Anomaly detection from system logs through deep learning

During the last decade, we observe in organizations a surge of numbers of cyber-attacks originating internally. In this project, we aim to develop deep learning models to detect suspicious activity (such as malicious events, system failure or attacks) from log data generated by the Desjardins ecosystem.

Intact : Extraction et vérification de faits

L’un des enjeux majeurs dans le domaine de l’assurance est la fraude. Une étude américaine proposait il y a quelques années que celle-ci représentait environ 10% de chaque dollar que paie une compagnie d’assurance en dédommagement. L’incidence de la fraude est importante pour Intact sur ses résultats financiers, mais également pour l’ensemble de ses clients, car le coût de la fraude se retrouve payé dans chacune des primes d’assurance. Intact traite environ 500 000 réclamations en automobile et 150 000 réclamations en habitation chaque année.

Consolidating High-Frequency and Textual Data for Optimal Anomaly Detection in Derivative Markets

In the last few years, a high increase in the interest of traders and investors towards financial instruments directly led to an important augmentation of the information received daily by exchanges. Exchange regulators, who constantly monitor markets to unveil potential infractions, traditionally perform their investigation manually and the notable growth in market activity represents an important risk of fraudulent events going unnoticed. In response to that new reality, exchanges around the globe are establishing automated surveillance systems that track market activity.

Reinforcement Learning based Graph Convolutional Recommender Systems

This project aims to use and experiment deep learning technique on modern recommender systems such as Graph Convolutional Network. The purpose of this implementation will be to drastically improve recommendation structure’s benchmark. This will allow extract user’s embedding by mapping from pre-existing features that describe the user such as ID and relevant attributes.In this project students will be integrated as a member of the advanced analytics research team that includes multiple PhD holders in relevant domains.Students would work on the following main topics: 1.

Co-operators : Création de plongements et représentations pour l'assurance commerciale

En assurance commerciale, il est important de pouvoir comprendre l'industrie dans laquelle opère une entreprise afin de bien identifier les risques auxquels l’entreprise est exposée. À cette fin, l'approche traditionnelle consiste à assigner à chaque entreprise un code d'industrie. Cependant, cette assignation est problématique car la plupart des classifications d'industrie comptent des centaines et souvent même des milliers de classes.

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.

Multimodal Representation Learning from raw data to detect customers emotional state in the financial industry

Currently, call centres effort in this matter is largely reactive. Someone calls in, they are upset, and agents respond accordingly. However, this approach is not always most effective, especially with difficult customers. Therefore, knowing the customers current emotional state is very important for appropriate problem solving.

Developing a trading strategy for Bitcoin Market using Long Short-term Memory (LSTM) architecture

Prediction of financial markets time series market direction is a challenging task mainly due to the unprecedented changes in economic trends and conditions in one hand and incomplete information on the other hand. Therefore, developing different forecasting, like LSTMs, have been employed by quantitative traders recently.Long Short Term Memory networks (LSTMs”) – are a special kind of recurrent neural network (RNN), capable of learning long-term dependencies.

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.

Former des capacités organisationnelles les technologies émergentes : Le cas de Desjardins et l'intelligence artificielle

L’intelligence artificielle suscite beaucoup d’intérêt de la part des grandes entreprises et Desjardins ne fait pas exception. Cependant, les organisations confrontées à la nature ambiguë et incertaine de cette technologie émergente ont généralement de la difficulté à la saisir et l’intégrer dans leurs pratiques et processus. Dans cette optique, ce stage tente d’investiguer les mécanismes par lesquels une organisation peut faciliter la construction d’un cadre commun afin diriger l’action collective envers l’intégration d’une technologie émergente telle que l’intelligence artificielle.

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