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This article covers these key topics: 

What AI explainability means and why it matters in financial services 

As financial regulators focus on AI governance, explainable AI is quickly becoming a regulatory expectation. Learn explainability techniques and what banks and credit unions should ask AI vendors.

How does explainable AI in banking compare to black-box AI? 

Explainable AI (XAI) is any artificial intelligence system that shows why it generated a certain output, including what influenced it, how, and how much. Black-box AI produces outputs without enough transparency to understand the model’s approach, limitations, reliability, or the factors driving a specific decision.

For banking compliance officers and other technology decision makers, AI explainability is tied to regulatory and examination expectations, so it is a business necessity rather than merely a technical consideration. An opaque AI model can make it harder for financial institutions to validate, document, and defend AI-driven decisions, whether they are in credit underwriting, financial crime investigations, customer service, or other areas. Financial institutions must know how the AI works and how to know if it’s working right.

Here’s what examiners expect, how XAI works in practice, what questions every bank or credit union should ask an AI vendor, and how Abrigo delivers explainable AI solutions.

Related webinar: "Defensible AI in financial services: How to operationalize AI safely and effectively"

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Explainability is becoming a compliance requirement

As financial regulators increase their focus on AI governance, explainable AI is quickly becoming a regulatory expectation. Across the banking industry, regulators have sent the same message: if AI influences an important business decision, the institution must be able to explain how the technology reached its conclusion. Saying a model is a "black box" is insufficient.

SR 26-2 and model risk management

The expectation starts with model risk management. Earlier this year, the Federal Reserve, FDIC, and OCC issued SR 26-2, which replaces SR 11-7 and SR 21-8. At its core, the guidance says institutions need to understand and manage their models well enough to manage the risks of their use. That includes model design, assumptions, data, methods, limitations, performance, and monitoring. The principles apply to AI and machine learning models used in banking as well as traditional statistical and quantitative models. Credit unions should view these expectations through their own supervisory framework, including NCUA’s risk-management focus for AI use.

FFIEC IT Examination Handbook

But explainability extends well beyond model risk. The FFIEC IT Examination Handbook warns that AI lacking transparency or explainability can be “unclear how inputs are translated into outputs,” increasing compliance and operational risk.

Reg B

Regulation B requires creditors to provide specific reasons for adverse credit actions, including when AI is involved. Saying the technology was too complex or opaque to understand doesn’t void the obligation. Fair lending reviews also depend on understanding why models produce the outcomes they do related to credit access, pricing, and underwriting. AI explainability is essential for identifying and managing potential bias.

Other organizations

Other leading organizations reinforce an emphasis on explainability. The Financial Action Task Force (FATF) has highlighted explainability and transparency as key considerations for AI used in AML and financial crime solutions. The GAO defines explainability as the ability to understand how and why an AI system produces its decisions, predictions, or recommendations. The National Institute of Standards and Technology (NIST) builds on that definition with four principles: explanations should be supported, meaningful to the intended audience, faithful to the model's actual behavior, and transparent about the model's knowledge limits.

These principles align closely with what banking supervisors increasingly expect. Whether the issue is model risk management, fair lending, or AML, the question is ultimately the same: Can the institution explain how its AI reached a decision and when that decision should not be trusted? That's the difference between explainable AI and a black-box model. It is also the difference between AI that can withstand regulatory scrutiny and AI that cannot.

How explainable AI works in practice

AI explainability isn't one-size-fits-all. The degree and output should fit the use case and depend on the type of AI and the decision being made. For predictive AI, explainability focuses on why a model made a prediction or score. For generative AI, it's about understanding how a response was produced, including the prompts, retrieved information, and supporting evidence. For agentic AI, explainability extends a step further, capturing why the AI chose a particular course of action and the sequence of decisions it took to complete a task.

Regardless of the technology, the goal is the same: make AI decisions understandable, traceable, and defensible. In practice, XAI can utilize many techniques. These include feature attribution, reason codes, confidence scores, prompt and response traceability, supporting evidence, action logs, or other artifacts that help analysts, validators, auditors, and examiners understand how an AI system reached its conclusion.

Two explainability techniques common in banking AI

For predictive AI models, the two explainability techniques financial institutions are most likely to encounter are SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). They are not regulatory requirements, but they have become widely used methods for opening the black box.

SHAP

SHAP explains why a model reached a specific score or decision by showing how much each input contributed to the outcome. For example, in banking, it can identify whether cash flow, debt levels, or transaction behavior had the greatest influence on a credit decision, fraud score, or AML alert. Being able to attribute specific influencing features can be useful for supporting internal review, model validation, and adverse action analysis.

LIME

LIME takes a different approach. Rather than assigning contribution values to each feature, it builds a simpler approximation of the model around a single prediction. The surrogate model helps analysts and validators better understand the model's behavior for that specific decision.
The important point isn't whether an AI solution uses SHAP, LIME, or another explainability technique. It's whether the system can produce clear, defensible explanations and the evidence to support them that stand up to model validation, audits, and regulatory examinations.

Graphic showing AI explainability techniques

Banks and credit unions evaluating AI vendors should ask whether the system provides SHAP, LIME, reason codes, or an equivalent method for explaining individual outputs. The answer should include how explanations are generated, whether they can be exported, who can access them, and how they are documented for validation, audit, and examination.

Examples of problems with black-box AI

Black-box AI creates practical problems because banking decisions must be explainable to several audiences: customers, compliance staff, validators, auditors, management, boards, and examiners. Predictive performance alone does not answer the institution’s governance questions. Examples of how black-box AI can be problematic include:

  1. Loan denial with no usable reason code. A credit model recommends denial, but the institution cannot identify the principal factors that drove the decision. That creates adverse action risk under Regulation B and CFPB guidance.
  2. Fraud alert with no explainable trigger. If an analyst cannot determine which activity caused a fraud detection system to flag an account or transaction, the review may take longer. Documentation may be weaker, and confidence in the alerting process may decline, as the FFIEC Handbook warns.
  3. Model drift goes undetected. A model may perform differently over time as borrower behavior, market conditions, products, or data quality changes. SR 26-2 describes ongoing monitoring, including whether a model continues to perform as expected, as part of model risk management. An institution that cannot understand what is changing in the model’s behavior may have difficulty identifying problems or deciding when remediation is needed.
  4. Fair lending review cannot identify outcome drivers. An examiner, auditor, or compliance officer asks which variables influenced different outcomes across applicants or markets. If the institution cannot evaluate the drivers of model outcomes, it may struggle to assess fair lending risk, test for unintended bias, or explain how the model is being controlled.

An explainability checklist for AI vendors

Vendor due diligence should include whether the financial institution can oversee, validate, monitor, and document the AI system throughout its lifecycle.

Ask vendors these XAI questions:

  • What is the model’s intended use, and what uses are out of scope?
  • What data is used to train, test, and operate the model?
  • What validation documentation is available, including assumptions, limitations, methodology, and performance?
  • Can the system explain individual decisions, recommendations, alerts, or scores?
  • Does the system provide SHAP, LIME, reason codes, feature attribution, or an equivalent explanation method?
  • Can explanations be exported for audit, validation, or examiner review?
  • Does the system support accurate adverse action reasons when used in credit decisions?
  • How are performance, drift, data quality, retraining, and version updates monitored and documented?
  • What audit trail is maintained, including inputs, outputs, timestamps, users, overrides, and final decisions?
  • How does the vendor support independent validation, examiner review, and alignment with FFIEC, consumer compliance, and internal governance expectations?
  • What controls prevent unauthorized changes or inappropriate use?
  • For community financial institutions, the goal is practical exam defensibility. The institution should be able to show what the AI system does, how it is controlled, how performance is monitored, how exceptions are handled, and how decisions or recommendations can be explained.

How Abrigo delivers explainable AI

Explainability isn't something Abrigo adds after an AI model is built; it's designed into the entire AI lifecycle. Whether AI is making a prediction, generating a recommendation, or executing an agentic workflow, every outcome should be understandable, auditable, and aligned with the institution's governance requirements.

Strong model governance

Abrigo starts with strong model governance. AI models undergo independent third-party validation before deployment and are supported by comprehensive documentation describing their intended use, development methodology, assumptions, limitations, performance, governance, and monitoring. This gives financial institutions the information they need to incorporate Abrigo's AI into their own model risk management programs.

Decision-level explainability

For AI-driven predictions, Abrigo provides decision-level explainability using SHAP-based feature attribution. Rather than presenting only a score, the platform identifies the factors that most influenced the outcome and translates them into intuitive, human-readable explanations. Analysts can understand what drove a credit decision, fraud score, or AML alert, making decisions easier to review, document, and defend.

Graphic showing Abrigo Fraud Detection's transparency dashboard

Traceability

For AI-generated recommendations and agentic workflows, explainability comes through traceability. Abrigo records user prompts, AI responses, supporting citations, timestamps, user identity, interaction history, and administrative actions, creating a comprehensive audit trail that can be exported for governance, audit, and examiner review. As AI agents perform increasingly sophisticated tasks, Abrigo also emphasizes policy alignment, human oversight for higher-risk situations, and continuous quality controls so institutions retain control over AI-assisted actions.

Model monitoring

Explainability also extends beyond individual decisions. Abrigo continuously monitors its consortium-trained AI models for performance, feature stability, data drift, and prediction quality. When meaningful changes are detected, models are reviewed and refreshed as appropriate to help ensure they continue to perform as intended.

Ultimately, AI explainability is about trust. Analysts need to understand what drove a recommendation. Validators need evidence that models behave as expected. Auditors need a complete record of AI activity. And examiners need confidence that AI-assisted decisions can be understood, challenged, and defended.

That's the philosophy behind Abrigo's AI platform. Abrigo’s AI is built not only to be intelligent but also explainable, auditable, and governed from end to end.

Explainability from the start for AI governance

In a regulated environment, an AI system’s value depends on whether the institution can use it responsibly, control it effectively, and explain it when asked. Explainability should be part of AI governance from the start. Financial institutions evaluating AI should require clear documentation, individual-level explanations where needed, audit trails, performance monitoring, and vendor support for validation and examiner review.

The information, content, and materials provided through this website are for informational purposes only and are not intended to constitute legal advice. Customers should consult with their legal counsel regarding the application of laws and regulations to their specific circumstances.

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Not all developers of banking AI technology are the same 

Regulated financial institutions require AI technology designed for data protection, auditability, and human involvement.  Here’s Abrigo’s AI development approach.

This article covers these key topics: 

"Nothing can be a black box."

Financial institutions cannot afford mystery in their technology. They need tools that protect data, support auditability, and help teams understand how outputs are produced. As artificial intelligence becomes part of more banking workflows, those expectations should guide how AI is developed, deployed, and reviewed.

I recently spoke with Danny Piangerelli, Abrigo’s Senior Vice President of Technology, on Abrigo’s “Ahead of the curve” podcast for bankers, and we discussed responsible AI in banking and what it takes to build AI tools for regulated institutions.

Piangerelli leads data and AI platform engineering at Abrigo, and his approach starts with the environment in which banks and credit unions operate. Financial institutions are regulated and audited. Their vendors have to account for those realities from the beginning.

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As Piangerelli put it, “Nothing can be a black box.”

This idea is central to how Abrigo develops responsible AI in a regulated environment. AI can help users find information faster, generate drafts, review patterns, and interact with complex data in more natural ways. For those tools to be useful in banking, they also need to be secure, explainable, and transparent.

Responsible AI starts with the realities of banking

AI can feel new, but Abrigo’s approach to new technology is grounded in long-standing company principles: protect customer data, protect access to that data, and give users visibility into how systems work.

During our discussion, Piangerelli described Abrigo’s starting point as consistent with other technology decisions the company has made over the past 20 years. Each unique financial institution must be in the driver’s seat and able to defend its software to auditors and examiners.

For AI development, the practical test is straightforward, with focus areas like.

  • Can the data be protected?
  • Can users explain what the system is doing?
  • Can the user review the output?
  • Can the institution understand how the tool fits into its workflow and risk framework?

Responsible AI in banking must address those questions before a tool can be trusted for real work inside a financial institution.

Secure AI depends on data protection and controlled access

One of the clearest risks with AI in financial services is data exposure. Banks and credit unions often want to use AI to search policies, summarize documents, answer questions, or support staff. They also have sensitive data that cannot be treated casually.

Abrigo heard this directly from customers, Piangerelli said. Financial institutions wanted the usefulness of a ChatGPT-like experience, but they could not upload private documentation into public tools.

Abrigo’s response with AskAbrigo, our AI-powered banking agent, was to provide a place where customers could upload and interact with their own documentation. The goal was to give users access to AI-powered knowledge assistance while keeping their information within a controlled environment.

He described it as giving customers “a place within the defined and secure Abrigo-hosted environment, where all their other applications and data have been hosted, to upload safely and to be able to interact with those documents safely through this knowledge agent.”

Secure AI for financial institutions also requires boundaries between private data and public research. Customers may want internet-enabled capabilities for public information, while their internal documents and customer data remain protected.

“[Financial institutions’] data is still kept private, and it's not sent out to the internet in any of these searches,” Piangerelli said. He added that Abrigo prevents the agent from accessing internally uploaded data when the user is interacting with the internet.

For banks and credit unions, those controls make AI more usable. Teams get flexibility without sending sensitive information outside the appropriate environment.

Explainable AI gives users visibility into outputs

Responsible AI in banking also requires explainable AI. Financial institutions need to know where answers, alerts, summaries, and narratives come from. Piangerelli says Abrigo tools are built with “the ability for the systems to be able to explain and audit what they're doing and decisions they're making.”

The form of explainability depends on the use case.

For Abrigo’s anti-fraud models in Abrigo Fraud Detection, the models are designed to produce an explanation of how they arrive at an answer, including which values were weighted more heavily. In AI-generated narratives or assistant-style tools, explainability may come through documentation and source visibility. When AskAbrigo answers questions using documentation or data, Abrigo provides references that show where the answer came from. Visibility into model behavior helps users understand why a model is surfacing a result. It also supports stronger review, escalation, and documentation.

Anyone who has used AI tools for research or drafting knows how useful source visibility can be. A polished answer can still be wrong, incomplete, or unsupported. In banking, users need a way to verify the answer and decide whether it is usable.

Transparent AI keeps users in the workflow

Transparency also means making clear what role the AI plays. Abrigo’s approach keeps people involved in review and decision-making. Piangerelli described the narrative use case, which Abrigo incorporates as a draft-and-edit workflow in solutions for financial-crime fighting, credit-memo generation, loan review, and allowance for credit losses reporting. The AI can generate text. The user, he said, “can agree or disagree or edit it or delete it or do whatever they want.”

Human review is essential for regulated workflows. AI can help users move faster, but the user still brings judgment, institutional knowledge, borrower context, and accountability.

I thought about this in the context of my own work with transcripts. I may use AI to help summarize a long discussion or draft content from a webinar, but I still need to review the result closely. I need to know where the content came from, whether the quotes are exact, and whether the draft reflects the speaker’s meaning.

The same principle applies inside banking workflows. A model or assistant may surface information, create a first draft, or help a user explore data. The person using the tool still needs confidence in the source and control over the final output.

Transparent AI helps create confidence by showing the user what the system used, how the output was created, and where human review belongs.

Abrigo’s AI development approach focuses on empowerment

One of the parts of our conversation on Abrigo’s approach that stood out was Piangerelli’s explanation of how Abrigo thinks about AI and team development. He described Abrigo’s approach as the “Iron Man approach.”

“Instead of building a robot that goes and does your job, what if we built an Iron Man suit?” he said. “You're still in control, but now you are empowered to do some big-time stuff that you couldn't do in the past.”

That is a useful way to think about responsible AI in banking. The strongest applications of AI help skilled people at banks and credit unions do more with the knowledge, judgment, and experience they already have.

For developers, AI may help generate tests, review code, create documentation, or support product workflows. For product managers, it may help create prototypes or documentation that communicate an idea earlier in the process. For bankers, it may help users find information faster, draft narratives, review data, or reduce repetitive work that slows down customer-facing activity.

The common thread is control. The user remains responsible for reviewing the work and deciding how to apply it.

Responsible AI should build on a trusted foundation

We also discussed an important point about pace. AI is developing quickly, and the pressure to react can be intense. Abrigo’s approach for its financial institution customers is to build from a strong foundation and keep customer trust at the center of the work.

“We've built a business on top of a really secure, very resilient underlying data system, structure that passes all of the regulations, passes all of the audits,” Piangerelli said. “On top of that, we've built software that our customers are pleased with. It is growing, it is getting better, it's getting stronger.”

Abrigo uses that foundation to evaluate AI opportunities. The goal is to bring useful AI into financial institution workflows at a pace customers can depend on, allowing banks and credit unions to adopt AI as they’re comfortable doing so.

In other words, Abrigo is focusing on “the latest and greatest” while incorporating it “at a pace that our customers can really depend on, and trust, and still be out in front,” he said.

Responsible AI in banking requires innovation and continuity. Financial institutions need tools that help them adapt, along with confidence that the systems supporting their work remain secure, explainable, and auditable. The track record Abrigo already has of doing each of these with some 2,400 financial institutions should support confidence in the vendor partnership as banks and credit unions move more into using AI.

What responsible AI looks like at Abrigo

For Abrigo, responsible AI in a regulated environment comes down to several practical commitments. It means:

  • Building AI inside secure environments designed for banks and credit unions.
  • Protecting institutional and customer data.
  • Creating clear boundaries between private documentation and public research.
  • Giving users source references and explanations.
  • Keeping people in control of review, edits, and final decisions.
  • Designing tools that help teams work faster without hiding how outputs are created.

Those principles are critical as AI becomes more embedded in banking workflows. The technology will keep changing, so financial institutions using AI successfully will need systems they can trust, vendors that understand regulation, and tools that support human judgment.

Responsible AI in banking requires discipline. At Abrigo, it also starts with a simple expectation: no black boxes allowed.

Need help adopting AI with confidence and control? Our advisors can help with policy development and governance structure.

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Is your financial institution ready to deter AI-enabled elder fraud?

For years, financial institutions have worked to protect older adults from elder financial abuse ranging from government impersonation schemes to romance fraud and fraudulent investment opportunities. While these scams are not new, the tools criminals use today are dramatically different.

Artificial intelligence (AI) has transformed the fraud landscape, enabling bad actors to create convincing voices, realistic videos, sophisticated messages, and entirely fabricated identities at a scale we have never seen before. For financial institutions, this means traditional fraud indicators are becoming harder to spot, and the consequences for customers can be devastating.

The question is no longer whether AI will impact fraud. The question is whether financial institutions are prepared for the new generation of AI-enabled scams.

Learn how Abrigo Fraud Detection prevents elder fraud

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How AI is affecting elder fraud

Older adults remain one of the most targeted populations for fraud. According to the FBI, adults over 60 lost more than $7.7 billion in 2025, up 59% from the previous year. Seniors often have significant accumulated assets, may live alone, and frequently place trust in authority figures and personal relationships. According to industry estimates, only a fraction of elder financial abuse incidents are ever reported due to a variety of reasons, such as shame, embarrassment, and sometimes the victim's diminished mental capacity.

Artificial intelligence has amplified the effectiveness of these scams in several ways:

  • Personalized fraud campaigns can be created almost instantly
  • Scam communications contain fewer grammatical mistakes and obvious warning signs
  • Criminals can rapidly adapt their tactics based on victim responses
  • Emotional manipulation becomes more convincing and more scalable

In other words, AI allows fraudsters to automate trust-building.

 

Leveraging AI voice technology for fraud

One of the most concerning developments is voice cloning technology. With only a few seconds of audio obtained from social media videos, voicemail recordings, or other public sources, criminals can create convincing replicas of a person’s voice. A grandparent may receive a frantic call that appears to come from a grandchild claiming to be in trouble. The voice sounds authentic. The story sounds urgent. The request for money feels legitimate.

The victim often acts before verifying the situation. For front-line bank staff, this creates a challenge. Customers may arrive convinced they are helping a loved one or responding to an emergency. What appears to be an ordinary wire transfer may actually be the result of sophisticated AI-enabled social engineering.

 

Deepfakes create new risks

AI-generated images and videos are also creating significant challenges for financial institutions. Fraudsters increasingly combine stolen personally identifiable information with AI-generated images to create synthetic identities. These identities can be used to facilitate:

  • Fraudulent account openings
  • Check fraud
  • Credit card fraud
  • Loan fraud
  • Employment fraud
  • Online scams

The challenge is that many traditional identity verification processes were not designed to detect AI-generated personas.

Financial institutions should be aware of warning signs such as inconsistent identity documents, suspicious technical issues during remote verification sessions, refusal to complete multifactor authentication, and photos that appear altered or inconsistent with other identifying information.

The emergence of deepfake technology has become such a concern that FinCEN issued an alert highlighting its use in financial crimes and encouraging institutions to identify and report related activity appropriately.

 

How is AI being used in Romance scams?

Perhaps nowhere is AI’s impact more evident than in romance and investment scams. Historically, fraud investigators could often identify fraudulent profiles through poorly written messages, inconsistent stories, or obvious fake photographs. AI has changed that equation.

Today’s criminals can generate realistic photos, create believable online personas, and maintain sophisticated conversations over extended periods. These tools allow fraudsters to build trust faster and with greater credibility.

“Pig butchering” schemes, a type of sophisticated investment fraud, have become one of the fastest-growing fraud threats affecting older adults. This troubling analogy refers to a manipulation technique that exploits a victim's vulnerabilities through frequent interactions, text messaging, and social engineering. Today, these usually involve investment schemes and cryptocurrency fraud.

Victims may spend months communicating with someone they believe is a romantic partner or a trusted friend before the conversation shifts toward an investment opportunity. The fraudster then introduces cryptocurrency investments, exclusive trading platforms, or supposedly guaranteed returns. Fake account dashboards display fabricated profits, reinforcing the victim’s confidence.

By the time fraud is discovered, retirement accounts may have been liquidated, and life savings lost. Many seniors have outlived their earning capacity and are no longer able to make up a significant financial loss, which often leads to depression and even premature death.

The human element remains the strongest defense

Despite increasingly sophisticated technology, AI-enabled fraud still relies on human emotions.

Fear. Trust. Loneliness. Urgency.

Financial institutions remain uniquely positioned to identify these situations before losses occur because they can observe both transactional activity and customer behavior.

Some common warning signs include:

  • Sudden large wire transfers to unfamiliar recipients
  • New cryptocurrency activity that is inconsistent with the customer's history
  • Liquidation of retirement assets for unexplained investments
  • Customers who appear coached, fearful, or unusually secretive
  • Requests that follow urgent phone, video, or online communications

In many cases, the transaction itself is only part of the story. The customer’s behavior often provides the strongest indication that fraud may be occurring.

 

Why early intervention matters

One of the most difficult realities of elder fraud is that victims often believe they are making informed decisions. The customer who is sending funds to a fraudulent investment platform may be convinced they are building wealth. The customer responding to a cloned voice emergency may be certain they are helping a family member. That is why early intervention is critical.

A delayed transaction, additional questioning, or escalation to a fraud specialist can prevent life-altering losses. Financial institutions should empower front-line employees to slow down suspicious transactions, document behavioral observations, and escalate concerns, even when the customer appears confident. Training, collaboration between fraud and AML teams, and strong internal procedures remain essential components of an effective response strategy.

Detection technology must evolve just as quickly as fraud. Criminals are using AI to make scams more believable and harder to detect, so financial institutions should use AI to strengthen their defenses as well. AI-powered fraud monitoring can identify unusual transaction patterns, behavioral changes, and emerging fraud trends that may not be recognized through traditional rules alone.

Combined with experienced investigators and well-trained frontline employees, these solutions help institutions focus on the highest risk activity, intervene sooner, and protect customers before a suspicious transaction becomes a devastating loss. AI is not replacing human judgment. It is giving financial institutions another tool to stay one step ahead of increasingly sophisticated fraudsters.

The future of fraud is AI-enabled

Artificial intelligence is not creating entirely new fraud schemes. Instead, it is making existing elder financial abuse scams more believable, scalable, and harder to detect.

For financial institutions, success will depend on recognizing that fraud prevention is no longer solely about monitoring transactions. It also requires understanding customer behavior, identifying emerging AI-driven tactics, and intervening before a transaction becomes a loss.

As fraudsters continue to adopt new technologies, financial institutions must evolve just as quickly. When AI is used to manufacture trust, vigilance becomes more important than ever.

 

Learn more about current fraud trends with our 2026 Abrigo Fraud Survey results.

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FAQs

What is elder fraud?

Elder fraud is an act targeting older adults that attempts to deceive with promises of goods, services, or financial benefits that do not exist, were never intended to be provided, or were misrepresented. 

How is AI affecting elder fraud?

Artificial intelligence has amplified the effectiveness of these scams in several ways:

  • Personalized fraud campaigns can be created almost instantly
  • Scam communications contain fewer grammatical mistakes and obvious warning signs
  • Criminals can rapidly adapt their tactics based on victim responses
  • Emotional manipulation becomes more convincing and more scalable
How is AI being used in Romance scams?

Today’s criminals can generate realistic photos, create believable online personas, and maintain sophisticated conversations over extended periods. These tools allow fraudsters to build trust faster and with greater credibility.

Why does early intervention matter?

The customer who is sending funds to a fraudulent investment platform may be convinced they are building wealth. The customer responding to a cloned voice emergency may be certain they are helping a family member. That is why early intervention is critical.

Why ongoing sanctions monitoring matters

Most financial institutions understand the importance of screening new customers against sanctions lists during onboarding. It is a foundational part of a strong compliance program and an important step in preventing prohibited relationships.

But what happens after the account is opened? That question deserves more attention. While sanctions screening often begins during onboarding, it should not end there. Customers change, sanctions lists change, and risk changes. Institutions that rely solely on one-time screening may not discover an issue until it has already created operational challenges, required extensive investigation, or raised concerns during an examination.

An effective sanctions screening program recognizes that compliance is an ongoing process, not a single event.

This article covers these key topics: 

Evolving risks

A customer who presents little risk today may look very different a year from now. A business customer may add new beneficial owners. An existing customer may begin sending international wires for the first time. A company may expand into new markets or establish relationships with foreign entities. At the same time, the Office of Foreign Assets Control (OFAC) continues to update sanctions lists in response to geopolitical developments.

None of these developments are unusual. They are part of doing business. The challenge is ensuring your sanctions screening program can identify changes that may require additional review.

That does not mean every customer should be screened every day. It does mean institutions should have a well-documented risk-based strategy for determining when additional screening makes sense and be prepared to explain that strategy to examiners.

 

Four events that should prompt a second look

Every institution’s risk profile is different, but there are several situations in which additional sanctions screening warrants consideration.

Updates to sanctions lists. New sanctions designations can affect existing customer relationships. Institutions should consider how updates to sanctions lists are incorporated into their screening processes and whether existing customers are reviewed when appropriate.

Changes in beneficial ownership. Ownership changes can introduce new risks, particularly for commercial customers. Rescreening beneficial owners helps ensure institutions have an accurate understanding of who they are doing business with.

Significant changes in customer information. Changes to legal names, business structures, addresses, or other identifying information may warrant additional screening depending on the institution’s risk profile.

New or higher risk activity. A customer who begins conducting international transactions or engaging in higher-risk business activity may require additional review, even if the original onboarding presented no concerns.

These events do not automatically indicate suspicious activity. They provide opportunities to reassess risk using current information.

Staying on top of fraud is a full-time job. Let our Advisory Services team help when you need it.

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The cost of reactive screening

When institutions discover potential sanctions issues months or years after onboarding, the investigation is rarely simple.

Compliance teams must review customer records, analyze transaction activity, gather supporting documentation, and determine whether the alert represents a true match or a false positive. Operations teams may delay transactions while the review is completed. Relationship managers answer customer questions about unexpected delays.

Even when the alert is ultimately cleared, the institution has already invested valuable time and resources. Reactive screening often creates work that could have been reduced or avoided through earlier identification of changing risks.

False positives

False positives are an unavoidable part of sanctions screening, but excessive false positives can become an operational risk in their own right.

When investigators spend a large portion of their day clearing low-value alerts, they have less time to focus on higher-risk activities. Alert fatigue can also make it more difficult to identify truly significant matches.

That is why institutions should periodically evaluate not only the number of alerts they receive, but also the quality of those alerts.

Questions worth asking include:

  • Are investigators spending too much time reviewing obvious false positives?
  • Have customer demographics or business lines changed in ways that affect screening performance?
  • Is the institution regularly reviewing screening thresholds and matching logic?
  • Does the current process support efficient investigations while maintaining appropriate risk controls?
  • If sanction screening software is in use, how can the settings be modified to reduce false positives?

Reducing unnecessary alerts is not about lowering compliance standards. It is about helping compliance professionals spend their time where it provides the greatest value.

 

Compliance reducing friction

Strong sanctions screening protects the institution, but it should also support efficient operations. When screening processes are well designed, compliance teams spend less time resolving preventable issues. Customers experience fewer unnecessary delays. Business lines have greater confidence in the consistency and documentation of reviews.

That is why proactive sanctions screening is more than a regulatory expectation. It is a practical way to improve operational efficiency while maintaining effective risk management.

 

Building a stronger sanctions program

Sanctions compliance is becoming more complex as customer relationships, payment activity, and global events continue to evolve. Financial institutions that view sanctions screening as a one-time onboarding requirement may struggle to keep pace with those changes.

Instead, institutions should periodically evaluate whether their screening strategy reflects how risk develops over the life of a customer relationship. Efficient sanctions screening software can support that effort by helping institutions automate ongoing screening, reduce unnecessary false positives, and give compliance teams more time to focus on higher-risk activity. Many sanctions screening solutions also allow institutions to add supplemental watchlists, giving them the flexibility to align screening with their specific risk profile rather than limiting reviews to only what is required.

The most effective sanctions programs do more than identify potential matches. They establish thoughtful processes for determining when additional screening is appropriate, reduce unnecessary investigative work, and help compliance teams focus on the risks that matter most.

That approach strengthens compliance, supports operational efficiency, and ultimately helps build confidence among regulators, employees, and customers alike.

 

Find out how to automate sanctions screening to reduce false positives.

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This article covers these key topics: 

Improve fraud detection without upping headcount

For many credit unions, new payment rails, rising digital adoption, and evolving fraud schemes are stretching already lean teams. But regulators continue to expect strong controls, timely investigations, and well-documented processes, which means credit unions must find new ways to improve fraud detection without adding headcount.

