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What the Q1 2026 ACH metrics mean for ACH fraud detection

The ACH Network entered 2026 with strong momentum as financial institutions continue to modernize payments. Newly released metrics from Nacha show accelerating adoption of Same Day ACH and continued growth in business-to-business (B2B) payments—two trends that are reshaping the payments landscape for banks and credit unions alike.

Key topics covered in this post:

Same Day ACH growth metrics
Balancing customer expectations for speed with security
What financial institutions should prioritize to fight fraud

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Same Day ACH growth continues to accelerate

The Q1 Nacha numbers reflect changing customer expectations around real-time payments, increasing pressure to modernize ACH fraud detection strategies, and the need to adapt to an evolving regulatory and operational environment.

According to Nacha, there were 403 million Same Day ACH payments in the first quarter of 2026, representing a 23.6% increase over the same period last year. The value of those payments reached $1.1 trillion, up 22.1% year over year and marking the second consecutive quarter that Same Day ACH value surpassed the $1 trillion threshold.

This sustained growth should signal to community financial institutions that faster payments are no longer viewed as a premium or niche service. Businesses and consumers increasingly expect funds to move quickly, predictably, and securely. With these expectations, ACH operations can no longer be treated as purely back-office functions. ACH has become a strategic channel directly tied to customer experience and competitiveness. Many institutions are evaluating how Same Day ACH complements broader, faster payments strategies while maintaining operational resiliency and compliance.

B2B payments are driving ACH network expansion

Nacha also reported that nearly 2.1 billion B2B payments moved through the ACH Network during Q1, an increase of 9.4% compared to a year ago. These numbers reinforce a broader industry shift away from paper checks and manual payment processes. Commercial customers increasingly want digital payment experiences that improve cash flow visibility and reduce processing delays.

For banks and credit unions, B2B ACH growth creates opportunities to deepen treasury relationships and deliver more value-added services. At the same time, commercial ACH activity can introduce elevated fraud risks due to higher transaction values and increasingly sophisticated fraud tactics. This is where ACH fraud detection becomes especially critical.

Faster payments increase the need for strong ACH fraud detection

As payment speed increases, fraud decisioning windows shrink. Financial institutions have less time to identify suspicious activity before funds move. Fraudsters understand this dynamic and increasingly target ACH channels using account takeover schemes, business email compromise, synthetic identities, and mule account activity.

The continued rise of Same Day ACH means institutions need fraud controls that can operate in near real time without creating unnecessary friction for legitimate customers. Modern ACH fraud detection solutions increasingly rely on layered approaches that combine behavioral analytics, anomaly detection, risk scoring, and ongoing transaction monitoring. Institutions also need visibility across payment channels, since fraud patterns rarely remain confined to a single rail.

The challenge is balancing speed with security. Customers expect payments to move faster, but they also expect their financial institution to protect them from fraud. According to survey data from Abrigo, the majority (51%) of American respondents aged 25 to 34 believe banks should always reimburse fraud victims.

What financial institutions should prioritize next

As ACH usage moves into faster, higher-value payment scenarios, expectations for financial institutions’ governance, risk management, and fraud prevention will grow accordingly. Financial institutions are increasingly evaluating whether existing processes, staffing models, and technologies are equipped to support the growing volume and velocity of transactions.

The Q1 2026 ACH metrics point to several priorities for banks and credit unions:

Strengthening ACH fraud detection capabilities to support faster payment environments

Improving visibility into high-risk transaction behavior

Modernizing operational workflows to support increasing Same Day ACH volume

Supporting commercial clients transitioning away from checks

Balancing payment speed with risk management and compliance expectations

The ACH Network remains one of the foundational payment rails in the U.S. financial system, but the way institutions use it is changing rapidly. For financial institutions, the message from Q1 is clear: ACH modernization is no longer optional. It is now central to how institutions compete, protect customers, and support the future of digital payments.

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Large sporting events like the FIFA World Cup can exacerbate human trafficking

The upcoming 2026 FIFA World Cup brings excitement for soccer fans around the globe, but for financial institutions and law enforcement, major sporting events also signal the need for heightened vigilance against human trafficking.

What is human trafficking?

Human trafficking is a crime in which force, fraud, or coercion is used to compel a person to perform labor, services, or commercial sex acts. Large-scale events often create increased demand for temporary labor, hospitality services, transportation, and commercial sex, creating opportunities for traffickers to exploit victims.

Financial institutions of all sizes play a critical role in identifying and reporting suspicious activity connected to human trafficking. According to FinCEN Director Andrea Gacki, “Timely reporting on suspicious activity potentially connected to human trafficking, regardless of threshold, is crucial in helping law enforcement aid possible victims and prosecute their traffickers.”

The U.S. Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) issued a Notice urging financial institutions in and around cities hosting the 2026 FIFA World Cup to increase vigilance. The 2026 FIFA World Cup is expected to draw millions of domestic and international visitors, and financial institutions of all sizes play a pivotal role in detecting and reporting suspicious activity related to human smuggling and human trafficking.

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Behavioral indicators and transactional red flags

Behavioral red flags may include the presence of a controlling third party who:

Speaks on behalf of the customer
Insists on remaining present throughout the interaction
Attempts to complete paperwork without consulting the customer
Retains possession of the customer’s documents or funds
Behaves aggressively or intimidates the customer

Additional warning signs may include customers who:

Appear malnourished, fatigued, or show signs of abuse
Do not know where they are staying or provide inconsistent stories
Show little control over their own finances or personal documents

Sex trafficking during major events

FinCEN notes that victims of sex trafficking may be forced to travel frequently to meet clients within short timeframes. As a result, victims or traffickers may exhibit unusually large travel-related transactions. Victims often receive payment for commercial sex acts in cash, but traffickers may also use peer-to-peer transfers, credit card payments, digital assets, or prepaid access cards.

Other transactional red flags include:

Frequent cash deposits into easily accessible ATMs
Rapid transfers of deposited funds to another account
Transactions inconsistent with a customer’s normal activity patterns

Labor trafficking during major events

The increased demand for labor and services surrounding major sporting events can also create opportunities for labor trafficking.

Seemingly legitimate businesses in host cities may use exploitative employment practices to meet staffing demands. Financial institutions may observe:

An absence or deviation from expected payroll expenses
Wages transferred from a victim’s account to another individual’s account
Large deductions from employee wages
Minimal or no transactions associated with maintaining essential living needs, which may indicate a trafficker’s financial control over a victim

Financial institutions are key partners in combating human trafficking

According to the National Human Trafficking Hotline, working in the financial industry provides you the opportunity to report suspicious behavior involving 92% of the various types of human trafficking. When you encounter transactions that just don’t feel right, don’t hesitate to file Suspicious Activity Reports (SARs) and call the National Human Trafficking hotline at 888-373-7888.

FinCEN also encourages voluntary information sharing among financial institutions and financial institution associations, including appropriate cross-border sharing with foreign financial institutions, to help identify and prevent potential money laundering or other illicit activity related to human trafficking.

Customer-facing employees are especially important in identifying potential trafficking activity because victims may have limited contact with people outside of their traffickers, other than when visiting financial institutions.

It's easy to feel overwhelmed by the number of potential human trafficking transactions you find, knowing the abuse that lies behind these transactions. The good news is there are data scientists designing ways to detect these patterns without human intervention. Financial institutions with AML software that incorporates AI will be empowered to find data that can free victims faster.

Learn more human trafficking behavioral indicators with this checklist: "Human Trafficking Red Flags."

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The risk of outdated technology and processes

Some financial institutions look efficient on paper but have outdated processes and technology systems. These can reveal gaps in compliance, data integrity, or risk controls. Misclassified borrowers, inconsistent data, and incomplete information lead to flawed decisions.

This article covers these key topics:

Three outdated modes of operation
Hidden costs of outdated modes and processes
The path forward to innovation
The role of artificial intelligence

'Business as usual' no longer works

My introduction to banking came in a small hometown institution, working summers between college years. Back then, the industry (only half-jokingly) ran on the “3-6-3” rule: pay 3% on deposits, charge 6% on loans, and be on the golf course by 3:00—especially on Thursdays where I worked.

Those days are gone. Margins are tighter, competition is faster, and borrowers have more options than ever. “Business as usual” no longer works.

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3 Outdated modes of operation

Underwriting

Early in my career, underwriting involved paper, pencils, and a good typewriter ribbon—and it took days. Today, we have automated loan origination systems…and it still takes days.

No amount of technology will fix an obsolete, overly complex underwriting approach. Meanwhile, fintechs offer decisions in minutes and funding within a day. You can debate their philosophy, but your borrowers won’t. They care about speed and certainty. And they will pay for it.

The contrast to lending at many financial institutions is stark: a straightforward credit that could be decisioned in hours instead moves through days of rework, duplication, and handoffs.

Risk isn’t one-size-fits-all, yet we treat it that way. Simple credits should move quickly and consistently. Time and expertise should be reserved for complex risk, where it actually matters.

Approvals

In response to past failures, the industry swung from excessive approval autonomy to excessive control. The result: layered approvals, diffused accountability, and slow decisions.

Recent data shows most institutions still require three or more approval levels for small business credit. That is a process choice with consequences, and in most cases, the process choice isn’t about risk management; it’s risk avoidance.

Not every exposure deserves the same treatment. A small loan that cannot threaten the institution should not be subjected to the same process as a top-tier exposure.

Put this in perspective. Stack rank your loan portfolio by smallest borrower to largest. How much of your portfolio exposure is represented by 50% of your borrowers? 60%? 70%? 80%? I suspect that 70% to 80% of your borrowers combined represent a fraction of your portfolio. Why do you want to spend so much time and effort approving facilities to them when, if you charged off the whole segment (which is highly unlikely given the diversity), you can’t lose your institution?

If you want people to manage risk, give them ownership and make them accountable. Use portfolio tools to identify outliers. Stop treating every loan like your worst-case scenario requiring elevated approvals.

Technology and data

We depreciate software over 3–5 years yet hesitate to invest in modern systems, especially core platforms. Instead, we build workarounds.

The result is a patchwork of spreadsheets—isolated, inconsistent, and poorly governed. Meanwhile, we still insist the core is the “single source of truth,” even when it clearly isn’t.

Worse, we launch products we can’t operationally support. That leads to manual processes, shadow systems, and sometimes even Post-it notes backing what we often market as automated capabilities. Critical data lives outside the system of record and is reconciled manually.

Core replacement is expensive and disruptive. But avoiding it comes with its own cost: inefficiency, errors, and a slow bleed of resources.

Hidden costs = credit costs

There are institutions that look efficient on paper, only to later reveal gaps in compliance, data integrity, or risk controls. Efficiency ratios can mask underlying fragility.

If you don’t trust your data, you can’t manage your portfolio. Misclassified borrowers, inconsistent data, and incomplete information lead to flawed decisions.

If your institution is slow, process-heavy, and reliant on backward-looking metrics because that’s all you have, you have a credit problem, whether you recognize it or not.

The path to closing innovation gaps

In the effort to avoid risk (as opposed to manage it), the result is often an elevated risk profile due to inefficiency, ignorance and error. But there is a way to fix this. This isn’t easy—but it is straightforward.

Principles

Start with a clear foundation:

Focus on decision making speed and quality
Favor controlled, incremental change over reactive “big bang” efforts
Prioritize risk reduction over marginal productivity gains
Align credit culture, policy/guidance, and incentives with the desired operating model

Identify blind spots

Bring the right people (line, credit, loan servicing, CRO, CCO, CFO) into a room and dissect the credit process end-to-end. The only “sacred cow” is that there are no sacred cows. If a step exists, it should be defensible. If it isn’t, it should be challenged.

Ask simple but uncomfortable questions:

Why does underwriting take so long?
What slows approvals?
Where are exceptions and overrides coming from? Are they relevant to the true credit risk of the institution, or just pet peeves?
How are people actually spending their time?
Where are losses—or (more likely) excessive servicing costs—emerging?
Do we have the data we need? If not, why?

The goal is clarity: what prevents effective, quick, risk-based decisions?

Fix the operating model

Most efforts fail due to resistance to change rather than due to a lack of insight. Without executive and board-level alignment from the start, modernization will stall.

Focus on:

How time is actually spent vs. how it should be spent
Streamlining underwriting and eliminating low-value steps
Reducing unnecessary approvals
Clarifying accountability
Aligning incentives with desired behaviors

Once you go through this process and make the necessary adjustments, the next step is to ensure that your policies, guidance, and procedures (as well as the incentive program) are simplified and realigned to the new model and CLEARLY communicated to your team with plenty of time for feedback and internalization. To simply impose change without communication and alignment is another major flashpoint of failure.

Modernize data (incrementally)

You can’t fix data all at once. But you do need to start somewhere. Pick a handful of critical credit data elements and clean them for your largest exposures. Then expand. Build processes to maintain data quality at every touchpoint, with clear points of accountability and consequences for failure.

In parallel, improve the completeness and timeliness of borrower information, also with accountability and consequences. From there, establish governance to systematically address gaps.

Progress will be incremental, but standing still is not an option.

Use technology thoughtfully

The biggest mistake institutions make when it comes to modernizing systems is layering new technology onto broken processes. Far too often, automation fails because decisions still require excessive time and effort.

Here are some key questions to address as you consider a new loan origination system or other technology:

What decisions can be automated?
Where is human judgment truly needed?
Are roles and responsibilities clear?
Is underwriting aligned with actual risk?
Are we identifying emerging risks early?

The role of artificial intelligence

AI is a strategy and a tool. Before financial institutions can answer “How do we use AI?” it helps to answer “What problem are we solving?”

Today, on individual credit, AI can already support:

Benchmarking and comparative analysis
Early detection of financial stress
More consistent underwriting decisions

At the loan portfolio level, AI can surface patterns that are very difficult to detect manually—across markets, products, or borrower types.

But AI requires discipline:

Clear governance by use case (underwriting, monitoring, portfolio management)
Defined human accountability
Transparency in outputs
Ongoing monitoring for drift and bias

AI can enhance judgment. It cannot replace responsibility.

Rethink how decisions are made

Competing today requires more than adopting new technology. It requires rethinking how decisions are made.

Better decisions—faster, more consistent, and grounded in reliable data—aren’t just an efficiency gain. They are a credit risk imperative. The most dangerous credit risks rarely announce themselves. They build quietly through slow processes, unclear accountability, and unreliable data, until they surface in ways that are harder to correct.

And yes—it’s still okay to play golf on Thursday afternoon.

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At first glance, a machine in a store might look like a standard Automated Teller Machine (ATM). Some are traditional ATMs that dispense cash. Others are cryptocurrency ATMs, often called Bitcoin teller machines (BTMs), that allow customers to buy or sell cryptocurrency using cash. They may share a similar name, but they operate very differently and pose distinct risks and compliance expectations for financial institutions.

As cryptocurrency adoption continues to expand, more merchants are installing BTMs to generate additional revenue. This trend makes cryptocurrency ATM monitoring an increasingly important part of a financial institution’s risk management program. During site visits and customer reviews, staff can no longer assume that every machine functions under traditional ATM rules.

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Why BTMs require closer attention

Unlike a traditional ATM, a BTM facilitates the exchange between fiat currency and cryptocurrency. This activity typically classifies the operator as a money services business (MSB), which brings added regulatory obligations under the Bank Secrecy Act (BSA) and anti-money laundering (AML) requirements.

From a risk standpoint, BTMs introduce several concerns:

The potential for pseudonymous transactions, depending on controls
Rapid movement of funds across jurisdictions
Increased exposure to fraud and illicit activity
Third-party operators with varying levels of compliance maturity

Financial institutions already understand that higher-risk products require stronger oversight. As expectations for BSA and AML programs continue to evolve, maintaining awareness and control over emerging channels, such as BTMs, is essential.

Regulatory expectations for BTM operators

Financial institutions should expect BTM operators to meet specific regulatory and compliance requirements. Gaps in these areas may indicate elevated risk.

Registration and licensing
BTM operators must register as an MSB with the Financial Crimes Enforcement Network (FinCEN). Many states also require a money transmitter license or additional cryptocurrency-related licensing.
AML program requirements
A compliant operator should maintain a written AML program that includes:
Customer identification and verification procedures
Ongoing transaction monitoring
Suspicious activity reporting
A designated compliance officer
Independent review
Operators should conduct independent audits of their AML program and utilize appropriate monitoring procedures. These tools help identify exposure to high-risk wallets, sanctioned entities, or suspicious transaction patterns.

Banking relationships
Because BTMs require a financial institution to facilitate cash flow, the operator’s compliance posture directly impacts the bank or credit union. This underscores the importance of due diligence and ongoing oversight.

This is where cryptocurrency ATM monitoring plays a key role. Understanding who owns, operates, and manages the machine is foundational to assessing risk.

Third-party risk

A common challenge is that merchants do not always own the BTM located on their premises. In many cases, a third-party provider installs and operates the machine, and the merchant receives a share of the revenue.

This type of arrangement can introduce additional third-party risk that is not always obvious at first. For example:

The merchant may rely entirely on the operator’s AML program
The financial institution may have limited visibility into transaction activity
Compliance accountability may be unclear or misunderstood

In some situations, merchants are unaware of the regulatory requirements tied to BTMs. Unscrupulous providers may place machines without fully explaining the responsibilities involved. That lack of transparency can expose both the merchant and the financial institution to risk.

Even when the merchant is not the operator, the presence of a BTM should influence how the relationship is risk-rated and monitored.

Customer due diligence and risk rating

Financial institutions should incorporate BTMs into their customer due diligence and risk assessment processes.

At onboarding and during periodic reviews, consider:

Does the customer own, operate, or host a BTM?
Who is responsible for regulatory compliance?
Is the operator registered with FinCEN?
Is the appropriate state licensing in place?

The presence of a BTM does not automatically make a customer high risk. However, it should prompt a closer evaluation of the customer’s overall risk profile and may warrant enhanced due diligence.

Ongoing monitoring is equally important. Changes in ownership, transaction volume, or business activity should trigger reassessment.

Red flags

In addition to understanding the structure of the relationship, financial institutions should be aware of transactional red flags associated with BTMs. Incorporating these into your cryptocurrency ATM monitoring processes can help identify potential issues early.

Examples of suspicious activity may include:

Repeated cash deposits followed by immediate cryptocurrency purchases
Customers structuring transactions to avoid identification thresholds
Multiple individuals using the same machine in a coordinated manner
Unusual transaction volumes inconsistent with the business type
Customer complaints indicating confusion or possible fraud

These patterns should be evaluated within the institution’s existing suspicious activity monitoring framework and escalated when appropriate.

Internal processes and training

Effective cryptocurrency ATM monitoring requires coordination across multiple teams. Frontline staff, lenders, and BSA professionals all play a role in identifying and managing risk.

Financial institutions should:

Train staff to recognize BTMs during site visits
Update procedures to require documentation and photos of machines
Enhance customer questionnaires to include cryptocurrency-related questions
Ensure BSA and AML teams understand how BTM activity fits into monitoring systems

A lack of awareness at the frontline level can lead to missed risk indicators. Ongoing training and clear communication help ensure that emerging risks are consistently identified and addressed.

Practical steps

To strengthen your approach, consider implementing the following:

Ask all business customers whether they have an ATM or a BTM
Identify who owns and operates the machine
Obtain FinCEN registration details and verify licensing where applicable
Maintain copies of BTM agreements, including AML compliance language
Incorporate BTM activity into your risk assessment and monitoring processes

If agreements do not clearly address compliance responsibilities, that may signal a need for further review.

Looking ahead

Cryptocurrency and alternative payment channels continue to evolve. As adoption increases, regulators are likely to maintain or expand their focus on these areas.

Financial institutions that take a proactive approach to cryptocurrency ATM monitoring will be better positioned to manage risk, support their customers, and meet regulatory expectations. This includes building scalable processes, leveraging technology where appropriate, and ensuring staff are equipped with the knowledge they need.

Understanding the difference between an ATM and a BTM is no longer a minor detail. It is a necessary step in maintaining a strong, risk-based compliance program in a rapidly changing financial landscape.

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FAQs

What is a BTM?

A BTM, or Bitcoin Teller Machine, is a kiosk that allows users to buy or sell cryptocurrency using cash, debit cards, or digital wallets. For banks and credit unions, BTM activity may require monitoring as part of AML transaction monitoring and financial crime compliance workflows.

What is the difference between an ATM and a BTM?

An ATM allows customers to access traditional banking services, while a BTM, or Bitcoin Teller Machine, allows users to buy or sell cryptocurrency. For banks and credit unions, understanding the difference helps BSA/AML teams identify crypto-related transaction activity that may require enhanced monitoring.

Why should financial institutions monitor cryptocurrency ATM activity?

Financial institutions should monitor cryptocurrency ATM activity because it can create elevated fraud, money laundering, and scam risk. AML transaction monitoring software can help banks and credit unions detect unusual cash withdrawals, rapid movement of funds, or customer behavior connected to crypto scams.

What red flags may indicate suspicious BTM-related activity?

Suspicious BTM-related activity may include repeated cash withdrawals, transactions inconsistent with a customer’s profile, elderly customers sending funds after coercion, or activity tied to known crypto scam patterns. Abrigo-related AML/CFT workflows can help financial institutions identify, investigate, and document these red flags more consistently.

How can banks and credit unions reduce risk from cryptocurrency ATM transactions?

Banks and credit unions can reduce cryptocurrency ATM risk by combining staff training, customer education, risk-based monitoring, and clear escalation procedures. Financial crime compliance software can support this process by centralizing alerts, customer data, investigation notes, and SAR decisioning.

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Why lenders should lead the conversation on timing, cash flow, and performance

In today’s uncertain environment, many business borrowers hesitate to move forward with equipment investments. Concerns about interest rates, cash flow, or economic conditions often lead them to delay financing in an effort to “save money.”

Borrower hesitation presents both a challenge and an opportunity for financial institutions that are diversifying with equipment finance. By reframing the conversation, banks and credit unions can demonstrate the equipment finance advantage and help clients make decisions that strengthen both operational performance and credit quality.

Aging equipment creates a performance problem

When borrowers defer equipment upgrades, the impact isn’t always immediate, but it is cumulative. Aging equipment can lead to increased downtime and reduced efficiency. Over time, these issues can erode margins and disrupt revenue generation.

Leasing equipment also helps businesses avoid the long-term burden of obsolescence. As technology evolves, older equipment often becomes less efficient and more expensive to maintain. With a lease structure, borrowers have the flexibility to replace their assets at the end of the term and transition to newer, more efficient equipment without being locked into outdated technology.

For lenders, aging equipment introduces risk. A borrower operating with unreliable equipment may face inconsistent cash flow, making repayment capacity less predictable. Discussing the advantages of equipment leasing helps lenders shift customers' mindsets from cost avoidance to performance stability and shows how updated equipment supports stronger financial outcomes.

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Borrower hesitation signals an advisory opportunity

For banks or credit unions offering equipment leasing, client hesitancy creates an opening for lenders to step into a more advisory role. When a client delays an equipment decision, it is rarely just about rates. It often reflects uncertainty about timing, return on investment, or broader business conditions. Rather than focusing solely on loan terms, lenders should get familiar with the factors impacting equipment financing and guide conversations around:

Operational efficiency and lifecycle planning
Revenue impact of purchasing new equipment vs leasing
How new technologies are reshaping asset valuation

Having these discussions positions your institution as a strategic partner, not just a funding source.

Reframing financing around cash flow alignment

One of the most effective ways to shift borrower perspective is to focus on cash flow timing. Instead of making a large upfront payment to purchase equipment, leasing allows businesses to spread the cost over manageable monthly payments. This helps businesses maintain liquidity and frees up funds for other critical expenses.

Taking a cash flow approach can resonate strongly with borrowers who are hesitant to commit capital in uncertain conditions. Emphasizing how equipment financing supports flexibility can help clients see financing as a tool for managing financial pressure.

Strengthening relationships through proactive guidance

Banks and credit unions that actively engage clients in these conversations can differentiate themselves in a competitive lending environment. Rather than reacting to financing requests, they can anticipate needs and provide guidance before operational challenges emerge.

Helping borrowers understand the hidden costs of waiting—and the strategic benefits of acting—supports stronger client outcomes and more resilient loan portfolios. Ultimately, communicating the equipment finance advantage is not just about closing more deals; it’s about building long-term relationships grounded in insight and trust.

This blog was developed with the assistance of ChatGPT, an AI large language model. It was reviewed and revised by Abrigo's subject-matter expert for accuracy and additional insight.

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What does agentic LOS mean? What is it?

Advance lending efforts with agentic loan origination

In agentic LOS, software agents apply workflow context, policy logic, and human oversight across systems and documents throughout the lending cycle to help loans by banks and credit unions progress more consistently.

This article covers these key topics:

The challenges of loan origination
Agentic LOS in plain terms
How agentic AI differs from traditional AI and generative AI
Where financial institutions should start

The challenges of loan origination

Today’s loan origination process remains heavily manual, with bank and credit union staff spending significant effort on repetitive tasks.

Financial institutions report spending about 20 minutes per application on simple quality-control reviews and up to an hour on complex loans, even though most steps (document checks, policy exception validation, tickler and covenant setup, and cross-system reconciliation) are structured and repeatable.

At the portfolio level, the inefficiency compounds. Periodic loan reviews take 8+ hours per loan, translating to 1–1.5 months to complete just 20 credit risk reviews. This workload often forces teams to sample the portfolio rather than provide full coverage.

Fragmented systems, unstructured data, and frequent mismatches across credit memos, documents, and core systems drive continuous rework, missed exceptions, and heightened examiner risk for busy financial institutions.

The result is a high-cost, human-coordinated operating model where 60–70% of the workload could be automated through better orchestration and intelligence. This is exactly the kind of problem agentic systems are designed to solve.

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The friction behind lending workflows

Even with current processes, loan pipelines of financial institutions rarely break in obvious ways. Instead, they slow down quietly. A lender waits on a missing borrower document. An analyst tracks down a policy exception. A covenant requires manual setup and follow-up. Data needs to be reconciled across systems.

These small interruptions, repeated across every loan, create the operational drag that defines today’s experience. Some bankers describe it as a classic “human glue everywhere” system, with fragmentation across roles and functions.

This friction doesn’t exist in isolation; it spans the entire lending lifecycle. And it’s this friction-filled workflow that agentic AI is designed to address and reimagine, especially in the context of a loan origination system (LOS).

The lending lifecycle: Progress via coordination

Traditionally, loan origination is just one phase of a much broader lending lifecycle—from acquisition and credit evaluation to closing, servicing, and ongoing risk monitoring. Yet in most institutions, these stages operate as disconnected workflows, with data re-entered, decisions revalidated, and work repeatedly handed off at each transition. What begins as a borrower application turns into a series of fragmented processes, each introducing delay, rework, and risk.

An agentic LOS changes this by creating continuity across the lifecycle—ensuring that information, policy logic, and workflow context carry forward seamlessly, so lending work progresses as a coordinated system rather than a sequence of disconnected steps.

Agentic LOS, in plain terms

In plain terms, an agentic LOS shifts the system from tracking work to actively moving it forward. Instead of relying on users to push each step, software agents operate within the workflow: identifying missing information, validating documents, applying policy logic, coordinating next steps, and surfacing exceptions in real time.

They use context from across systems and documents to take action while keeping humans in the loop for approvals and judgment calls. The result is faster execution within a system that continuously drives loans toward completion.

How agentic AI differs from traditional AI and generative AI

Agentic LOS represents a fundamental shift from how automation has worked in lending.

Traditional software is procedural—users click through screens, enter data, and hand off tasks. Traditional AI can score or classify, and generative AI can summarize documents or draft narratives, but both are largely reactive, waiting for a prompt or input.

Agentic AI, by contrast, is goal-driven. It can recognize that a loan is incomplete, pull the relevant data, compare it against policy, initiate follow-ups, update workflows, and escalate issues when needed. Instead of supporting individual tasks, it orchestrates entire workflows, moving loan origination from a reactive process to a proactive, continuously advancing system.

Why agentic LOS matters

Loan origination is not a single decision for a financial institution. It is a chain of decisions, documents, handoffs, follow-ups, and controls. Over time, that chain has accumulated friction. A single loan can pass through multiple roles—relationship managers, analysts, underwriters, reviewers—each advancing the file step by step. Even with modern systems, the experience often feels like a digital version of passing paper from desk to desk.

That friction is more than just inefficiency. It is lost capacity. Time spent extracting data, checking documents, managing ticklers, validating covenants, and routing files is time not spent on borrowers, credit insight, or growth.

This is where an agentic LOS changes the equation.

Instead of workflows that wait on people, an agentic system actively moves work forward. It handles routine coordination, pulls context across systems, flags issues early, and ensures that required steps are completed consistently. The process becomes more continuous, less fragmented, and far less dependent on manual follow-up.

The impact compounds quickly. Cycle times shrink. Errors surface earlier. Audit trails improve. Borrowers experience faster, more consistent service.

More importantly, the role of people changes. Lenders and analysts spend less time managing process and more time applying judgment—understanding borrowers, structuring deals, and making better credit decisions.

Decision velocity increases. Operational drag decreases. Institutions gain the ability to scale lending without linearly scaling headcount.

That is the real promise of an agentic LOS—not just efficiency, but controlled growth.

What makes this a meaningful shift

The opportunity lies beyond simply automating tasks. Agentic LOS can translate credit policy, workflow rules, and institutional knowledge into a system that can act on them.

Done well, this strengthens relationship banking. By removing internal friction, institutions can deliver faster responses, clearer communication, and more consistent execution, while still keeping humans in control of decisions that matter.

Modernization, in this sense, stops being a back-office upgrade. This form of modernization is a front-line advantage.

Where financial institutions should start

The most effective starting point is not identifying the technology. It is workflow clarity.

Map the origination process end-to-end. Identify where time is spent, where delays occur, and which steps are repetitive, rules-driven, and prone to error. Then separate those from the moments that require human judgment, negotiation, or discretion.

Start with contained, high-impact use cases: policy exception validation, document comparison, covenant checks, tickler setup, and workflow bottleneck detection. These areas are measurable, lower risk, and create immediate operational lift.

Build with guardrails. Prove value. Expand deliberately.

Where this is headed

Adoption is still early, but momentum is building quickly. Generative AI is already in production across many institutions, and agentic AI is moving from experimentation to strategic priority.

The conversation is shifting from “What is this?” to “Where does it fit, and how do we govern it?”

To move successfully, financial institutions will want to balance urgency with discipline. Modernize data access, tighten policy logic, and focus on use cases that deliver real operational value without introducing unnecessary risk.

Finally, recognize that this is more than a technology shift; it is an operating model shift.

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FAQs

What is an agentic LOS?

An agentic LOS is a loan origination system that actively moves lending work forward rather than merely tracking tasks. Software agents within the LOS apply workflow context, policy logic, and human oversight across systems and documents throughout the lending cycle to help loans progress more consistently.

How does an agentic LOS compare with manual loan origination processes?

An agentic LOS provides more consistent workflow coordination than manual loan origination processes. Agentic loan origination can reduce reliance on staff to track missing documents, validate exceptions, reconcile information, and move files forward across disconnected steps.

How does an agentic LOS improve loan origination?

An agentic LOS improves loan origination by reducing manual coordination across documents, policies, systems, and approvals. It helps financial institutions address rules-driven, repetitive steps such as document checks, exception validation, covenant setup, tickler creation, and workflow follow-up.

How is agentic AI different from generative AI in lending?

Agentic AI differs from generative AI because it can coordinate actions across a workflow, while generative AI primarily creates or summarizes content. In an LOS context, agentic LOS uses agentic capabilities to identify incomplete files, compare data against policy, surface exceptions, and escalate issues for human review.

What lending tasks can an agentic LOS help automate?

An agentic LOS can help automate repetitive, rules-driven lending tasks such as document comparison, policy exception validation, covenant checks, tickler setup, and workflow bottleneck detection. Agentic LOS should ensure humans remain responsible for approvals, credit judgment, borrower negotiation, and decisions requiring discretion.

How can an agentic LOS help lenders and analysts?

An agentic LOS helps lenders and analysts spend less time managing processes and more time applying judgment where it is critical. Agentic loan origination can reduce operational drag by coordinating routine follow-ups, surfacing issues earlier, and helping teams focus on borrowers, deal structure, and credit quality.

What should financial institutions do before adopting an agentic LOS?

Financial institutions should clarify their lending workflows before adopting an agentic LOS. Abrigo recommends mapping the origination process end to end, identifying delays and repetitive rules-driven steps, and separating automation opportunities from decisions that require human judgment.

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Sanctions screening as part of a holistic risk prevention strategy

When a wire transfer appears routine, it can be easy to treat it as a processing task rather than a financial crime risk event. That is a mistake. Financial institutions should assume that bad actors moving funds across borders or through the U.S. financial system are actively looking for weaknesses in screening, data quality, escalation, and human review. In many cases, the goal is not simply to complete a payment but to disguise who is involved, where the funds are going, and why the transaction should have raised concern.

This is what makes sanctions evasion through wire activity such an important issue for anti-money laundering/combating the financing of terrorism (AML/CFT) teams. The risk is not limited to clearly foreign transactions or direct matches to a watch list. It often appears in altered entity names, missing address fields, layered ownership structures, routing through neutral countries, or payment details that do not align with expected customer behavior.

For community financial institutions, the takeaway is straightforward. The wire room, operations staff, and AML/CFT team all play a role in identifying suspicious activity before it becomes a missed alert or regulatory issue.

This article covers these key topics:

Evolving tactics
What institutions can do
Why sanctions screening is not a standalone task

Why wire transfers remain vulnerable

Wire transfers move quickly, involve multiple parties, and rely heavily on the quality of the data entered at origination. This creates an opportunity for abuse. Businesses attempting to avoid sanctions screening rarely rely on a single tactic. They may alter identifying details, use intermediaries, test payment channels, or exploit control gaps. A transaction that does not generate an alert is not necessarily low risk. It may indicate that key information was incomplete or manipulated.

Effective detection requires more than name screening. It depends on understanding context, reviewing supporting details, and recognizing when small inconsistencies form a larger pattern of sanctions evasion.

Common tactics used to avoid sanctions screening

Obscuring beneficial ownership: Layered entities, shell companies, nominee owners, or relatives listed as owners can make screening less effective. Ownership structures may also be designed to fall just below internal review thresholds. When beneficial ownership data is incomplete or inaccurate, institutions may screen the wrong party or miss the true risk.
Routing through intermediaries or neutral countries: Rather than transact directly with a sanctioned party or higher-risk jurisdiction, businesses may route payments through freight forwarders, import and export firms, law firms, or banks in countries that appear less suspicious. This can obscure the true origin or destination of funds. Transactions that involve unexpected routing or unnecessary intermediaries should prompt additional review, particularly when the structure does not align with the stated business purpose.

Misrepresenting transaction details: Altering payment data is one of the more common tactics. A business may omit the purpose of the payment, shorten a legal name, remove foreign company identifiers, or present a party as domestic rather than international. For example, removing suffixes such as Ltd., S.R.L., GmbH, S.A., or Pte. can affect whether a transaction matches. Using a U.S. address for an overseas entity without a clear explanation can create a similar risk.

Exploiting remote initiation channels: Online banking, mobile banking, and batch uploads can reduce opportunities for manual review. Foreign IP addresses, VPN usage, rapid fund transfers, and high-volume wire activity can increase risk, especially when the activity does not match the customer’s normal profile.
Taking advantage of weak screening logic: Outdated systems or poorly tuned rules may fail to identify misspellings, aliases, or non-Latin characters. Overreliance on exact matches and limited fuzzy logic capabilities can lead to false negatives. Institutions should regularly evaluate whether their screening tools are aligned with current risks and capable of identifying variations in names and identifiers.

Manipulating data to create false negatives: Some businesses intentionally omit or alter data fields that could trigger a match. Missing addresses, partial legal names, or inconsistent identifying details may appear minor on their own. Together, they can indicate deliberate sanctions evasion. Staff should be trained to identify these inconsistencies and validate information when needed, including comparing wire details to publicly available information.

Using alternative payment channels: Some actors move funds through digital assets, privacy-focused services, or noncompliant platforms to obscure transaction flows. Even when a financial institution is not directly handling these transactions, related wire activity may still reflect exposure.
Sending test transactions: Small-dollar wires can be used to test whether controls are functioning as expected. If a transaction does not trigger an alert, activity may escalate. Institutions should not assume that low-value transactions are low-risk.
Exploiting policy gaps and delays: Outdated policies and procedures, infrequent updates to the sanctions list, delayed alert reviews, and inconsistent training can create gaps that allow suspicious transactions to slip through controls. Timeliness and consistency are critical.
Avoiding the U.S. financial system: Some parties attempt to structure transactions outside of U.S. jurisdiction. However, exposure can still exist if U.S. dollar clearing occurs at any stage. Institutions should understand the full transaction flow, not just the immediate parties involved.

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What institutions should do

Financial institutions do not need perfect information to improve their response. They need strong controls, consistent processes, and well-trained staff.

Start by reviewing policies and procedures to ensure they reflect current wire risks, escalation paths, and departmental responsibilities. Confirm that screening occurs at appropriate points and that sanctions lists and system settings are updated regularly.

Training is equally important. Staff should be able to recognize missing or altered data, unusual routing patterns, and transactions that do not fit the customer profile. They should also understand when to escalate an issue as a potential financial crime concern rather than treating it as an operational exception.

Technology should also be evaluated. Screening systems should align with the institution’s risk profile and include appropriate fuzzy logic capabilities. Strong data quality and thoughtful system tuning can improve detection while reducing unnecessary alerts.

A broader approach to sanctions risk

Sanctions screening should not be viewed as a standalone system task. It is part of a broader risk management effort. Wire transfers provide valuable information beyond the payment itself. When institutions connect screening results with customer due diligence, transaction context, and ownership information, they gain a clearer picture of potential risk.

This approach is increasingly important as payment channels become faster and more complex. Institutions that strengthen controls, improve training, and maintain effective escalation processes are better positioned to manage the risk of sanctions evasion.

Conclusion

Wire transfers remain an essential service, but they also present ongoing risk. Businesses attempting to avoid sanctions screening may manipulate data, use intermediaries, or exploit process gaps.

