Paymongo Protect

Paymongo Protect delivers comprehensive, real-time fraud detection and management tool designed for fast-growing businesses. With instant risk scoring powered by advanced machine learning, every transaction receives a score from 0 to 1000, helping you make informed decisions at a glance.

Paymongo Protect's flexible rules engine empowers your team to define custom conditions for automatic transaction approval or blocking—whether it’s based on risk scores, card origins, or other factors. This means you can tailor fraud prevention to your unique business needs, reducing manual effort and streamlining operations.

The fraud team dashboard offers a centralized view of risk activity, giving teams the tools to review flagged transactions, understand risk drivers, and reverse decisions with confidence. Dive deeper with intelligent insights and analytics, uncovering fraud trends and optimizing your strategy over time.

Paymongo Protect ensures robust protection across your entire transaction flow and supports the following payment methods:

• Cards
• E-Wallets like GCash, PayMaya, GrabPay
• QR Ph
• Others (coming soon)

Machine Learning Engine for Fraud Detection

Paymongo Protect is powered by an advanced machine learning engine, trained on extensive historical transaction data validated by internal Paymongo fraud teams. This engine evaluates each transaction in real-time, using a dynamic risk model that incorporates multiple signals, including:

• Behavioral patterns (e.g., transaction frequency, changes in user behavior)
• Device signals (e.g., device fingerprints, browser information)
• Velocity checks (e.g., rapid transaction attempts, repeated failed payments)
• Anomaly detection (e.g., outlier transaction amounts, unusual geographic patterns)

These signals are processed to generate an accurate fraud risk score for every transaction. The risk score informs both automated rules and manual review processes, helping your team act quickly and confidently.

Risk Score Ratings and Feature Transparency

Paymongo Protect not only produces a risk score, but also provides clear explanations for each score. The dashboard reveals which features contributed most to a transaction’s risk—such as device changes, mismatched address details, or high transaction amounts—offering transparency and supporting informed decision-making.

Default Risk Levels

Paymongo Protect assigns risk levels based on the transaction’s risk score:

• Medium Risk: Score of 500 to 800
• High Risk: Score of 800 and above

Transactions with medium or high risk are typically flagged for review or blocking, depending on your configured rules.

For details on how to configure automated actions based on risk scores, see the Rules documentation.

Features

Real-Time Risk Scoring and Explanations

• Dashboard Overview:
  The Paymongo Protect dashboard displays real-time risk scores for every transaction, alongside details of the factors contributing to each score. Example risk drivers include:
  • Unusual transaction amounts
  • Geolocation mismatches
  • Suspicious transaction origin
  • High-risk email domains

Segmentation and Trend Monitoring

• Segment Transactions:
  Filter and group transactions by risk level (e.g., low, medium, high) to quickly surface patterns and anomalies.
• Trend Analysis:
  Track risk levels and fraud signals over time, helping you spot emerging threats or seasonal spikes in fraudulent activity.

Deep Dive and Case Investigation

• Drill Down on High-Risk Cases:
  Inspect individual transactions flagged as high-risk to understand the specific signals and rules triggered.
• Score Explanations:
  Review transparent, feature-level breakdowns to see which factors most influenced the decision, supporting confident manual reviews.

Historical Pattern Comparison

• Fraud Pattern Analysis:
  Compare current fraud trends to historical data to uncover shifts in tactics or vulnerabilities.
• Proactive Strategy Optimization:
  Use insights to proactively refine your fraud prevention approach, adapting to new threats and minimizing manual workload.