Transaction Fraud Cut payment fraud with real-time protection

Prevent fraud at the point of payment—across traditional, instant and P2P rails.

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Hawk's Transaction Fraud

Product Differentiators Strike fast, stopping payment fraud with hawk-eyed precision

Rapidly trained transaction fraud models

Stop unauthorized fraud in its tracks with AI models tailored to your transaction patterns, matured, and deployed in under three days

Real-time, explainable AI

Halt fraud instantly (150 ms average) and release legitimate funds quickly with crystal-clear AI reasoning for each flagged transaction

Self-serve rule management & sandbox testing

Create, test, and deploy fraud rules on the fly to block flash fraud and fraudsters testing the limits of your controls

payment fraud detection software Shut down attacks by addressing risks at speed

Don't let misleading 'real-time' claims and reliance on external support leave you exposed; get effective protection with tailored transaction fraud defenses and agile control

Round Amount Rule Platform Screenshot

Prevent payment fraud with real-time precision

  • True real-time payment and card fraud detection (150 ms average)​
  • Support for all payment rails with rail-agnostic API (ACH, BACS, Card, Check, P2P, SEPA, Wire)
  • Payment interdiction that allows you to block, hold, and release transactions​
  • Out-of-the-box rule guidance including ACH/wire fraud, ATM fraud, card fraud, check fraud, merchant fraud, and more
  • Self-serve rule management, configuration, and live sandbox testing​
  • Rapidly trained AI models to detect chargeback fraud, stolen credit cards, and more
  • Automatic AI model governance​

AI from Hawk Generate an instant return on investment with explainable AI

AI transforms transaction fraud detection, reducing unnecessary alerts and providing clarity that enables analysts to act with confidence, all while keeping legitimate customers’ payments moving.

Cut losses from known transaction fraud patterns

Transaction fraud-specific machine learning models are tailored to your distinct card and payment fraud risks and deployed at speed with Hawk’s Day One Defense Models 

Quickly spot new types of unauthorized fraud

Hawk’s advanced anomaly detection models protect customers by detecting suspicious payments outside of their typical behavior

Cut unnecessary customer friction

Reduce noise with made-for-you false positive reduction models and accelerate transaction review with intuitive AI explanations

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Prevent more suspicious payments with a true real-time payment fraud solution

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The Hawk Difference Discover how Hawk raises the bar for efficient payment fraud detection

The Traditional Way

  • Ineffective, generic out-of-the-box AI fraud typology models  
  • Support team–managed rules that delay fraud response, letting some fraudulent transactions slip through. 
  • AI that generates alerts unclear explanations on why alerts are triggered 
  • Outdated user interface that slows down investigation creating friction 
  • Single control setup for all product lines and white-label customers 
  • Fragmented APIs with higher complexity and maintenance overhead 

The Hawk Way

  • Day one defense models for personalized protection at the speed of out-of-the-box models
  • Full control with flexible, self-serve rule management
  • Clear and contextual AI explanations which speed up alert review
  • Sleek interface and unified case management, delivering cross-payment rail insights all in one place
  • Multi-tenant structure for different business units or merchants
  • One payment-rail agnostic API 
Use cases

No payment rail left unprotected

Easily add new payment methods on the fly with Hawk’s rail-agnostic API

ACH, SEPA & BACS

Secure domestic and cross-border payments, preventing fraudulent schemes like:

  • ACH kiting or lapping
  • Fraudulent SEPA credits 
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Card

Prevent fraudulent debit and credit card transactions and reduce chargebacks before they impacts your business. Detect risks such as:

  • Card-present (CP) and card-not-present (CNP) fraud
  • Card testing and BIN attacks  
  • Stolen card credentials 
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Check

Stop fraudulent checks before they clear. Detect fraudulent check activities including:

  • Forged checks
  • Altered checks
  • Counterfeit checks
  • Check kiting and floating schemes 
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P2P

Protect your customers and platform from scams and fraudulent peer-to-peer payments, including: 

  • Phishing attacks and social engineering
  • Mule accounts
  • Rapid transfers between accounts 
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SWIFT & Wire

Ensure high-value transfers are secure, and unauthorized payments are prevented. Fraudulent activity may involve:

  • Fraudulent SWIFT messages
  • Transfers to high-risk jurisdictions 
     
See it in action

Hawk's AI Day One Defense Models: How to get tailored fraud prevention AI models quickly

Hawk's Day One Defense Models are AI typology blueprints fine-tuned to the specific needs of each financial institution, to deliver hyper-personalization, fast deployment, and high accuracy.

Read more

Frequently Asked Questions Want to know more?

Modern transaction fraud detection operates as a high-speed filtration system that analyzes financial activity in milliseconds. The process typically follows these four critical stages:

 

  1. Data Ingestion & Enrichment: As a transaction is initiated (via credit card, ACH, or wire), the system collects data points including the transaction amount, merchant category, geographic location, and device ID. It then enriches this with historical data, such as the user’s typical spending patterns and known fraud blacklists.
  2. Behavioral Analysis: A payment fraud detection system would typically compare the transaction against "normal" behavior. It looks for anomalies—such as a sudden high-value purchase from a new IP address—and calculates a risk score based on thousands of variables.
  3. Rule-Based Validation: The transaction is simultaneously checked against specific regulatory or internal business rules (e.g., "Flag any international transfer over $10,000" or "Block transactions from high-risk jurisdictions").
  4. Real-Time Decisioning: Within milliseconds, the system produces an outcome:
    • Approve: The risk is low; the transaction proceeds instantly.
    • Decline: High-risk indicators trigger an immediate block to prevent fund exfiltration.
    • Step-up Authentication: The user is asked for additional verification (like an OTP or biometric scan) to confirm the intent.
    • Review: The transaction is flagged for a human analyst to investigate via a fraud monitoring dashboard.

Modern detection systems are designed to identify and block a wide array of sophisticated attack vectors across all payment rails (ACH, Card, Wire, and Real-time Payments). The primary fraud types protected include:

  • Authorized Push Payment (APP) Fraud: This occurs when a criminal tricks a victim into manually authorizing a payment to an account they control—often through Business Email Compromise (BEC) or impersonation scams.
  • Account Takeover (ATO): Detection monitors for suspicious login behavior or sudden changes in transaction patterns that indicate a third party has gained unauthorized access to a legitimate user’s credentials.
  • Card-Not-Present (CNP) Fraud: By analyzing device fingerprinting and behavioral biometrics, the system identifies fraudulent online transactions where the physical card is not present, even if the attacker has the correct card details.
  • Synthetic Identity Fraud: This involves identifying accounts created using a mix of real and fabricated information (like a stolen SSN with a fake name), which are often used to "warm up" credit lines before a "bust-out" attack.
  • Money Muling & Smurfing: Sophisticated monitoring detects patterns of rapid, high-volume "pass-through" transactions used to launder illicit funds or bypass anti-money laundering (AML) thresholds.
  • Internal or "Friendly" Fraud: Identifying anomalies that suggest an insider is manipulating transactions or a customer is falsely claiming they did not receive goods to trigger a chargeback.

Rail-agnostic fraud monitoring allows a financial institution to oversee all payment types—such as ACH, Wire, SEPA, Swift, Cards, and Real-Time Payments (RTP)—within a single, unified system. The primary benefits include:

  • Cross-Rail Pattern Recognition: Fraudsters often move illicit funds across different payment methods to hide their tracks. A rail-agnostic approach detects "hop-over" patterns (e.g., a suspicious card deposit followed immediately by an outgoing RTP transfer) that siloed systems would miss.
  • Unified Risk Scoring: Instead of maintaining separate risk engines for different departments, you gain a 360-degree view of the customer. A high-risk event on a credit card can instantly update the risk profile for that user’s checking account or business wire facility.
  • Operational Efficiency: Managing one platform instead of four reduces the "tool sprawl" for fraud analysts. This leads to faster investigations, consistent reporting, and a significant reduction in the total cost of ownership (TCO) for your compliance stack.
  • Future-Proofing for New Payment Rails: As new instant payment networks (like FedNow or Pix) emerge, a rail-agnostic system can integrate these new data streams into existing fraud models without requiring a complete infrastructure overhaul.
  • Reduced False Positives: By analyzing the totality of a customer’s behavior across all channels, the system gains higher "contextual certainty," ensuring legitimate customers aren't blocked just because they switched from using a card to a bank transfer.

Articles & Resources The latest from Hawk

Top Pitfalls and how to avoid them

AI for Fraud Prevention: Top Pitfalls and How to Avoid Them

Read about the missteps to avoid when employing AI in your fraud prevention system, like underusing internal data, relying on generic models, or ignoring explainability.

Chartis Report Path to AI Banks

New Report: Financial Crime Compliance Enters New Era — Banks Scale AI for Results in Accuracy and Operational Efficiency

Read about AI-enabled compliance in the new industry report by Hawk and Chartis.

Celent FRAML Report

New Report on AML & Fraud Convergence Shows Mid-Market Banks & Credit Unions Are Embracing FRAML

New report from Celent and Hawk shows that over half (53%) of US mid-market banks and credit unions are looking to expand their convergence of anti-money laundering and fraud prevention.

Request a demo Stop 2X more threats with 50% less effort with Hawk

Request a demo with one of our product experts and find out if Hawk meets your business needs.

During the demo process you'll touch on Hawk's:

  • API infrastructure and data integration capabilities
  • Modular design and flexible multi-tenant set up
  • No-code rule builder, AI feature library, and model explanations
  • Any further questions you may have