The good news is that fraud teams can make meaningful gains by refining workflows, using AI thoughtfully, and aligning staff time with the highest-value activities.

Why efficiency matters more than ever

Fraud teams are under strain across the industry. Staffing challenges, turnover, and expanding responsibilities in BSA/AML and fraud functions make it difficult to keep pace with alert volumes and investigations.

At the same time, fraud risks are accelerating. Real-time payment systems like FedNow operate 24/7, requiring institutions to detect and stop fraud in seconds—not hours. This puts pressure on teams to respond faster without sacrificing accuracy.

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Start with smarter fraud workflows

Inefficient processes are often the biggest barrier to performance. Many credit unions still rely on manual steps, disconnected systems, or inconsistent case management practices.

Well-designed fraud workflows can help teams:

  • Reduce time spent on low-risk alerts
  • Standardize investigations
  • Improve documentation and audit readiness
  • Prioritize the highest-risk activity first

Streamlining fraud workflows ensures that analysts spend less time navigating systems and more time making informed decisions. This directly supports a stronger fraud detection rate while reducing burnout.

Align team structure with risk

The way your credit union distributes its AML and fraud workload may be impacting the speed and accuracy of its program. Often, credit unions can improve performance by:

  • Delegating routine reviews to junior staff
  • Reserving experienced investigators for complex cases
  • Cross-training team members to handle multiple fraud types
  • Establishing clear escalation paths

Cross-training is especially important in lean environments. It reduces dependency on any single individual and helps maintain continuity during turnover or absences, both of which are a growing concern across financial institutions. When teams are aligned to risk and complexity, they can move faster without compromising quality.

Leverage technology to scale without adding staff

Technology is one of the few levers that allows credit unions to grow capacity without growing headcount. Centralized platforms that bring together alerts, case management, and analytics help eliminate swivel-chair work and create consistency across the team.

As seen across community financial institutions, the right fraud tools can free up staff from manual tasks and enable growth without additional hires.

In fraud operations, this includes:

  • Automated alert triage
  • Integrated case management
  • Cross-channel transaction monitoring
  • Real-time detection capabilities

These capabilities are especially important as fraud becomes more complex and faster-moving. Credit unions need systems that can keep pace.

Use AI to prioritize, not replace

AI adoption is top of mind for many institutions, but success depends on how it’s applied. For credit unions with lean teams, AI is most effective when it enhances human decision-making rather than attempting to replace it. For example, AI-driven fraud detection tools can:

  • Score and prioritize alerts based on risk
  • Identify patterns across channels and transactions
  • Surface anomalies that may be missed manually
  • Reduce false positives

This allows investigators to focus their time where it matters most. Instead of reviewing every alert equally, teams can concentrate on the cases most likely to impact the institution, resulting in fewer fraud losses.

 

Focus on what moves the needle

Not all activities contribute equally to outcomes. High-performing teams regularly evaluate where time is spent and adjust accordingly.

Areas that often deliver the biggest impact include:

  • Tuning detection scenarios to reduce noise
  • Reviewing alert thresholds and rules regularly
  • Monitoring performance metrics like false positives and case resolution time
  • Continuously refining fraud workflows

Building a sustainable path forward

Lean teams are the norm at many community financial institutions, and operating with a smaller team does not mean accepting lower performance. By investing in better fraud workflows, using AI strategically, and aligning team efforts with risk, institutions can improve outcomes in a sustainable way. With the right approach, credit unions can do more with the team they have and continue protecting both their members and their mission.

Smarter tech, safer future. Reduce loss and protect customers with Abrigo Fraud Detection.

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What is an EFA loan? Benefits for commercial lending

An Equipment Finance Agreement (EFA) helps businesses acquire essential equipment while preserving cash flow, making it an attractive financing option for commercial borrowers. Learn how EFA lending supports business growth, expands commercial and industrial (C&I) lending opportunities, and helps financial institutions strengthen long-term customer relationships.

What is an Equipment Finance Agreement loan?

Commercial and small business borrowers are continually looking for financing options that allow them to invest in critical equipment while preserving working capital. Whether expanding operations, replacing aging machinery, or adopting new technology, businesses often need financing solutions that align with their cash flow rather than requiring significant upfront expenditures.

For financial institutions, an EFA loan provides an opportunity to meet those needs while expanding lending relationships. Equipment financing can help lenders support business growth, deepen customer relationships, and diversify their commercial and industrial (C&I) portfolios with assets that directly contribute to borrowers' operations.

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An Equipment Finance Agreement (EFA) is a financing structure that enables businesses to acquire essential equipment while repaying the obligation through scheduled installment payments over an agreed-upon term. Rather than making a large capital purchase upfront, borrowers can preserve liquidity while putting revenue-generating equipment to work immediately.

Equipment financed through EFAs commonly includes:

  • Commercial vehicles and transportation equipment
  • Manufacturing and production machinery
  • Medical and healthcare equipment
  • Technology infrastructure and office systems

Because the equipment itself typically serves as collateral, EFAs offer a practical financing solution for many commercial borrowers while helping lenders manage credit risk appropriately.

How EFA loans help financial institutions and their C&I customers

Commercial equipment financing plays an important role in helping financial institutions grow their C&I portfolios. Businesses across nearly every industry depend on equipment investments to remain productive and competitive, creating ongoing financing opportunities throughout the customer lifecycle.

Supporting equipment purchases also allows lenders to finance assets that contribute directly to a borrower's operations and revenue generation. As businesses expand, those financing relationships can naturally lead to additional opportunities, including treasury management services, deposit relationships, lines of credit, and future commercial loans.

Relationship banking remains one of the greatest competitive advantages for community financial institutions. By offering flexible financing solutions that address evolving business needs, lenders can strengthen long-term customer relationships while positioning themselves as trusted financial partners. Technology that reduces administrative work also enables lenders to spend more time developing those customer relationships and pursuing new business opportunities.

Operational considerations for managing an EFA loan

Like many commercial lending products, equipment finance introduces operational requirements beyond the initial approval process. Financial institutions must manage documentation, monitor collateral throughout the life of the agreement, and maintain servicing processes that support both regulatory expectations and customer service goals.

