Fraud Analytics Course

Fraud analytics combines analytic technology and techniques with human interaction to help detect potential improper transactions, such as those based on fraud and/or bribery, either before the transactions are completed or after they occur. The process of fraud analytics involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. The findings are then translated into insights that can allow a company to manage potential threats before they occur as well as develop a proactive fraud and bribery detection environment. In areas of anti-fraud, anti-bribery, and anti-money laundering, the regulatory environment has tightened. At the same time, fraud, corruption, and abuse are unrelenting—and constantly evolving. It’s a different world out there. And fraud analytics can help make sense of it.
Goals
Adding analytics to this mix can significantly expand fraud detection capabilities, enhancing the “white box” approach of the rules-based method.
Identify hidden patterns, Enhance and extend existing efforts
Use unstructured data to improve organisational processes and efficiency.
Use the Predictive Modelling features for planning
Objective
ML Algorithm trained to Identify concealed observations from any normal observations.
Challenges
Changing Patterns over time.
Imbalance in the Classification of Fraud detection Model.
Time consuming in feature generation.