Grant: CCI Applied Research and Development Grants
Collège de Bois-de-Boulogne
$150,000.00
Development of a data-driven engine for the private investment sector
EF 2022-23
Résumé
The enthusiasm for entrepreneurship has significantly increased in recent years in the high technology sector. As a result, the many innovative projects that are in the early stages of their life cycle sometimes need refinement both in terms of the value proposition they suggest and the structure of the company that supports them. It is in this perspective that private investment firms and accelerators like Holt play a critical role. They support start-up companies in defining their business model, tracing the technological roadmap and designing a marketing plan. However, given the premature nature of their intervention and the high level of risk of the investment, better control over the entire investment decision process is required: from the discovery of startups with high potential to the allocation of funds to these companies. Briefly, what the project aims to achieve is the development of a solution for venture capital firms, VC as a Service (or VCaaS), that would significantly improve the investment decision process of its users through a data-driven approach. This involves, among other things, establishing a portrait of companies by identifying all the factors that could have an impact on the quality of the investment. One of the important factors in this process is the quality and objectivity with which a startup's potential is assessed within its technological and economic context. This requires specialized experience and skills in the field of expertise of the company. Indeed, this assessment is carried out by investment experts (referred to as advisors), selected from a pool of collaborators with diverse profiles. The right match between a startup and advisor is therefore crucial for the success of an investment portfolio. Optimizing this process through data science requires the use of advanced artificial intelligence techniques in the fields of information retrieval systems, recommendation systems, natural language processing, supervised learning, as well as reinforcement learning.
Programme
CCI Applied Research and Development Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
CCARD-2022-00246
Name nom
Malette, Marie-Eve
Department département
Head Office/Siège social
Organization id
2437
Institution établissement
Collège de Bois-de-Boulogne
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
150000
Program id
CCARD
Program name en
CCI Applied Research and Development Grants
Program name fr
ICC Subventions de recherche et développement appliquée
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
97
Committee name en
Scholarships and Fellowships Internal Decision Committee
Committee name fr
Comité de décision interne - Bourses
Area of application code
9999
Area of application group en
Not available
Area of application group fr
Non disponible
Area of application en
Not available
Area of application fr
Non disponible
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Non disponible
Research subject en
Not available
Research subject fr
Non disponible
Installment
inst:1
Partie
1
Nb partie
3
Application title
Development of a data-driven engine for the private investment sector
Application summary
The enthusiasm for entrepreneurship has significantly increased in recent years in the high technology sector. As a result, the many innovative projects that are in the early stages of their life cycle sometimes need refinement both in terms of the value proposition they suggest and the structure of the company that supports them. It is in this perspective that private investment firms and accelerators like Holt play a critical role. They support start-up companies in defining their business model, tracing the technological roadmap and designing a marketing plan. However, given the premature nature of their intervention and the high level of risk of the investment, better control over the entire investment decision process is required: from the discovery of startups with high potential to the allocation of funds to these companies. Briefly, what the project aims to achieve is the development of a solution for venture capital firms, VC as a Service (or VCaaS), that would significantly improve the investment decision process of its users through a data-driven approach. This involves, among other things, establishing a portrait of companies by identifying all the factors that could have an impact on the quality of the investment. One of the important factors in this process is the quality and objectivity with which a startup's potential is assessed within its technological and economic context. This requires specialized experience and skills in the field of expertise of the company. Indeed, this assessment is carried out by investment experts (referred to as advisors), selected from a pool of collaborators with diverse profiles. The right match between a startup and advisor is therefore crucial for the success of an investment portfolio. Optimizing this process through data science requires the use of advanced artificial intelligence techniques in the fields of information retrieval systems, recommendation systems, natural language processing, supervised learning, as well as reinforcement learning.