Manual processes can make these responsibilities more time-consuming, particularly as equipment finance portfolios grow. Streamlined workflows and integrated technology can improve visibility across the portfolio, simplify collateral tracking, and reduce administrative burden for lending teams.

When documentation and servicing activities are managed efficiently, lenders gain greater confidence in portfolio oversight while creating a better experience for both staff and commercial borrowers.

Supporting portfolio growth through equipment finance

As demand for flexible business financing grows, institutions that can strengthen commercial relationships while supporting portfolio diversification will be better positioned to serve commercial borrowers and deliver a consistent lending experience.

This blog was written with the assistance of ChatGPT, a large language model. It was reviewed by Abrigo subject matter experts.

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FAQs

What is an EFA loan?

An Equipment Finance A loan is a financing agreement that allows businesses to purchase equipment through scheduled payments while preserving working capital.

What types of equipment can be financed with an Equipment Finance Agreement?

Businesses commonly use Equipment Finance Agreements to finance vehicles, manufacturing equipment, medical devices, construction equipment, and technology systems.

How does an EFA loan differ from a traditional business loan?

An EFA loan is specifically designed for equipment purchases, with the financed equipment typically serving as collateral for the agreement.

How can financial institutions manage equipment finance portfolios more efficiently?

Modern lending technology can streamline documentation, collateral tracking, servicing workflows, and portfolio visibility throughout the life of each financing agreement.

This article covers these key topics: 

What banks and credit unions need to know when buying a new LOS  

Replacing your loan origination system? Learn five planning steps to improve workflows, prepare data and integrations, support adoption, and reduce implementation risk.

Planning eases LOS transitions

If your financial institution is replacing your loan origination system (LOS), understanding the planning process before implementation begins can make the transition more manageable. After all, for many financial institutions, loan origination system implementation is unfamiliar territory. The current system may have been in place for years, with workflows, workarounds, and reporting habits built around it over time.

As a result, leaders who begin exploring a legacy LOS replacement usually have the same questions: What will the process look like? How difficult is it? What has to happen before implementation starts? How do we make sure the new system improves lending operations in a meaningful way?

You might also like this webinar, “Agentic LOS: The next evolution of loan origination”

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“The biggest obstacle for implementing new technology is often getting people to embrace, adopt, and use it,” says Bailey Barretto, Abrigo Director of Advisory Services, and a change management expert. Sharing information about what’s involved in replacing an LOS will create better buy-in from everyone the system touches: lending, credit, operations, compliance, and even the borrower.

The timeline for replacing a loan origination system varies by institution size, workflow complexity, data quality, integrations, and internal decision-making capacity. But replacing loan origination software usually requires five planning priorities: defining the outcomes the institution wants to improve, mapping current lending workflows, preparing data and integrations, understanding implementation steps, and planning for user adoption.

The institutions that prepare thoughtfully usually find the process manageable and the results worth the effort. And an experienced LOS software partner should be able to help you in each area as you plan.

1. Start with the outcomes you want to improve

For many banks and credit unions, replacing loan origination software is less about swapping systems and more about improving how lending work moves across teams. For best results on a loan origination system replacement, then, begin with a business question rather than a software feature list.

Define what the institution is trying to improve before evaluating loan origination systems or specific vendors. Why is the bank or credit union considering changing the LOS? Lending teams may be dealing with slow approvals, duplicate data entry, inconsistent credit memos, limited visibility into pipeline activity, or too much dependence on spreadsheets and email. Leadership may be looking for stronger reporting, cleaner audit trails, better borrower service, or a more consistent lending experience that promotes growth.

Useful questions include:

  • Where does the current process slow down?
  • Which tasks take the most staff time?
  • Which manual steps create the most frustration?
  • What reporting or compliance gaps are the most visible?
  • What would better look like from a lender’s perspective, a credit officer’s perspective, and a borrower’s perspective?

From there, define a small set of measurable goals. Examples might include shorter approval times, fewer manual touchpoints, more consistent documentation, or better visibility into loan pipelines. Those goals give the implementation team a clear target and help keep the project focused on results.

2. Identify how lending really works today

Over time, lending workflows tend to accumulate exceptions. A team creates a spreadsheet to bridge a gap. A credit memo gets prepared outside the system. A document is routed by email because it is faster in the moment. Those workarounds solve immediate problems, but they also make the overall process harder to manage. Replacing an LOS creates a useful opportunity to examine how the institution actually processes loans—from the first conversation through approval and closing. This examination can help with implementation planning and optimization of team efforts.

Before implementing modern loan origination technology, map the current-state workflow in detail.

Questions worth asking include:

  • Where do deals leave the LOS?
  • Where is information entered twice?
  • How are credit memos prepared?
  • Which approvals create bottlenecks?
  • Which steps depend on email, spreadsheets, or shared drives?
  • Where do lenders spend time that adds little value for borrowers or staff?
  • These questions may reveal process improvements that belong in the new system design. The goal is to understand where the institution is starting so the new LOS can support a simpler, more consistent future state.

3. Prepare the data and the integrations early

Data migration is one of the most important parts of a loan origination system transition.

Start by deciding what needs to move and what can remain archived. Active loans usually need to migrate. Historical records may need to stay available for audit, servicing, or research purposes. Loan pipeline data may need special handling so users do not lose visibility during the transition.

Useful questions include:

  • Which records should move into the new LOS?
  • Which historical files should remain archived?
  • How will staff access older loan records when needed?
  • What cleanup is required before migration?
  • How will open deals be handled during the transition?

Integrations deserve the same early attention. Most lending teams rely on several systems besides the LOS.

Common integrations include:

  • Core processing systems
  • Document preparation software
  • eSignature tools
  • Loan boarding software
  • Imaging or document management platforms

The implementation team should understand those connections early. That helps prevent duplicate work and supports workflows that feel natural to the people using them every day.

4. Understand what’s involved with LOS implementation

For many institutions, implementation is the part of the project that feels least familiar. That makes clarity especially valuable.

A good implementation usually includes a series of steps that build on one another:

  • Discovery and requirements gathering
  • Workflow design
  • Configuration
  • Data migration planning and testing
  • Integration setup and validation
  • User acceptance testing
  • Training and role-based preparation
  • Go-live support
  • Post-launch optimization

Each step is vital to implementation. Discovery helps the team understand what the institution needs. Workflow design translates that understanding into a practical structure. Testing exposes issues before they affect users. Training helps staff feel comfortable when the new system goes live.

