of networks. Page 451 of 453

7 Simulation is done by the 30 input data. In this case 22 errors are within the limit of minimum and maximum standard deviation which is 73% of total errors. If we consider good and bad result analysis, 27 errors are in good category while 3 are in bad category, which works out to 90% and 10% respectively. Therefore, we can say that the developed and the trained neural network have performed very well. It is seen that the developed network has achieved that the objectives with acceptable measurements. 11. APPLICATIONS There are plenty many applications where the concept of this work can be applied. Some of them are:- The Artificial Neural Network derived here is a good tool for prediction of the amount of insurance (sum assured) with respect to six attributes which are used in underwriting of the given application. Insurance company can use this as a tool to filter out over or under insurance cases. In this manner this tool can work as a core supportive tool for insolvency avoiding system. Insurance is a matter of solicitation and there is always a lot of confusion about the prospective amount of insurance which a person wants to take life-insurance. In our society, insurance is resorted to for saving income taxes and to cover risk of life. Many times people suffer from over-insurance with respect to their financial and other social-condition and.ignoring these situations leads to increase in the cases of insurance lapses which should be avoided. This avoidance is better for person who is covered by life-insurance and insurer too. Thus this project can work as a metric tool to guide the customer who is in under-insurance category and for those who are overestimating their insurance. 12. FUTURE SCOPE There are the following future scopes The above researches are used in the insurance sector for the simplicity in the work optimally. Worked as core supportive tool in insurance For data segregation. 13. REFERENCES [1] Ancient world (2008 ), [2] Basic Life Insurance Terms (1996), Life-Insurance-Terms- Everyone-Must-Know [3] Bert Kramer( 1995), The Evaluation of Dutch Non-Life Insurance Companies, A Comparison of an Ordered Logit and a Neural Network Model, Structure, Control and Organization of Primary Processes [4] Concept of Life Insurance (2008), [5] Ernest P. Gross and George S. Vozikis (ECIS 2000 Proceeding), Prediction of Insolvency of Life Insurance through Neural Networks, European Conference on Information Systems (ECIS) Page 452 of 453

8 [6] Frequently Asked Questions (1997), [7] Glossary (1995), [8] Index (1994), [9] Insurance (1996), [10] Insurance Regulatory and Development Authority (1996) [11] Jang-HeeYoo and Byoung-Ho Kang ( ), A Hybrid Approach to Auto-Insurance Claim Processing System, 0=7803=212W9 IEEE [12] Keywords (1995), [13] Life Insurance Corporation of India (2003), [14] Neural Network (1996), [15] Neural networks (2003), [16] New York Life Insurance Company (2008), [17] Ng ShuChiet, SaifulHafizahJaaman, Noriszura Ismail, SitiMariyam and Shamsuddin (2009), Insolvency Prediction Model Using Artificial Neural Network for Malaysian General Insurers, 2009 World Congress on Nature & Biologically Inspired Computing [18] NEGNEVITSKY MICHAEL, Artificial Intelligence-A Guide to Intelligent Systems, second edition, Pearson Education Limited [19] Overview of Insurance Sector in India (2006), [20] Paulo J. G. Lisboa, Terence A. Etchells, Ian H. Jarman, Corneliu T. C. Arsene, M. S. HaneAung, Antonio Eleuteri, Azzam F. G. Taktak, Federico Ambrogi, PatriziaBoracchi, and EliaBiganzoli (September 2009), Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination, IEEE Transactions on neural networks, Vol. 20, No. 9, [21] Perceptron (1997), [22] Peter Mulquiney (2006 ), Artificial Neural Networks in insurance loss reserving [23] The Indian Insurance Industry (1999), [24] Underwriting Rules (1996), [25] Weizhong Yan and Piero P. Bonissone (2006),"Designing a Neural Network Decision System for Automated Insurance Underwriting, International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada. Page 453 of 453