Analyzing SROs penalties: Are fines proportionate to offence?

Dr. Lokanan used Research and Professional Development funds to research and present a relational database that models and predicts fines imposed by self-regulatory organizations.

The proposed project will employ a relational database to model and predict fines imposed on offenders by self-regulatory organizations (SROs). Using predictive models, I will be able to solve a stochastic and deterministic analytical problem and test for bias in decision making. The data for the project comes from three relational databases: The Investment Dealers Association of Canada (IDA), The Mutual Funds Dealers Association (MFDA), and the Investment Industry Regulatory Organization of Canada (IIROC). The objective is to store the data in a Structured Query Language (SQL) database for scalable analysis. I intend to present the findings at the artificial intelligence summit in Seattle.