🔹 Definition
Fraud Scoring is a risk assessment technique that assigns a numerical score or risk level to a user, transaction, or account based on the likelihood of fraudulent behavior. It leverages data analytics, machine learning models, and behavioral indicators to identify suspicious patterns in real time and support decision-making in fraud prevention, AML compliance, and identity verification workflows.
Fraud scores are used by financial institutions, fintech platforms, e-commerce providers, and compliance teams to detect and prevent first-party, third-party, and synthetic identity fraud before financial losses occur.
🔹 Frequently Asked Questions (FAQs)
Q1: How is a fraud score calculated?
Fraud scoring models analyze a variety of data points, including:
- User behavior (e.g., login patterns, velocity of activity, device switching)
- Identity signals (e.g., mismatched names, document inconsistencies, geolocation anomalies)
- Transaction history (e.g., unusual amounts, rapid changes, peer risk)
- External data (e.g., blacklists, known fraud networks, dark web exposure)
Each factor contributes to a composite score that reflects the risk level.
Q2: What do fraud score thresholds mean?
- Low score = low risk → allow transaction or approve onboarding
- Medium score = moderate risk → flag for review or apply step-up verification
- High score = high risk → block transaction, trigger enhanced due diligence (EDD), or deny access
Thresholds can be customized based on business risk appetite and regulatory context.
Q3: How does fraud scoring support compliance?
- Automates detection of high-risk users during onboarding
- Triggers EDD or STR filing based on risk thresholds
- Enhances real-time transaction monitoring
- Improves auditability and accountability with scoring logs and justifications
Q4: Can fraud scoring systems be bypassed?
While highly effective, fraud scoring tools are not foolproof. Sophisticated fraudsters may test systems using:
- Synthetic identities with clean histories
- Device spoofing and proxy networks
- Mimicry of low-risk behavior
Combining fraud scoring with biometrics, device fingerprinting, and manual review helps close these gaps.
