Anomaly Detection in Financial Data

In this joint collaboration with Scotiabank we hope to solve a commonly faced problem by large financial institutions. It is to detect errors in financial datasets. This could be due to typing errors made by a human or a computer glitch that causes an incorrect value to be stored. To identify these errors, we plan to build an error detection system. It will model how financial variables change in relation to other variables. This will help us identify groups of variables that move, through time, in a similar manner. With this knowledge we will then be able to spot errors in the data. This system will enable the bank to build trading models that are not affected by these errors and reduce the time taken to identify them.

Intern: 
Abdullah Farouk
Faculty Supervisor: 
Natalia Nolde
Project Year: 
2018
Province: 
British Columbia
Partner: 
Program: