About CRIM

R&D mandate for a digital transition at Croesus

Context: Croesus is a Quebec-based company with more than 180 employees that offers portfolio management and business intelligence products to financial institutions such as CIBC and TD Bank Financial Group, and to companies such as Industrial Alliance or IBM. Croesus has maintained a history of investment data that covers more than 30 years, and the variety and quantity of queries on this database continues to grow. The company’s IT executives and directors realized that, given the technical limitations of their database, such increases could ultimately hinder the growth of services. Croesus, and more specifically its software development division, turned to CRIM to assist it to implement an R&D project that ultimately led the company towards a profound IT transition. The goal of the project was to identify the best database management system (DBMS) available to process big data and develop a road map for its implementation.

ProjectCroesus turned to CRIM for help in selecting the database management system (DBMS) that best suited their needs and in planning its implementation. CRIM has enabled the company to make this important transition without major interruptions in its activities. CRIM’s transparent and disinterested approach also appealed to Croesus, which has become a supporter of open innovation.


Upcoming event

  • Gala des Prix Innovation 2020 de l'ADRIQ
    19 November 2020 0:00
    Palais des Congrès de Montréal
    Le Gala Prix Innovation 2020 de l'ADRIQ aura lieu le 19 novembre 2020, au Palais des congrès de Montréal.
  • #Conference 3 CRIM experts will take part in the 37th International Conference on Machine Learning (ICML) online th… https://t.co/4okSz705x0
  • Économie Québec RT @economie_quebec: [Communiqué de presse] Québec implante un nouveau modèle pour valoriser davantage la #recherche publique ?? https://t.…

Recent Publications

  • On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification

  • An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers