Development of machine learning and artificial intelligence based tools to improve efficiency in financial services

Our interactions with actors in the financial services industry, including our partner company, uncovered that they possess large amounts of data pertaining to investors and markets, but have yet to extract/learn information of significant value from that data such as expected actions by clients. The industry is conscious of this, but while they are making the needful investments in IT, they report lack of academic expertise in machine learning (ML) / artificial intelligence (AI) to unlock full potentials of such investments. This project will combine academic and industrial expertise to resolve this bottleneck. We will develop ML / AI based tool to allow predictions of actions by client, specifically client churn and to help identify optimal fee structures as well as targeted populations, which Purefacts views as necessary to improve productivity and earning potential. We will also develop descriptors of accuracy of such predictions.The feasibility of the project is assured by deep expertise of each party in respective domains: this applicant’s in applied math, coding and ML, academic supervisor’s in ML methodologies, and Purefacts’ expertise in financial services to individuals and major financial institutions. Methods and tools developed in the project will be applicable to other industries.

Faculty Supervisor:

Sergei Manzhos

Student:

Owen Ren

Partner:

PureFacts

Discipline:

Journalism / Media studies and communication

Sector:

University:

Université du Québec : Institut national de la recherche scientifique

Program: