1. Introduction: The Nacha 2026 Landscape

The March 2026 Nacha rule changes represent the most substantial fraud compliance updates in over a decade. These updates require all financial institutions, including both ODFIs and RDFIs, to implement risk-based fraud monitoring for ACH credit entries across originated and received transactions. Institutions must monitor for fraud at both the entity and transaction level, with documented procedures and risk-based thresholds.

Key areas of focus include identifying anomalous activity in incoming credits, monitoring micro-entries, SEC code usage, R17 returns, and unbalanced file activity. Increased scrutiny of originator behavior and return rates is also expected.

To meet these expectations, financial institutions must adopt smarter, predictive tools that reduce manual effort, improve fraud response, and ensure compliance readiness.

2. Affirmative’s Risk-Based Monitoring Capabilities

Originated Transaction Risk Scoring

Affirmative’s machine learning engine evaluates each originated transaction for its likelihood of being returned. Trained on an industry-leading ACH dataset that grows by over $1 trillion annually, our models produce accurate and prioritized risk scores that help fraud teams act quickly and effectively.

Key Benefits:

• Prevent unauthorized, NSF, or high-risk returns
• Reduce manual review workload
• Support regulatory compliance with minimal disruption

Irregular Incoming Items

We scan each incoming ACH transaction for signs of suspicious activity. Fraud indicators include micro-entries, dormant originator reactivations, and abnormal patterns in tax refund deposits.

Common Patterns Detected:

• Dormant Originator Activity
• High Originator Returns
• Clusters of IRS or State tax payments sent to one account
• Microdeposits (low value transactions probing for value account numbers)
• Unexpected SEC codes

Originator Risk Scoring

We assign predictive risk scores to originators based on the likelihood of unauthorized debit activity. Think of a FICO score, but for your originators. This allows financial institutions to take preventive action.

Why It Matters:

• A small percentage of originators drive the majority of unauthorized activity
• Risk scoring supports tiered controls such as prefunding and reserve management
• Streamlines annual originator reviews

Originator Risk Scoring

Our system continuously tracks ACH debit and credit utilization against assigned limits. Sudden changes may indicate fraud or misuse, allowing for quick intervention.

Example: An originator with a $750K daily limit increases activity from $100K to $700K over three weeks. This deviation was flagged and linked to mule account testing. 

Velocity Monitoring

Affirmative monitors both the count and dollar amount of transactions to detect unusual spikes or frequency shifts. These could indicate identity fraud, account takeovers, or automated attacks.

Example: A business that typically sends 50 daily transactions jumps to 500, totaling $250K. Investigation by the bank reveals compromised credentials and bot-driven fraud.

Unauthorized Return Rate Monitoring

Our platform alerts when an originator exceeds acceptable thresholds for unauthorized debits. Sustained high return rates are a red flag for fraud and can trigger regulatory violations.

Example: An originator reached a 1.2% unauthorized return rate. The financial institution launched a review, uncovered falsified authorizations, and filed a SAR.

R17 Return Monitoring

R17 returns signal issues with invalid or questionable data. Spikes in R17 activity may indicate brute-force testing, fabricated account details, or credential stuffing.

SEC Code Distribution Alerts

We monitor originators’ use of Standard Entry Class (SEC) codes and alert when activity deviates from historical norms. A shift toward high-risk codes, such as WEB or TEL, may signal elevated fraud risk.

Balanced vs. Unbalanced File Monitoring

ACH files should contain equal credits and debits. An imbalance may indicate tampering, formatting issues, or internal manipulation.

3. Fraud Monitoring Summary Table