For banks and credit unions, the path forward is clear. Maintain current policies and procedures, train staff to identify evasion tactics, and ensure screening tools are aligned with actual risk exposure. These steps support compliance while protecting the institution, its customers, and the financial system.

FAQs

Why are wire transfers considered high risk for sanctions evasion?

Wire transfers move quickly, involve multiple parties, and depend heavily on data quality, making them vulnerable to manipulation and abuse.

How do businesses attempt to evade sanctions screening?

They use tactics such as altering entity names, hiding beneficial ownership, routing through intermediaries, and manipulating transaction details.

What are false negatives in sanctions screening?

False negatives occur when suspicious transactions are not flagged due to incomplete, inaccurate, or deliberately altered data.

What should financial institutions do to reduce sanctions risk?

Institutions should maintain updated policies, train staff to identify evasion tactics, and ensure screening tools are aligned with current risks. during transactions, such as anxiety, confusion, or secrecy, which means detection should combine software and staff observation.

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Abrigo ThinkBIG's Innovation Hall showcasing AI solutions

Familiar pressures, new AI territory for financial institutions

Key topics covered in this post:

Supporting better decisions
Execution begins with useful work
AI’s value depends on data, automation, and context
Governance and trust are adoption requirements

Supporting better decisions with AI

Financial institutions are digging deeper into artificial intelligence (AI) at a time when familiar pressures are intensifying: loan and deposit growth, efficiency, fraud, cybersecurity, credit quality, staffing constraints, and rising customer expectations. Boards and regulators are asking questions, too, pushing AI planning, governance, and accountability to the forefront.

That shift from experimentation to execution was a central theme at Abrigo’s recent ThinkBIG conference, where presenters discussed how banks and credit unions can use AI to support better decisions without losing the required oversight. Speakers emphasized applying AI in the right business context, with the right policies, data, and human involvement.

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Jay Blandford, Abrigo CEO

Abrigo CEO Jay Blandford told nearly 1,200 attendees from banks, credit unions, and conference sponsors that AI can help institutions scale insights while creating more opportunities to strengthen relationships, a core advantage for banks and credit unions that compete on local knowledge and service.

“Relationships scale trust, and that’s your real competitive moat,” he said.

Execution begins with useful work

Several speakers suggested focusing AI execution first on practical work: specific tasks that can be defined, reviewed, and improved. Using artificial intelligence doesn’t have to put the bank or credit union at unnecessary risk, and it doesn’t have to solve everything or the institution’s biggest problem to start.

“The goal isn’t to get AI perfect; it’s to start building the capability,” said Andy Snow, Abrigo’s Chief Customer Officer. “Waiting is a decision. It is not free.”

For many institutions, that means starting with a narrowly defined workflow, identifying where staff review is required, documenting how outputs will be checked, and measuring whether the tool improves speed, consistency, or capacity before expanding the use case.

Responsible action starts by using AI where risk is lower and value is clear. In fact, a good approach is to begin by identifying repetitive tasks that can be reviewed and controlled, or work that is costly, slow, or overly manual. That’s often the work where AI can improve consistency and give employees more time for judgment-based work.

Applying AI to work that is repeatable, time-consuming, and well understood will help institutions build confidence and further focus their AI efforts.

Melissa Marsal, right, and John Brichetto, left, at Abrigo's ThinkBIG conference on May 5, 2026

Melissa Marsal, a former community bank CEO and COO and now a community bank advisor, said the clearest AI opportunities for many will be tied to operational efficiency in processes governed by rules or defined workflows. She pointed to exception-item processing, document management, training procedures, and anti-money laundering/countering the financing of terrorism (AML/CFT) alert triage as examples of areas where institutions are engaging.

Tackling those areas can reduce the time talented staff spend on repetitive back-office tasks, giving them more capacity to interact with customers, review exceptions, or support higher-value work, she said.

“At the institutions that I’ve seen that have leaned into it, it’s working well,” Marsal said.

AI’s value depends on data, automation, and context

For financial institutions, it’s vital that AI be purpose-built for specific tasks, said Abrigo Chief Technology and Product Officer Ravi Nemalikanti.

Financial institutions face a convergence of technology-driven forces that can feel overwhelming, he said. “On one end, we have the speed of intelligence changing, driven by AI. On the other, changes in money movement are driving faster expectations for the speed of execution.”

General AI can provide generic answers and lacks a true understanding of specific business contexts. But Nemalikanti described how purpose-built agentic AI can understand the context, necessary details, and wider business process of an operating framework. Integrated into systems like loan origination, it can better understand data within the workflow to craft appropriate, specific responses at the right time and automate credit origination steps. “This is where we move from answers to action,” he said.

As work moves through end-to-end workflows, the AI begins to coordinate steps. Use cases could include reviewing credit prescreening results, collecting documents, and setting up ticklers.

The technology becomes more useful because it operates within the institution’s operating framework, helping teams act with better information, more consistent processes, and the oversight the banking industry requires.

“These are deployed into your environment to understand your data sets, your workflows, and most importantly, your policies,” Blandford said.

Governance and trust are adoption requirements

Just as financial institutions vary their initial AI use by risk, banks and credit unions should ensure that the governance and autonomy granted to AI actions align with their policies, risk appetite, documentation requirements, and regulatory expectations.

Governance was a recurring theme across the sessions. Snow said leaning into AI will be easier by working with “the right partner, someone you trust that understands your business and is not going to put you into a reckless situation.”

That guidance is especially important in financial services. AI tools need to fit into data privacy expectations, policy controls, auditability, and governance.

Employees also need enough education to understand where AI can help, where it needs review, and where human judgment remains essential, especially in banking.

Speakers consistently positioned AI as a way to support people rather than displace the value of banker judgment.

“There are some amazing things happening with AI,” said Brad Schaefer, Abrigo Vice President, Product. “But we feel the human interaction and touch and judgment is going to be the driving factor to make that productive.”

Ultimately, the speakers framed AI as a way to give financial institution staff more time and better information for the relationship-centered work that sets community institutions apart. “Relationships are what build trust, and trust is the foundation for how you differentiate yourself,” Blandford said.

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Elder Fraud in the age of artificial intelligence

As artificial intelligence (AI) evolves, fraudsters are using it to refine their targeting of older adults. Scams that once required time and effort can now be executed faster, at greater scale, and with convincing detail.

The impact is significant. According to the FBI’s Internet Crime Complaint Center (IC3), seniors lost $7.75 billion in 2025, and losses continue to rise. Financial institutions are seeing a shift in which AI is increasingly used to enhance fraud schemes, while human manipulation remains at the core. Bad actors are blending advanced tools with careful research and patience to exploit trust. AI-driven elder fraud is now both a technology challenge and a relationship challenge, requiring institutions to respond with equal parts innovation and awareness.

The evolving tactics behind elder financial exploitation

Fraud schemes targeting seniors are not new. Phishing attempts, romance scams, and investment fraud have existed for years. What has changed is the level of sophistication and personalization.

Fraudsters are now using generative AI tools such as voice cloning and deepfakes to make their outreach more believable. A deepfake can replicate a person’s voice, image, or video in a way that feels authentic, even to someone who knows the individual well. Criminals often gather details from social media or other public sources, studying their targets before making contact.

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This preparation is important to understand. While AI accelerates execution, bad actors are still doing their homework. They learn family names, travel plans, and communication styles. They understand how a grandchild speaks to a grandparent or how a trusted contact might phrase a request. That human intelligence, combined with AI, creates a powerful and dangerous mix.

Social engineering tactics remain highly effective. Romance scams, in particular, continue to be widely used because they rely on building emotional connections over time. These fraudsters are patient. They invest weeks or months in building trust before making a financial request. AI may help scale their efforts, but success still comes from manipulation rooted in human behavior.

Consider an AI-enhanced grandparent scam. A fraudster reviews a grandchild’s social media, noting a trip abroad and a nickname like “Nana.” Using voice cloning, they place a call that sounds like the grandchild in distress, asking for urgent help with bail money. The voice's realism, combined with accurate personal details, creates a sense of urgency that can override caution.

What once might have raised suspicion now feels credible. That shift is what makes AI-driven elder fraud particularly challenging to detect.

What financial institutions can do to fight elder fraud

Financial institutions play a critical role in protecting older adults. The combination of technology, staff awareness, and strong customer relationships remains the most effective defense.

Robust fraud detection systems should be configured to identify unusual activity, especially for accounts held by older clients. Monitoring for sudden wire transfers, atypical withdrawal patterns, or new payees can help flag potential fraud early. Tailoring these parameters to customer behavior improves detection and response times.

Equally important is ongoing staff training. Front-line employees and fraud teams need to recognize the signs of both AI-driven scams and traditional social engineering. Customers who appear anxious, confused, or unusually secretive during transactions may be under pressure. Training should focus not only on identifying red flags but also on responding with empathy and clarity.

Clear communication protocols also matter. Reinforcing that the institution will never request sensitive information through unsolicited calls, emails, or texts helps set expectations. This becomes even more critical as AI-generated messages and voice calls become harder to distinguish from legitimate ones.

Strong customer relationships remain one of the most effective tools. A simple conversation can uncover concerns that technology alone might miss. When staff feel comfortable asking questions about unusual transactions, they create opportunities to pause and verify before releasing funds from the account.

Understanding regulatory expectations

Regulators continue to emphasize the importance of detecting and reporting elder financial abuse. Fraud remains a national priority, and institutions are expected to adapt as threats evolve.

Filing suspicious activity reports is only one part of the response. Institutions must demonstrate a broader culture of vigilance, supported by training, staffing, and effective processes. FinCEN has emphasized that adequate staffing and resources are critical to maintaining an effective AML/CFT program, including timely detection and reporting of suspicious activity.

Collaboration between fraud and anti-money laundering teams is increasingly important. Complex fraud schemes do not fit neatly into one category, and siloed approaches can slow detection. Integrating insights across teams allows institutions to respond more effectively to emerging threats.

Community education

Technology alone will not solve this challenge. Many cases of elder fraud can be prevented through consistent and practical education. Hosting in-person sessions at branches, senior centers, or community organizations provides an opportunity to explain how modern scams work. Demonstrating how AI can replicate voices or create realistic messages helps make the risk more tangible.

Partnerships with local law enforcement or community groups can extend reach and reinforce credibility. Printed materials, short videos, and account alerts can also help keep fraud prevention top of mind between interactions.

Education works best when it is ongoing. A single conversation may not be enough, but consistent messaging builds awareness and confidence over time.

Practical tips

Simple, actionable guidance can help seniors protect themselves from increasingly sophisticated fraud attempts. Encourage clients to verify unexpected requests by contacting the person or organization using a known, trusted number. Remind them to be cautious with urgent messages, especially those that involve pressure to act quickly.

Account alerts can provide early warning signs of unusual activity, allowing for faster intervention. Regularly reviewing account statements or involving a trusted family member can add another layer of oversight.

Clear, straightforward communication is key. When clients understand what to watch for and how to respond, they are better equipped to avoid becoming victims.

Protecting seniors in a rapidly changing fraud environment

AI has changed the speed and scale of fraud, but it has not replaced the human element. In many ways, it has amplified it. Fraudsters are pairing advanced technology with deliberate research and well-practiced manipulation tactics that have proven effective for years. For financial institutions, the path forward is not about choosing between technology and human insight. It is about strengthening both.

By investing in fraud detection, prioritizing staff training, and maintaining strong relationships with customers, institutions can better protect older adults and reinforce trust in their communities. Staying proactive today is essential to managing the risks of tomorrow.

FAQs

What is elder fraud detection software for banks and credit unions?

Elder fraud detection software helps financial institutions identify suspicious transactions and behavioral red flags that may signal financial exploitation of older adults. In this context, Abrigo’s fraud detection approach combines transaction monitoring, staff awareness, and customer insight to help banks and credit unions respond earlier to AI-enhanced scams

Why is elder fraud harder to detect in the age of AI?

Elder fraud is harder to detect because fraudsters now use generative AI, voice cloning, and deepfakes to make scams more believable and more personal. The article explains that AI increases scale and realism, but the fraud still succeeds through human manipulation, emotional pressure, and careful research.

How does fraud detection software help prevent elder financial exploitation?

Fraud detection software helps prevent elder financial exploitation by flagging unusual account activity such as sudden wires, atypical withdrawals, or new payees. Abrigo’s fraud detection framing also stresses configuring alerts around customer behavior so institutions can investigate faster and intervene before funds leave the account

What should banks monitor for possible elder fraud?

Banks should monitor for transaction patterns that fall outside a customer’s normal behavior, including sudden wire transfers, unusual withdrawals, and newly added payees. The article also points to behavioral warning signs during transactions, such as anxiety, confusion, or secrecy, which means detection should combine software and staff observation.

Why does human insight still matter in fraud detection software?

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