How AI Fraud Detection Software Works

How fraud detection software scores a transaction

Each transaction carries signals: amount, timing, device, location, payment method, and how the account has behaved in the past. The model weighs those signals together rather than one at a time, returns a risk score, and passes it to the approve, review, or decline decision within the payment flow.

Rules alone stop working

Static rules catch yesterday's fraud pattern and decline today's good customers. Fraud shifts faster than a rule library can be rewritten, and every added rule quietly increases false declines. A learning model updates as the pattern shifts and reports which signals drove the change.

The false-decline cost nobody measures

Blocking a legitimate purchase costs the sale and often the customer. Averisys Fraud Intelligence Platform measures both sides: fraud caught and revenue lost to declines, so a threshold can be set on evidence rather than instinct. What-if scenarios estimate the effect of tightening or relaxing a threshold before it goes live.

Explaining a decline

Risk, payments, and support teams all need the reason behind a decision. The platform states which signals drove each score in plain English, so a decline can be defended to a customer, an acquirer, or an auditor without opening a model.

Chargebacks and recovered revenue

Fraud losses, chargeback fees, and manual review time are all measurable. The platform ties each to the decisions that caused it and ranks the changes expected to recover the most revenue, so fraud work is reported in financial terms.

Frequently asked questions