Data-driven prediction of project issues using AI

A considerable percentage of BNC projects are impacted by stoppages, re-adjustments and delays. Such issues are often communicated too late to prevent significant losses or facilitate the reallocation of funding to maximize value creation for the bank and its clients. The majority of delivery metrics (risk, statuses, stakes) that help to anticipate strategic issues and take action are based on the experience of project managers. The democratization of project management platforms such as JIRA now allows the bank to access more precise project data and track project activity. On-time identification of even a small percentage of such projects can facilitate the reallocation of funds and save millions of dollars in funding.
The goal of this project is to use ML to model the probability of project stoppage and delay. The initiative will collect actionable data and key performance metrics from the project management toolset at BNC and utilize such indicators to predict the overrun of the budget and/or schedule of features and alert the right upstream stakeholders to take action.

Faculty Supervisor:

Emad Shihab

Student:

Partner:

Banque Nationale du Canada

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

Concordia University

Program: