Cognitive Claims AI

认知主张人工智能

• 批准号: 508821-2017
• 负责人: Pralat, Pawel
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640144
• 关键词: Cognitive; Claims; AI

IMC is developing a tool for the Property & Casualty (P&C) insurance industry. P&C companies are seekingways to minimize their cost of claims (currently running at some 60% of premiums) as well as increase theirrate of fraud detection, which they estimate at 10% of actual fraud.IMC believes it is possible to improve this performance by using available data to predict the behaviour of theclaimant and treat them appropriately. The most useful data would be the statement of claim by the customer,however, as this is in the form of a telephone interview, it is not readily useable for algorithmic modelling.Therefore, IMC is seeking to find a way of turning the content of the statement of claim into data useable forpredictive modelling of the claim outcome. IMC has identified a number of tools for converting naturallanguage data into numerical scores, but the considered prediction problems are non-standard and so requiredevelopment of novel approaches to data modeling. The opportunity to collaborate with Dr. Pralat inresearching the potential for such algorithms will allow IMC to determine if this tool represents a viableopportunity.

IMC正在为财产和意外伤害(P&C)保险行业开发一种工具。P&C公司正在想方设法将索赔成本降至最低(目前约占保费的60%)，同时提高欺诈检测率，他们估计欺诈检测率为实际欺诈的10%。IMC相信，通过使用现有数据来预测受骗者的行为并对其进行适当处理，可以提高这一表现。最有用的数据将是客户的索赔声明，但由于这是电话访谈的形式，它不容易用于算法建模。因此，IMC正在寻求一种方法，将索赔声明的内容转化为可用于对索赔结果进行预测性建模的数据。IMC已经确定了一些将自然语言数据转换为数字分数的工具，但所考虑的预测问题是非标准的，因此需要开发新的数据建模方法。与普拉拉特博士合作研究这类算法的潜力的机会将使IMC能够确定这一工具是否代表着一个可行的机会。