Active learning for automatic generation of narratives from numeric financial and supply chain data

One of the major responsibilities of financial and business analysts is to generate narrative reports summarizing business trends based on time series data. As it stands, this analysis is typically done manually, requiring analysts to pour over huge amounts of data looking to understand trends, and performing ad hoc analyses to establish relationships between data streams. As such, it is currently a high touch process and even with the many analysts currently employed, important trends can go undetected for long periods of time. As in other fields, a push toward automation has the potential to increase efficiency in the financial and business analysis sector, increasing the speed of analysis, decreasing error rates, and freeing analysts from low-level tasks to allow them to focus on high-level synthesis. One of the challenges in automating financial and business analysis tasks is that analysts tend to rely on years of experience and domain specific knowledge to achieve good results. Thus they rely on a “Gestalt” to lead their analysis, which is difficult to replicate using a pure rule-based system. TBC

Faculty Supervisor:

Ayse Basar Bener

Student:

Partner:

Unilever Canada Inc

Discipline:

Engineering

Sector:

Manufacturing; Wholesale trade

University:

Toronto Metropolitan University

Program: