What is a Fraud Intelligence Platform?

How a fraud intelligence platform works

It analyzes user behavior, measures the direct and indirect impact on fraud and revenue, scores revenue risk by segment, and uses what-if scenarios to estimate how reducing risky behavior protects revenue. Every result is written up in plain English, so a risk or payments team can read the findings, the confidence behind them, and the recommended actions without a statistics background.

Fraud detection software vs. fraud intelligence

Most fraud detection software answers one question: is this transaction fraudulent? It scores each event, blocks or flags it, and moves on. Fraud intelligence answers the questions that follow. Which behaviors lead to fraud in the first place? How much revenue is exposed? Which change would cut the most fraud with the least disruption to legitimate customers? Averisys Fraud Intelligence Platform is built for those questions, so it complements the fraud detection software already screening transactions rather than replacing that screening step.

Fraud detection solutions and fraud prevention solutions

Fraud detection solutions find fraud that has already been attempted. Fraud prevention solutions try to stop it at the door, usually with rules, blocklists, and step-up verification. Both work on the transaction. Neither explains the pattern behind a rising chargeback rate, nor what a fix is worth. A fraud intelligence platform sits above both: it ranks the behaviors driving fraud, estimates the revenue tied to each one, and orders the actions by the revenue they protect.

AI fraud detection and fraud detection machine learning

AI fraud detection and fraud detection machine learning describe the modeling techniques — classifiers, gradient boosting, anomaly detection — used to separate fraud from legitimate activity. Averisys uses machine learning as well, including Recursive Feature Machines to rank what drives fraud, LightGBM to forecast revenue, Random Survival Forests to predict when an event is likely, and Neural Structural Equation Modeling to trace how behavior reaches fraud and revenue. The difference is what the models are pointed at: not only the fraud flag, but the revenue consequence and the recommended action.

Payment fraud detection and chargeback fraud

In payments, losses often arrive twice: once as the fraudulent transaction, and again as the chargeback, the dispute handling, and the lost customer. Payment fraud detection catches the first. Averisys Fraud Intelligence Platform focuses on chargeback fraud specifically, linking refund abuse, login irregularities, device switching, and payment anomalies to disputes and to the revenue those disputes cost.

Fraud detection in banking, insurance, and retail

Fraud detection in banking, insurance claims, and retail payments shares one problem: controls that are too tight can cost more in blocked legitimate customers than the fraud they stop. Because a fraud intelligence platform quantifies both sides — fraud reduced and revenue affected — a risk team can choose the level of friction on evidence instead of instinct. Averisys applies the same platform across financial services, insurance, retail, telecommunications, and government programs.

What to look for when comparing platforms

Ask whether the platform explains its findings or only scores them, whether it quantifies revenue rather than fraud counts alone, whether it can simulate a change before the change is made, and whether the output is readable by the people who must act on it. Ask how quickly a new question can be answered without a separate data science project. Those points separate a reporting dashboard from a platform a business can make decisions with.

Frequently asked questions