The Top Fraud Challenges Facing Community Banks in the US And How to Close the Gaps
Last year, the FBI's Internet Crime Complaint Center logged over one million complaints and $20.9 billion in total losses from cyber-enabled crime a 26% jump from the year before. Business email compromise alone accounted for $3 billion of that. Check fraud is running at $18 billion annually. And synthetic identity fraud volumes at US lenders grew 311% between Q1 2024 and Q1 2025.
None of those numbers are exclusive to big banks. Fraud threat actors don't skip community banks if anything, the perception that smaller institutions have weaker controls makes them more attractive targets.
The five challenges community banks struggle with most:
Check fraud- an old problem that got worse, faster
Synthetic identity fraud - the hardest to detect because the identity looks legitimate
ACH and wire fraud - three distinct attack types, often treated as one
Account takeover - behavioral detection is the gap, not credential verification
AML compliance - where fraud and financial crime cross and most banks miss it
Where these challenges converge is infrastructure. Community banks that track each problem in a separate system are operating with structural blind spots that organized fraud rings exploit. That's the deeper problem this article addresses.
1. Check Fraud: An Old Problem Getting Worse
Check fraud never went away. And in the past few years, it has come back at scale.
The FBI puts current annual check fraud losses at $18 billion, with 500 million fraudulent checks circulating each year and more than a million detected daily. A Federal Reserve survey found the number of financial institutions reporting attempted check fraud grew 10% from 2023 to 2024 alone.
Community banks are disproportionately exposed for two reasons. Their customer base - small businesses, sole proprietors, older account holders still uses checks heavily. And most community banks still rely on manual exception review, which doesn't hold up when fraud volumes spike.
What's changed is the method. Organized fraud rings have professionalized check washing chemically erasing ink from stolen mail and rewriting payees and amounts. The chemicals are easy to obtain. The checks are often deposited through mobile capture at a different institution before the originating bank flags anything. Counterfeit check schemes have also shifted toward business accounts, which carry larger average balances.
One bank fraud manager at ICBA described the irony: some of the oldest check fraud methods washing, forgery, altered payees — still work because they stay below the thresholds AI detection systems are tuned to catch, meaning the checks don't even get flagged for manual review.
Positive pay with payee match for all commercial accounts, not just select customers
Mobile deposit velocity limits and image-quality thresholds
Cross-account return item monitoring not just transaction-by-transaction flags, but pattern detection across accounts so a fraud ring doesn't cycle through dozens of customers unnoticed
2. Synthetic Identity Fraud: The Hardest Fraud to Detect
Between Q1 2024 and Q1 2025, synthetic identity document fraud grew 311%. US lenders faced $3.3 billion in exposure tied to synthetic identities in just the first half of 2025. Projections put total US synthetic identity losses at $23 to $35 billion annually by 2030.
The mechanism is straightforward, which is part of why it's so hard to stop. A fraudster combines a real Social Security number often belonging to a child, an elderly person, or someone with thin credit history with a fabricated name, address, and date of birth. They build credit history slowly, often over months or years. The account passes standard KYC checks because the SSN is valid and the credit history looks clean. Then the fraud hits typically a bust-out where the fraudster maxes out credit lines and disappears. When it works, there's no victim to file a complaint because the person on file never existed.
Bust-out fraud is now the most frequent fraud type across financial institutions, accounting for 21% of all fraud cases. And AI has made it faster and harder to catch. AI tools now generate fake pay stubs, bank statements, and tax records with realistic formatting, logos, and signatures that pass standard document verification. Deepfake technology allows fraudsters to bypass liveness checks in digital account opening by injecting AI-generated video directly into verification systems at the software level bypassing the camera entirely.
Document-only KYC doesn't catch any of this. No single data point reveals a synthetic identity. What gives it away is the pattern: inconsistencies in device behavior, application velocity across institutions, mismatches between SSN issuance dates and claimed age, or unusual credit activity relative to stated customer profile.
Cross-reference SSN issuance history against stated date of birth at account opening
Flag accounts where credit activity accelerated unusually fast after a dormant period
Look for shared device or IP signals across applications that appear unrelated
62% of banks say digital onboarding is the highest-risk point for synthetic identity exposure detection controls need to be heaviest at account opening, not after credit is extended
3. ACH and Wire Fraud: Three Threat Types, Not One
ACH fraud is often treated as a single category. It isn't. The three primary vectors have different mechanics, different targets, and different responses.
