詹姆斯库克代写assignment 保险初期

2020-02-09 03:59

汽车保险公司在发展初期，依靠传统的中介机构和4S店来获取新客户，并以放弃部分利润来支付相关佣金的方式进行报价评估。P2P智能汽车保险正是在机器学习的帮助下应运而生的。根据潜在投保人提供的信息，在线报价系统可以给出合适的报价。系统背后的算法基本上是建立一个决策树的过程，考虑了盈利能力和客户支付能力，优化了定价问题。该系统不仅节省了中间代理的经纪费用，而且节省了谈判和讨价还价的时间成本。尽管该系统简单易用，但自我报告的信息有时可能是虚假的。这时ML的另一个应用程序就开始发挥作用了。Snapshot在2011年成为了以使用为基础的汽车保险计划的一部分。安装在方向盘下的插入式摄像机记录并发回个人驾驶数据，其中大部分数据无法从人口统计信息中获取，因此无法向保险公司提供定制报价。保险诈骗是保险行业普遍存在的问题，其中汽车保险诈骗尤为严重。欺诈不仅会给保险公司造成资金损失，还会间接增加其他投保人的保费，更有甚者，会因为故意事故而危及生命。机器学习确实是更好地检测欺诈行为的游戏规则改变者。例如，Azati在2016年开发的基于人工智能的软件解决方案被汽车保险公司广泛用于调查可疑索赔。该系统能够将可疑率降低50%，并将欺诈检测的准确性提高5倍。通过从历史保险欺诈记录中收集的训练数据，该系统可以识别欺诈索赔的模式，并预测类似的行为。系统还可以通过增加更多的特征和扩大样本量来进一步改进，以提高准确性，找到特征与行为之间的特定相关性，而这是人工检查无法发现的。
詹姆斯库克代写assignment 保险初期
At primary stage of development, automobile insurers relied on traditional intermediary agent and 4S shops to acquire new clients and evaluate quote at the cost of surrendering part of profits to pay for relevant commissions. P2P intelligent car insurance emerged in response to that situation with help of machine learning. Based on information provided by potential policyholders, online quotation system can give out a appropriate quote. Algorithm behind the system which is basically the process of building a decision tree took profitability and customer payment capacity into consideration and optimize the pricing problem. The system can not only save the intermediate agent brokerage but also the time cost of negotiating and bargaining. Despite the ease and simplicity of the system, self-reported information can be spurious from time to time. That’s when another application of ML come into play. Snapshot became part of usage-based car insurance plan of Progressive in 2011. The plug-in camera installed under steer wheel records and sends back individual driving data, most of which can’t be captured from demographic information to insurers for customized quotation.As a prevalent issue in all lines of insurance business, Insurance fraud is especially severe in automobile section. Fraud can not only cause capital loss for insurers, increase insurance premium indirectly for other policyholders, and even worse, put lives in danger because of intentional accident. Machine learning is really the game changer in better detecting fraudulent behavior. For example, the artificial intelligence based software solution developed by Azati in 2016 is widely used by automobile insurers to investigate suspicious claims. The system is able to cut suspicious rate by 50% and increase fraud detect accuracy by five-fold. By training data collected from historical insurance fraud record, the system can identify pattern of fraudulent claims and predict on similar behavior. The system can be further improved by adding more features and enlarging sample size to improve accuracy and find specific correlation between feature and behavior that manual review can’t find out.

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