Author: The PayLynxs Team

Fraud is increasingly a networked crime. The same Business Email Compromise (BEC) crew, call-center scam, or check-alteration playbook will sweep across regions and brands in waves—hitting multiple banks and credit unions within days. In 2024, the FBI’s IC3 recorded $16B+ in reported internet-enabled losses, up ~33% year over year—evidence that organized scammers are scaling faster than any single institution can respond. (Federal Bureau of Investigation, Internet Crime Complaint Center)

How modern fraud actually “scales” across institutions

• Reusable kits & scripts. Phishing- and fraud-as-a-service ecosystems let bad actors replicate the same trap at national scale, swapping only logos and URLs. Recent takedowns (e.g., INTERPOL’s 16shop) show how ready-made kits industrialize scams across brands. IBM’s threat reporting likewise highlights phishing kits as a top initial access vector. (Interpol, IBM)
• Channel-hopping campaigns. The ring that targets one bank’s online banking this week can pivot to another’s mobile app—or switch to SMS “smishing”—next week, using the same victim narratives (delivery issues, payroll updates, prize claims).
• Serial payees & destinations. Even when victims, devices, or account numbers change, the counterparties they send to (emails, handles, URLs, receiving accounts) often repeat—prime signals to share.
• Check-fraud waves. Mail-theft-driven check fraud has surged; FinCEN’s analysis logged $688M in suspicious activity tied to mail theft over just six months, illustrating how a tactic rolls across many FIs. (FinCEN.gov)
• Where losses concentrate. Survey data show debit-card and check fraud account for the largest shares of FI fraud losses—exactly the areas where fast cross-FI signal-sharing pays off. (ABA Banking Journal, FRB Services)

Why sharing fraud data works

1. It breaks the copy-paste loop. When Bank A flags a payee/email/device pattern tied to an active scam, Banks B–Z can step-up authentication or pause suspicious sends before the playbook repeats. Sector frameworks emphasize establishing a common language so fraud intel moves quickly and usefully between teams and firms. (FS-ISAC, ABA Banking Journal)
2. It speeds containment. Cross-FI alerts turn a one-off incident into early warning for everyone else—especially useful for rolling text/email scams and recurring check-deposit patterns.
3. It improves precision. Narrow, recent, fraud-only signals (e.g., “first-seen payee used in eight recent BEC cases”) lift catch-rates without blanketing good customers.

What to share (fraud-only)

Focus on fraud indicators, not customer dossiers:

• Counterparty signals: emails/phones, URLs/domains, handles/usernames, payee name variants, merchant/descriptor fragments.
• Event fingerprints: time, IP ranges/ASN, app versions.
• Case outcomes: “open or closed,” “potential loss”.

The takeaway

Fraudsters already operate as a network. Data sharing lets our defenses do the same—turning isolated incidents into sector-wide early warnings and shrinking the window where a scam can spread. A focused, privacy-conscious sharing program helps banks and credit unions stop copy-paste scams before they ripple across the industry.  Learn more about the SimpliRisk Peer Fraud Network today.

Sources & further reading

• FBI IC3 2024: reported internet-crime losses exceeded $16B (+33% YoY). (Federal Bureau of Investigation, Internet Crime Complaint Center)
• FinCEN trend analysis: $688M in mail-theft-related check-fraud suspicious activity over six months. (FinCEN.gov)
• Debit-card & check fraud lead FI losses (survey/summary). (ABA Banking Journal, FRB Services)
• FS-ISAC “Cyber Fraud Prevention Framework” (common language & intel-sharing guidance). (FS-ISAC, ABA Banking Journal)
• Fraud kits scale repeatable scams across brands (INTERPOL 16shop takedown; IBM X-Force). (Interpol, IBM)

In 2026, NACHA will require risk-based monitoring for ACH credits to combat credit-push fraud. Large ACH processors must start March 20, 2026; everyone else by June 22, 2026. The rules emphasize risk-based processes, annual reviews, and does not require pre-posting monitoring. (Nacha)

Why this change matters

Fraud has shifted toward credit-push scams—criminals trick legitimate payers into sending funds to mule accounts (e.g., via BEC). NACHA’s new rules create a baseline of fraud monitoring across the network so both originators and receivers have a role in spotting suspicious activity and improving recovery outcomes. The rules also introduce and reference “False Pretenses” (payments induced by misrepresentation of identity/authority or ownership of the credited account), which squarely covers BEC, vendor and payroll impersonation. (Nacha)

Who is affected and when

• Phase 1 — March 20, 2026
  • Applies to all ODFIs and non-consumer Originators/TPS/TPSPs with ≥6M originated entries in 2023.
  • Applies to RDFIs with ≥10M received entries in 2023. (Nacha)
• Phase 2 — Practical date Monday, June 22, 2026 (effective date is Friday, June 19—a federal holiday)
  • Extends to all other Originators/TPS/TPSPs and all other RDFIs. Nacha explicitly notes the June 22, 2026, practical date. (Nacha)

What’s actually required

For RDFIs (incoming ACH credits)

Establish and implement risk-based processes and procedures reasonably intended to identify ACH credit entries initiated due to fraud (including “False Pretenses”). Review these processes at least annually. Nacha clarifies pre-posting monitoring is not required. (Nacha)

For ODFIs/Originators/TPS/TPSPs (outgoing)

Implement risk-based processes and procedures to identify entries suspected of being unauthorized or authorized under False Pretenses, with annual reviews. Pre-processing monitoring is not required by rule, though it offers the best chance to stop fraud. (Nacha)

What “risk-based credit monitoring” looks like (RDFI examples)

Your program can combine rules, analytics, and case workflows around signals such as:

• Velocity & anomalies: first-time credits from a new Originator, sudden step-ups in amount/frequency, off-cycle “PAYROLL” bursts, unusual flows to a newly opened account.
• Context mismatches: SEC code inconsistent with account type; unexpected Company Entry Descriptions relative to prior behavior.
• Account risk: account age, typical balances, historic inflows/outflows, prior fraud flags, segment risk.
  These examples mirror Nacha’s guidance and align with existing AML monitoring practices. (Nacha)

A note on “pre-posting”

Many institutions ask if they must monitor before posting credits. Answer: No. Nacha states pre-posting monitoring of credit entries is not required; programs can be post-posting while still being risk-based and effective. (Nacha)

How SimpliRisk can help

• Risk-based credit monitoring for unusual SEC codes, high volume or unusual cycle payroll, high dollar, high volume, unexpected jump in amounts.
• Alerts workflows to review monitoring events.
• Case investigations to dive deeper into unusual activity, leverage our Peer Fraud Network tools to evaluate the risk against other known fraud.

Organizations should consider a fraud case management product instead of using spreadsheets because dedicated solutions offer better security, efficiency, and scalability. Here’s why a fraud case management system is superior:

Improved Security & Compliance

• Access Control – Create specific user groups to perform certain functions, unlike spreadsheets where access control is limited.
• Audit Trails – Tracks all input and changes made to a case, ensuring transparency and accountability.

Automation & Workflow Efficiency

• Automated Case Assignment – Assign cases to investigators based on predefined rules.
• Reminders & Alerts – Notify investigators of deadlines, status changes, and important updates.
• Prebuilt Templates – Standardized forms and reports reduce manual data entry errors.
• Integration with Other Systems – Connects with core processing systems to import person and transaction data to avoid additional data entry.

Scalability & Performance

• Handles Large Data Sets – Unlike spreadsheets, which can slow down with large files, case management systems efficiently process large amounts of data.
• Centralized Repository – All fraud-related data, documents, and communication are stored in one place, improving case tracking and resolution.
• Collaboration & Multi-User Access – Multiple investigators can work on cases simultaneously without version control issues.

Advanced Reporting & Analytics

• Dashboards & Insights – Visualize fraud trends, case progress, and investigator performance.
• Custom Reports – Generate police or subpoena report instantly, saving time and better protecting your institution.
• Relationship Scanning – Use your fraud case data to constantly compare that with new cases to immediately flag matching data between cases (names, phone, email, IPs, TINs, etc.).  This would require complex programming in a spreadsheet or be highly manual.