Business Email Compromise (BEC)
The FBI's 2025 IC3 Annual Report puts BEC losses at $3.046 billion the second-highest loss category in US cybercrime, behind only investment fraud. That figure came from just 24,768 complaints, which works out to roughly $123,000 per incident on average. 86% of BEC funds move via wire transfer or ACH, which means by the time fraud is detected, recovery is often impossible.
Community banks are targeted because their business customers small and mid-sized companies typically have weaker internal controls than enterprise organizations. Attackers compromise or impersonate business email accounts and redirect payments to fraudulent accounts. The attack doesn't require sophisticated technical intrusion. It requires patience and social engineering.
AI has made BEC worse. Attackers now use AI chat tools to match a CEO's writing style and voice cloning to provide phone confirmation that sounds like the person being impersonated. In 2025, businesses reported over $30 million in losses from BEC attacks with a confirmed AI component. When the email looks right and the voice on the call matches, human detection fails.
Fraudsters obtain account and routing numbers through data breaches, phishing, or social engineering and initiate unauthorized pulls. The Regulation E dispute burden falls on the bank. Community banks often lack automated dispute management tools, so when unauthorized debit volumes spike, the workload is entirely manual.
Account holders initiate a legitimate ACH debit, receive goods or services, then file a return claim. This is harder to dispute and increasingly common.
Out-of-band verification for wire and ACH instructions above set thresholds, especially when payment details change a callback to a known number stops the majority of BEC attempts
ACH return rate monitoring by customer; accounts with elevated dispute patterns need escalated review
Note: Nacha rule changes taking effect in 2026 are specifically designed to reduce successful BEC-style fraud attempts and improve fund recovery community banks should review the updated compliance obligations now
4. Account Takeover: The Behavioral Detection Gap
Account takeover happens when a fraudster gains access to a legitimate account through stolen credentials, phishing, or SIM swapping, then drains funds, adds new payees, or turns the account into a money mule.
Community banks face a structural problem here. They compete for digital customers against institutions with much larger technology budgets, but they face the same fraud environment. Many have deployed online and mobile banking platforms through third-party vendors without clear visibility into the behavioral signals those platforms generate.
The FBI IC3 flagged the unique complexity of account takeover cases, with some involving more than 50 simultaneous ACH transactions across multiple banks making real-time recovery nearly impossible.
ATO rarely looks like fraud at the login moment. Credentials check out. The device might be recognized. What changes is behavior: login at an unusual hour, then a new payee added, then a transfer to an external account. Individually, none of those actions triggers an alert. Together, they're the signature of an ATO. Banks that can connect those events across login, profile change, and payment initiation in a coherent timeline catch ATO before funds leave. Banks that can't, don't.
This isn't hypothetical. Consider the difference between monitoring "this transaction is large" versus monitoring "this account logged in from a new device at 2am, added an external payee, and initiated a wire within 8 minutes." One approach generates missed fraud and false positives. The other is behavioral detection.
Re-authentication (not just session continuation) before adding new payees or changing contact information
Behavioral baselines per account that flag anomalies in patterns, not just amounts
Device fingerprinting to catch credential use from unfamiliar environments
Cross-channel event sequencing — login events, profile changes, and payment initiations need to be correlated, not monitored in separate systems
5. AML Compliance Gaps: Where Fraud and Financial Crime Cross
This is where many community banks quietly struggle most, and where the consequences of a gap are the most serious.
AML compliance Suspicious Activity Reports (SARs), transaction monitoring, customer due diligence is a federal obligation under the Bank Secrecy Act. FinCEN enforcement actions, civil money penalties, and reputational damage from BSA failures are real and documented.
The structural problem is that most community banks run separate fraud and AML programs. Fraud teams see transaction anomalies. AML teams see structuring patterns. Neither automatically sees what the other is seeing. A customer whose activity looks like both fraud proceeds and structuring for AML purposes may not trigger escalated review because no single team has the full picture.
FinCEN's recent advisories have specifically flagged the overlap between fraud and money laundering in elder financial exploitation, cryptocurrency fraud, and human trafficking-related financial patterns. Institutions treating these as separate problems miss connections that matter.
