A Complete Picture of Check Fraud Risk
Nasdaq Verafin provides financial institutions with a holistic check fraud solution that enhances detection for the most challenging typologies. By combining known customer information and behaviors with deposit-side and counterparty risk analysis, our check fraud solution ensures fewer false positives, superior detection, and allows for an enhanced investigation within a single application.
Using consortium counterparty data and machine learning analytics, our approach brings together a complete picture of check fraud risk – uncovering fraud faster, disrupting criminal networks and ultimately preventing future crime.
Comprehensive
Check Fraud Detection
Powered by Consortium Insights
The foundation of Nasdaq Verafin’s Check Fraud solution is built on a powerful consortium data set, comprised from thousands of customer partners, hundreds of millions of counterparties, tens of billions of check images, and over a billion transactions that are analyzed in our Cloud each week. Without sharing personally identifiable information (PII), this massive data set provides Nasdaq Verafin with a holistic view of check fraud, from deposit to in-clearing.
Cross-Channel Intelligence
Nasdaq Verafin’s cross-channel fraud detection allows your institution to see across multiple channels, such as wire, ACH and instant payments, in a single solution, to quickly determine high and low risk payee-payer relationships.
Machine Learning Analysis
Nasdaq Verafin provides superior detection of in-clearing check fraud by performing a range of evaluations with tools enhanced by machine learning, including image analysis, transaction and MICR analysis, and counterparty analysis. These machine learning analytics are trained on our immense consortium data set to detect patterns of evidence indicative of fraudulent and non-fraudulent checks.
Consortium Counterparty Deposit Analysis
Using Cloud data, Nasdaq Verafin considers deposit-side risking using our deposit fraud machine learning analytic on the in-clearing check fraud analysis and alerts to potential risk for strong fraud detection.
Behavioral Analysis
Nasdaq Verafin’s Check Fraud solution analyzes patterns of behavior and considers when activity is uncharacteristic for your customer and may be indicative of a potential scam.
Check Imaging
Nasdaq Verafin provides you with the ability to view images of potentially suspicious checks within the alert. At a click, you can view the front and back of a check and compare features against other checks drawn from the same institution or written by the same payer.
Optical Character Recognition
Using Optical Character Recognition (OCR), Nasdaq Verafin enhances detection of fraudulent or fake checks and reduces false positives by identifying pre-authorized checks and correcting encoding errors.
Additional Features
Check Fraud Typologies
Nasdaq Verafin’s Check Fraud software solves for the hardest typologies.
• Altered Checks
• Forged Maker
• Forged Endorsement
• Counterfeit / Fictitious
• Duplicate Presentment
• Check Kiting
• HELOC
Check Fraud Typologies
Nasdaq Verafin’s Check Fraud software solves for the hardest typologies.
• Altered Checks
• Forged Maker
• Forged Endorsement
• Counterfeit / Fictitious
• Duplicate Presentment
• Check Kiting
• HELOC
Benefits
Superior Fraud Detection
Better detection of fraudulent activity, including difficult stolen check typologies such as washed or altered payee.
Increased Efficiency
Fewer false positive alerts than conventional approaches, allowing for greater efficiency.
Holistic Solution
Combining insights into deposit-side risk, with the precision of check fraud machine learning analysis, provides a uniquely powerful solution to the full range of check fraud typologies.
Continual Improvement
A solution that provides complete coverage for today’s prevalent fraud activities and rapidly adapts through machine learning as new fraud types emerge.
One Solution
Uncover check fraud quicker and more effectively using an enhanced investigation process without ever leaving the Nasdaq Verafin application.