Better Investigation & Resolution

• Link Analysis – Connects related cases, entities, and transactions to detect and track fraud rings.
• Case Notes & Evidence Storage – Securely stores case details, documents, and investigator comments in a structured manner.
• Legal & Compliance Support – Provides documentation and case history for legal proceedings.

When to Upgrade from Spreadsheets

• If your fraud investigations involve multiple cases, multiple investigators, or high-risk financial transactions.
• If you need a more integrated workflow from referral to investigations.
• If you’re spending too much time on manual tracking, reporting, and data entry.

Conclusion

While spreadsheets may work for small teams with a low case volume, a fraud case management product significantly improves security, efficiency, and investigative accuracy—making it the smarter long-term investment.  Check out the SimpliRisk Fraud Case Management module for a scalable, efficient and affordable solution.

Fraud monitoring and Anti-Money Laundering (AML) monitoring are both critical components of financial crime prevention, but they focus on different aspects of criminal activity within financial institutions. Here’s a comparison of the two:

FRAUD MONITORING

• Primary Focus: To detect and prevent fraudulent activities such as identity theft, account takeovers, credit card fraud, check fraud, and other forms of financial deception.
• Goal: To protect customers and the institution from financial losses and reputational damage caused by fraudulent actions.

Scope

• Transactions: Monitors transactions for signs of fraud, such as unusual spending patterns, large withdrawals, or multiple failed login attempts.
• Behavioral Analysis: Uses behavioral analytics to identify anomalies in customer behavior that may indicate fraud.
• Authentication: Implements robust authentication mechanisms to prevent unauthorized access to accounts.

Techniques

• Rule-Based Systems: Uses predefined rules and thresholds to flag suspicious activities.
• Machine Learning: Employs machine learning algorithms to detect complex and evolving fraud patterns.
• Real-Time Alerts: Provides real-time alerts to flag potentially fraudulent transactions for immediate review.

Examples

1. Detecting an unusually high number of transactions from a single account in a short period.
2. Identifying transactions from locations inconsistent with the customer’s typical behavior.
3. Spotting changes in account details, such as address or contact information, that may indicate account takeover.

AML MONITORING

Primary Focus: To detect and prevent money laundering activities, which involve disguising the origins of illegally obtained money to make it appear legitimate.

Goal: To comply with regulatory requirements, protect the financial system’s integrity, and prevent the facilitation of criminal activities.

Scope

• Customer Due Diligence: Involves Know Your Customer (KYC) processes to verify customer identities and assess their risk.
• Transaction Monitoring: Monitors transactions for patterns consistent with money laundering, such as structuring, layering, and integration.
• Suspicious Activity Reporting: Identifies and reports suspicious activities to regulatory authorities.

Techniques

• Risk-Based Approach: Implements a risk-based approach to AML, where higher-risk customers and transactions receive more scrutiny.
• Transaction Analysis: Analyzes transaction data for red flags, such as large cash deposits, rapid movement of funds, or transactions with high-risk countries.
• Enhanced Due Diligence: Applies enhanced due diligence for high-risk customers, including politically exposed persons (PEPs) and those from high-risk jurisdictions.

Examples

1. Identifying a series of small deposits that just below reporting thresholds (structuring).
2. Spotting complex and rapid transfers between accounts that lack a clear business purpose (layering).
3. Detecting transactions involving countries with weak AML regulations or high levels of corruption.

KEY DIFFERENCES

Fraud Monitoring

• Focuses on preventing financial losses due to fraudulent activities targeting customers or the institution.
• Often uses real-time detection and response to stop fraud before it affects customers.
• Primarily driven by the need to protect customers and the institution, with some regulatory oversight.
• Covers a wide range of fraudulent activities, including theft, deception, and unauthorized transactions.

AML Monitoring

• Focuses on preventing the use of the financial system for laundering illicit funds and complying with regulatory requirements.
• Involves more comprehensive data analysis and reporting to identify suspicious patterns and comply with legal obligations.
• Strictly governed by regulatory frameworks and requires regular reporting to authorities.
• Specifically targets activities related to money laundering, such as structuring, layering, and integration of illicit funds.

Both fraud and AML monitoring are essential for maintaining the integrity and security of financial institutions, but they require different tools, techniques, and expertise to be effective.