Fraud Score

Overview

A fraud score is a numerical value assigned to a transaction, account, or individual to indicate the likelihood of fraudulent activity. It is generated using risk models that analyze multiple factors such as device data, geolocation, transaction history,identity verification results, and behavioral patterns. The higher the score, the greater the probability that the activity is fraudulent and requires further review.For banks, fintechs, payment processors, e-commerce platforms, and gaming providers, fraud scores help automate decision-making and prioritize investigations. They are widely used in transaction monitoring, onboarding, and account protection to reduce false positives while detecting real threats. Modern fraud scoring systems leverage AI and machine learning to continuously adapt to new fraud tactics, improving accuracy and speed. By integrating fraud scores into workflows, organizations strengthen compliance, minimize financial losses, and maintain customer trust.

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Related Terms

Risk Based Approach (RBA)
Model Drift (monitoring)
Velocity Checks

FAQ

How are scores built?

Combine engineered features and signals into models (GBMs, neural nets) with rule overlays. Calibrate to optimize business objectives and regulatory constraints.

What about explainability?

“Provide reason codes and feature contributions for adverse actions and audits. Transparent signals improve reviewer trust and customer outcomes.

How to keep scores fresh?

Monitor drift, retrain with recent labels, and run challenger models. Adjust thresholds for seasonality or new fraud patterns.

When to step up vs. block?

Use banded policies: block high-risk, challenge medium-risk, allow low-risk. Track conversion, false positives, and loss to refine cutoffs.