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.