Expense Fraud Detection Rate
Expense fraud detection rate is the percentage of flagged transactions confirmed as fraudulent or policy-abusive relative to total transactions reviewed. It measures the effectiveness of both automated controls and manual review processes in identifying genuine threats.
Ramp metric
Expense Fraud Detection Rate = (Confirmed Fraudulent Transactions / Total Flagged Transactions) × 100
Expense fraud detection rate is the percentage of flagged transactions confirmed as fraudulent or policy-abusive relative to total transactions reviewed. It measures the effectiveness of both automated controls and manual review processes in identifying genuine threats.
Full guide: definition, formula, and benchmarksHow to calculate Expense Fraud Detection Rate
Expense Fraud Detection Rate = (Confirmed Fraudulent Transactions / Total Flagged Transactions) × 100
Why Expense Fraud Detection Rate matters for Ramp users
Even well-controlled organisations face expense fraud, from fictitious receipts and personal purchases on corporate cards to collusion with vendors. A low detection rate may mean fraud is slipping through, while an excessively high flag rate with few confirmations wastes reviewer time.
Ramp users benefit from real-time transaction monitoring and automated receipt verification that surface suspicious patterns early. Tracking detection rate ensures these controls remain calibrated as spending patterns evolve and new fraud vectors emerge.
Driver
Conversion rate
Outcome · 58% contribution
Revenue
Understand and act on Expense Fraud Detection Rate with KPI Tree
Sync Ramp flagged-transaction and review-outcome data to your warehouse and compute detection rate in KPI Tree. Place it in a financial controls tree alongside compliance violation rate and spend anomaly detection.
Assign internal audit or finance ownership and set alerts when detection rate shifts significantly, indicating either improved fraud attempts or degrading control effectiveness.
Get started with your Ramp data
Connect your existing warehouse where Ramp data already lands.
Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.
Related Ramp metrics Ready to add to your trees.
Spend Anomaly Detection
Expense ManagementSpend anomaly detection measures the volume and value of transactions flagged as statistical outliers against historical spending patterns. It captures deviations in amount, frequency, merchant, or timing that warrant investigation.
View metric
Compliance Violation Rate
Expense ManagementCompliance Violation Rate = (Violating Transactions / Total Transactions) × 100
Compliance violation rate is the percentage of transactions that breach company spending policies, including merchant restrictions, spending limits, or category prohibitions. It measures policy enforcement effectiveness.
View metric
Out-of-Policy Spend Rate
Expense ManagementOut-of-Policy Spend Rate = (Out-of-Policy Spend / Total Spend) × 100
Out-of-policy spend rate measures the percentage of total spend that falls outside company expense policies, including excessive amounts, unapproved categories, or non-compliant merchants.
View metric
Duplicate Payment Detection
Expense ManagementDuplicate payment detection measures the number and value of potentially duplicated transactions identified across expense submissions and vendor payments. It quantifies recoverable waste in the payments process.
View metricExplore Expense Fraud Detection Rate across integrations
All Ramp metrics
Empower your team to understand and act on Ramp data
Map what drives your metrics, measure progress at any grain, prove what works statistically, and deliver personalised action plans to every team member.