英国诺丁汉特伦特论文代写 自愿性市场

Keywords:英国诺丁汉特伦特论文代写 自愿性市场

作为自愿性市场的车险机构之一，GEICO从1936年开始专门为政府官员提供车险服务，并不断扩大客户基础。现在它是美国第三大汽车保险公司，主要通过互联网和电话提供服务。为了弥补缺少面对面的代理来建立客户代表关系等不足，GEICO不断完善其智能定价、在线服务和理赔系统。这就是为什么我们选择GEICO来进一步分析使用ML技术的工具如何应用于其业务，并在未来更好地为公司服务。第一个问题来了:如何从成吨的索赔中准确地挑出骗子?在汽车保险行业，欺诈一直是一个关键问题。根据保险研究委员会的数据，2012年，汽车索赔欺诈和累积在美国增加了56亿到77亿美元的超额支付(Corum,2015)。作为美国主要的保险公司在美国，Geico也是欺诈的巨大受害者。这种模式的用户将是Geico的汽车理算员，他们的责任是决定是否解决索赔。他们的目标是在这个模型的帮助下，有效地识别真实索赔中的欺诈行为。根据Geico的工作描述，理算员应具有高中学历(Geico)。我们推断大多数用户可能对我们工具背后的机器学习一无所知。该工具的目标是完美地识别欺诈，这符合用户的目标。考虑到用户的技术背景，本工具的输入将包括提前收集的投保人信息、索赔描述、当天天气、该地区当时的交通状况以及其他需要的信息。只需手动输入索赔的描述，其他信息将在分析输入字符串后自动从数据库中进行匹配。
英国诺丁汉特伦特论文代写 自愿性市场
As one of the auto insurance agencies in voluntary market, GEICO started its business of offering auto insurance in 1936 exclusively for government officers and then expanded customer base. Now it is the third largest auto insurance writer in US and primarily serves via internet and telephone. To make up for deficiencies such as lack of face-to-face agent to build up customer-representative relationship, GEICO continuously put effort into improving its intelligent pricing, online service and claim settlement system. That’s why we choose GEICO for further analysis of how tools using ML technology is applied in its business and better serve the company in the future.Here comes the first question: how to pick frauds out of tons of claims accurately? Fraud has been a critical issue in car insurance industry. According to Insurance Research Council, automobile claim fraud and buildup added $5.6 billion -$7.7 billion in excess payments paid in the U.S. in 2012(Corum,2015). As a major insurance provider in the U.S., Geico is a huge victim of frauds as well.The users for this model would be Geico’s automobile adjusters whose responsibility is to determine whether to settle claims. Their goal is to identify frauds from real claims efficiently and effectively with the help of this model. According to Geico’s job description, adjusters should hold high school education(Geico). We infer that most users may know nothing about machine learning behind our tool.The goal of the tool is to perfectly identify fraud, which aligns with goal of users. Considering users technical background, the input of the tool will be the information of policyholder which was collected in advance, the description of the claim, the weather of the day, the traffic of that time at that area, and other needed information. Only the description of the claim needs to be entered manually, other information would be matched up from the database automatically after analyzing the input string.