حل تشریحی سوال شماره 15 زبان عمومی و تخصصی (انگلیسی)

Directions: Read the following three passages and answer the questions by choosing the best choice (1), (2), (3), or (4). Then mark the correct choice on your answer sheet. PASSAGE 1: Automobile accidents occur with a certain degree of randomness, and it is expected that they will be very difficult to predict on an individual-policy basis. Previous research has shown that an underwriter's ability to predict the actual value of a paid claim is exceedingly difficult, if possible at all. However, a successful system needs only to predict the incident (occurrence) of a loss, not the dollar value. In addition, a successful model would not have to predict each and every accident, as long as the predictions that the model makes are accurate. In fact, a new model needs only to outperform any current models, in order to prove itself worthwhile. As an industry rule-of-thumb, the average loss-to-gross-premium ratio is approximately 60%. The rest of the collected premium is used to pay operating expenses and a small profit of approximately 3%. Thus, if a new model could reduce losses by 1%, it would represent a 33% increase in operating profit. If a corresponding decrease in operating expenses such as loss-adjustment expenses is incurred, the operating profit could be increased by as much as 53%. This in itself is not a justification for using artificial neural networks, but it is enough incentive to try nontraditional techniques. While it is theoretically possible for a computer program to handle the underwriting function, a reliable model has not yet been developed. Aside from the practical development, the human-side must also be considered. It will take time for society, insurance regulators, and the insurance industry to understand and accept a computer- based underwriting model. The development of a computer-based model that might aid the underwriter in the decision making process will be a good first step. An artificial neural network as an underwriter's tool could be used in several ways "to confirm an underwriter's decision; to provide a suggested course of action; or to handle routine policies, allowing the underwriter to spend more time on more complex policies. While there may be many methods of designing such a tool, the author believes that artificial neural networks hold the greatest likelihood of success."