The resourcing reality at community banks is that one or two people often manage AML compliance. Manual SAR filing takes hours. Basic or absent automated transaction monitoring is common. The program meets minimum requirements under normal conditions but lacks depth to detect complex patterns across customer relationships.
Map fraud typologies directly to AML red flags in your SAR decision framework - an elder fraud case should trigger both fraud recovery and BSA/AML review as a matter of documented process, not ad hoc judgment
Transaction monitoring rules that flag layering behavior, not just transaction size
Cross-reference fraud investigation findings against SAR filing decisions systematically - the connection between a fraud case and a potential SAR should not depend on whether two people happen to talk
The Infrastructure Problem Behind All Five
These five challenges look different on the surface. They share one underlying problem: fragmented visibility.
Check fraud rings cycle across accounts. Synthetic identity fraud requires behavioral correlation across the account lifecycle. ATO requires connecting login events to payment events. BEC requires correlating compromise signals to payment instructions. AML gaps persist because fraud signals don't surface in AML workflows.
A community bank tracking each of these in a separate system — a fraud case management tool, a standalone AML platform, a manual check exception process, a third-party dispute tool — has structural blind spots. Fraud that crosses those system boundaries goes undetected.
One fraud professional at ICBA described the problem precisely: if a fraudster runs a $500 Zelle payment, then a $2,000 ACH, then a $1,500 wire, then a $2,000 FedNow payment, each of those transactions looks legitimate under siloed detection systems. When a bank can see all of those together, the pattern is obvious. When it can't, the bank loses on every leg.
This is what unified FRAML intelligence is for. When fraud detection, AML monitoring, behavioral analytics, and investigation workflows run on the same underlying data, patterns that are invisible in any single system become detectable. A customer flagged in fraud investigation whose accounts also show structuring patterns gets escalated. A device associated with ATO gets flagged when it reappears in a new account application.
Verafye is built to operate as this kind of unified intelligence layer — connecting fraud signals, AML patterns, behavioral data, and graph relationships across accounts, devices, and transactions. For community banks that need fraud and AML coverage without enterprise headcount or a large in-house data science team, the architecture matters more than any individual feature.
Regulatory Context: What Community Banks Are Expected to Do
Community banks operate under the same BSA/AML framework as larger institutions:
Bank Secrecy Act (BSA): Requires SARs, Currency Transaction Reports, and customer due diligence
FinCEN CDD Rule: Requires beneficial ownership identification for legal entity customers
Regulation E: Consumer dispute rights for unauthorized electronic fund transfers
FFIEC examination guidelines: Set examination expectations for fraud and AML programs proportionate to the institution's risk profile
Nacha 2026 rule changes: New rules targeting BEC-style fraud and improving fund recovery community banks should confirm compliance with their ACH operations team now
Examiners expect controls proportionate to the institution's customer base and transaction volumes. That doesn't mean the same systems as a top-10 bank. It does mean documented fraud risk assessments, transaction monitoring, and SAR processes that demonstrate the institution understands what's happening in its accounts.
Frequently Asked Questions
What types of fraud are most common in community banks?
Check fraud, synthetic identity fraud, ACH and wire fraud (including BEC), account takeover, and AML compliance gaps are the most consistent challenges. Check fraud remains the most reported, with FBI data putting annual losses at $18 billion. Synthetic identity fraud is harder to detect because the identity looks legitimate for months or years before the loss hits.
How do community banks detect synthetic identity fraud?
Document verification alone doesn't work — AI tools now generate fake documents that pass standard checks. Detection requires behavioral analytics and identity graph analysis: flagging SSN issuance history mismatches, unusual credit acceleration after dormancy, and shared device or IP signals across applications. 62% of banks identify digital onboarding as the highest-risk point, so controls need to be strongest there.
What is the difference between fraud and AML for small banks?
Fraud involves direct financial loss to the institution or its customers. AML (Anti-Money Laundering) involves detecting and reporting financial crime — including proceeds from fraud being laundered through accounts. They're related but handled under different regulatory frameworks. The problem for community banks is that running them as separate programs creates blind spots: fraud proceeds that need SAR filing don't get flagged because the fraud team and the AML team aren't working from the same data.
How can community banks afford modern fraud detection technology? Cloud-native and API-based platforms have brought costs down significantly. The relevant question isn't whether community banks can afford it — it's whether they're scoping correctly. Community banks don't need enterprise infrastructure built for 10x
