Transition model for insurance risks

Car (automobile) insurance is a very common type of insurance: policyholders pay a premium to get financial compensation in case an accident happens with their cars. Insurance companies use complex calculations and a lot of information to determine the value of these premiums. More specifically, they must also consider their expectations of the future. Predicting the future is impossible but with the help of artificial intelligence, the current project aims to improve the understanding of how a portfolio of insured cars can evolve in the upcoming years.

Spatio-Temporal Models for the Analysis of GPS Traces: Application to Road Safety - Year two

The goal of this research is to leverage the telematics data collected in the context of the usage-based insurance program at Intact for road safety improvement. Specifically, we aim to tackle issues on the identification of risky driver behaviors through the characterization of unsafe events and to identify sites on the road network with high probability of collision.

Intact – Analyse et extraction de caractéristiques de voitures à partir d’images

Intact Corporation financière est le plus important fournisseur d'assurances multirisques au Canada en primes annuelles.
Intact vise à offrir un service de réclamations accéléré à ses clients. Au moment d’ouvrir une réclamation, Intact demande d’ores et déjà à ses clients de fournir des images du véhicule qui permettent d’identifier préalablement la condition générale du véhicule. Il sera demandé au client de mettre en évidence la pièce d’équipement endommagée, en s’assurant que celle-ci est bien visible dans l’image.

Simulation-based decision support system for data analytics deployment

Data has been recognized as one of the most valuable assets of modern business. The capacity to gather, store, analyze and interpret data in great quantities can determine to a large degree the ability of a company to achieve goals and adapt to largely volatile environments. This is especially true for financial institutions where data is directly connected to profitability.

Spatio-Temporal Models for the Analysis of GPS Traces: Application to Road Safety

The goal of this research is to leverage the telematics data collected in the context of the usage-based insurance program at Intact for road safety improvement. Specifically, we aim to tackle issues on the identification of risky driver behaviors through the characterization of unsafe events and to identify sites on the road network with high probability of collision.

Intact - Analyse et extraction de caractéristiques de voitures à partir d’images

Intact Corporation financière est le plus important fournisseur d'assurances multirisques au Canada en primes annuelles.
Intact vise à offrir un service de réclamations accéléré à ses clients. Au moment d’ouvrir une réclamation, Intact demande d’ores et déjà à ses clients de fournir des images du véhicule qui permettent d’identifier
préalablement la condition générale du véhicule.

Insurance fraud detection in automobile insurance

We will focus on creating a series of time dependent models for detecting fraudulent claims depending on the level of dynamic information available, and fine tuning these models before testing them with live data and putting the retained models into production. Our objective is to better filter our actual label-claims to form a better control group on which we can train robust classifiers that will detect fraud.

Détection de la fraude à l’assurance

La fraude à l’assurance est devenue un problème important au Canada et dans de nombreux autres pays. Le partenaire n’est pas satisfait des modèles qu’il utilise pour la détection de la fraude dans ses dossiers de réclamations provenant de divers marchés, dont l’assurance automobile. Il a donc décidé d’entreprendre une collaboration entreprise-université afin d’améliorer sa détection de la fraude. Le financement aidera un étudiant de doctorat à réaliser sa recherche en entreprise.