Detecting Anomalies in Fee Schedule Assignments

Fee calculation engine is a tool that is utilized for fee assignment of the investors in financial advisory companies. The assigned fees for the clients can be miscalculated by the advisors. An AI-based monitoring system can reduce the mistakes in the fee calculation by comparing a new assessed fee with the previous assignment. To this end, we aim to build an AL-based anomaly detection system for detecting outliers in the fee evaluation process. This system extracts the patterns in previous fee assessment tasks and compares the new fee assignment duties with these patterns. If the assessed fee value is not compatible with the extracted patterns, the system will set an alarm for subject matter experts to reconsider the calculation and correct the possible mistakes. This system leverages machine learning teqniques and data analytic methods to provide such an anomaly detection mechanism.

Faculty Supervisor:

Rozita Dara

Student:

Partner:

PureFacts

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program: