Reference number : GC-2017-Q1-00403, amendment number : , agreement type : G, recipient legal name : Pralat, Pawel (Ryerson University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : IMC is developing a tool for the Property & Casualty (P&C) insurance industry. P&C companies are seeking_x000D_
ways to minimize their cost of claims (currently running at some 60% of premiums) as well as increase their_x000D_
rate of fraud detection, which they estimate at 10% of actual fraud._x000D_
IMC believes it is possible to improve this performance by using available data to predict the behaviour of the_x000D_
claimant and treat them appropriately. The most useful data would be the statement of claim by the customer,_x000D_
however, as this is in the form of a telephone interview, it is not readily useable for algorithmic modelling._x000D_
Therefore, IMC is seeking to find a way of turning the content of the statement of claim into data useable for_x000D_
predictive modelling of the claim outcome. IMC has identified a number of tools for converting natural_x000D_
language data into numerical scores, but the considered prediction problems are non-standard and so require_x000D_
development of novel approaches to data modeling. The opportunity to collaborate with Dr. Pralat in_x000D_
researching the potential for such algorithms will allow IMC to determine if this tool represents a viable_x000D_
opportunity., program purpose french : IMC is developing a tool for the Property & Casualty (P&C) insurance industry. P&C companies are seeking_x000D_
ways to minimize their cost of claims (currently running at some 60% of premiums) as well as increase their_x000D_
rate of fraud detection, which they estimate at 10% of actual fraud._x000D_
IMC believes it is possible to improve this performance by using available data to predict the behaviour of the_x000D_
claimant and treat them appropriately. The most useful data would be the statement of claim by the customer,_x000D_
however, as this is in the form of a telephone interview, it is not readily useable for algorithmic modelling._x000D_
Therefore, IMC is seeking to find a way of turning the content of the statement of claim into data useable for_x000D_
predictive modelling of the claim outcome. IMC has identified a number of tools for converting natural_x000D_
language data into numerical scores, but the considered prediction problems are non-standard and so require_x000D_
development of novel approaches to data modeling. The opportunity to collaborate with Dr. Pralat in_x000D_
researching the potential for such algorithms will allow IMC to determine if this tool represents a viable_x000D_
opportunity., agreement title english : Cognitive Claims AI, agreement title french : Cognitive Claims AI, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada