Catch the pattern before it becomes a loss.
Data mining, descriptive analytics, and predictive modeling for detecting fraud and anomalies — a 3-day intensive for teams tired of finding out about fraud after the fact.
Fraud costs companies billions worldwide every year — and it keeps evolving.
Constantly changing technology and trends have driven a dramatic increase in fraud. As a result, the techniques for detecting it keep evolving too, and are now applied across nearly every business field.
This course focuses on using data analytics and current techniques to monitor trends and patterns, in order to detect undesirable behavior and flag fraud before it compounds — not just reconcile the damage afterward.
Five areas, taught as a 3-day intensive program.
Overview of Big Data Analytics in Fraud Management
Where analytics genuinely changes fraud outcomes, and where it's overkill for the problem at hand.
Data Mining Techniques in Fraud Detection
Surface the patterns in large datasets that a manual review process would never catch.
Descriptive Analytics for Fraud Detection
Establish what "normal" actually looks like in your data, so anomalies are easier to spot with confidence.
Predictive Modeling for Fraud Detection
Build models that flag likely fraud before it's confirmed, not after the loss has already happened.
Social Network Analysis for Fraud Detection
Map the relationships between entities to uncover coordinated fraud that looks isolated at the individual level.
Bayesian risk and AML frameworks from a Chief Data Scientist who builds them for real clients.
Ambeone's Chief Data Scientist, Dr. Nishant Das, holds that same role at Marketways Arabia, where Bayesian risk and AML modeling for financial institutions is live consulting work — the frameworks in this course are drawn from that practice.
Pair this with Decision Intelligence with AI to go deeper into the statistical reasoning behind risk decisions.
Explore Decision Intelligence →