This is where the right partner makes a difference. Abrigo’s team includes project managers who have helped hundreds of institutions implement an LOS, and the advisory team includes certified change management consultants and project managers who focus on helping institutions build buy-in, prepare teams, and improve adoption over time. That kind of support acknowledges that implementation planning is a business effort as much as a technology effort.

Leaders should also involve vendor management, information security, compliance, and other risk stakeholders early enough to support the institution’s third-party risk management expectations without slowing the project later.

A few practical questions can help leaders prepare:

  • Who will own the project internally?
  • Which teams should participate in design and testing?
  • How much time will frontline users need before go-live?
  • What decisions need to be made early to avoid rework later?
  • What does success look like at 30, 60, and 90 days after launch?

Institutions that answer those questions early usually have a smoother experience implementing new software for originating loans.

5. Plan for adoption before go-live

A new LOS creates value when people use it well.

That means adoption should begin before launch and continue afterward. Training helps, of course, as does communication, sponsorship, and ongoing support. Leaders who involve lenders, credit staff, operations teams, and compliance early often see stronger results because the system reflects how the institution actually works.

A thoughtful adoption plan often includes:

  • Role-based training for different user groups
  • Internal champions who can answer questions
  • Clear expectations from leadership
  • Measurable adoption metrics after launch
  • Ongoing education as workflows and products evolve

The most helpful software providers will focus on increasing software adoption, boosting ROI, and helping institutions realize benefit from their technology investments. Whether through change management services, peer-to-peer community-like forums where financial institutions share best practices, responsive customer service, or ongoing live and on-demand training, a helpful software partner continues to support the replacement after go-live. The institution learns, adjusts, and improves as users become more comfortable with the new system.

How can leaders measure progress in practical terms? Here are some examples of questions to consider:

  • What percentage of loans are being processed in the LOS?
  • Are lenders using the system consistently?
  • Have manual workarounds declined?
  • Are approvals moving faster?
  • Is documentation more consistent?
  • Are users finding the system easier to work in?

Identifying practical key performance indicators helps leaders see whether the transition is delivering the expected value.

Checklist before replacing your loan origination system

As described above, understanding what happens during an LOS replacement can prepare financial institutions to get the full value out of the effort. Answering the following straightforward questions will make the process smoother:

  • Have we defined the business outcomes we want to improve?
  • Do we understand the current lending process well enough to redesign it?
  • Have we identified the data that needs to migrate?
  • Do we know which systems must integrate with the new LOS?
  • Have we planned for implementation roles, testing, and training?
  • Have we mapped out how adoption will be supported after go-live?

If the answer to any of those questions is uncertain, the institution has found a useful planning area.

Replacing an LOS is a process improvement opportunity

A loan origination system replacement is a significant investment of time, resources, and leadership attention. It also creates a chance to improve the way lending happens across the institution.

Every loan origination system implementation follows its own timeline, but the most successful projects share some commonalities. They begin with clear goals, a realistic view of current processes, early attention to data and integrations, a practical implementation plan, and a thoughtful approach to adoption. That combination helps the institution move through the transition with greater confidence.

Replacing an LOS is unfamiliar territory for most financial institutions. With experienced partners and thoughtful planning, institutions will understand what to expect, avoid common missteps, and keep the project aligned with the outcomes that matter most. Replacing your loan origination system can be manageable, and it can be a move that strengthens lending operations for years to come.

This blog was written with the assistance of ChatGPT, a large language model. It was reviewed by Abrigo subject matter experts.

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FAQs

What should a financial institution do before replacing a loan origination system?

A financial institution should define the business outcomes it wants to improve before replacing a loan origination system. Clear goals, such as faster approvals, fewer manual touchpoints, better reporting, or more consistent documentation, help keep the LOS implementation focused on measurable lending improvements.

Why is workflow mapping important before implementing a new LOS?

Workflow mapping is important because it shows how lending actually works today, and it can identify bottlenecks, duplicate data entry, workarounds, and steps handled outside the current LOS. Understanding the current process helps the institution implement a new loan origination system that supports simpler, more consistent lending operations.

What data and integrations should be reviewed before an LOS replacement?

Before an LOS replacement, institutions should identify which active loans, historical records, pipeline data, and archived files need to move or remain accessible. They should also review integrations with core systems, document preparation tools, eSignature platforms, loan boarding software, and imaging or document management systems.

How can banks and credit unions improve adoption of a new loan origination system?

Banks and credit unions can improve LOS adoption by planning for training, communication, leadership support, and user involvement before go-live. Role-based training, internal champions, adoption metrics, and ongoing support help lenders, credit teams, operations, and compliance staff use the new system consistently.

What is friendly fraud?   

For years, financial institutions have focused fraud prevention efforts on external threats such as stolen credentials, account takeovers, and payment scams. While those risks remain significant, another form of fraud is gaining momentum across the payments ecosystem: friendly fraud.

Also known as first-party fraud or chargeback fraud, friendly fraud occurs when a consumer disputes a legitimate transaction with their card issuer, often after receiving the goods or services. In some cases, the dispute may stem from confusion or a forgotten purchase. In others, the cardholder knowingly misrepresents the transaction to obtain a refund while retaining the product or service.

As digital commerce continues to expand, financial institutions are increasingly finding themselves at the center of this growing challenge.

A growing risk

Friendly fraud affects far more than just merchants, particularly in terms of chargeback volume. A chargeback occurs when a cardholder disputes a transaction with their card issuer, potentially resulting in funds being returned to the customer. Every chargeback requires financial institutions to investigate, review, and resolve the dispute, creating operational costs and increasing pressure on fraud and dispute management teams.

According to Mastercard’s 2025 State of Chargebacks Report, approximately 23 percent of all chargebacks are tied to first-party fraud. As dispute volumes continue to rise, financial institutions must balance their responsibility to protect consumers with the need to safeguard the integrity of the payments system.

This balance is becoming increasingly difficult as fraudsters learn to exploit consumer protection mechanisms designed to address legitimate unauthorized transactions.

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Why first-party fraud is different

Traditional fraud typically involves a criminal actor using stolen payment credentials or accessing an account without authorization. Friendly fraud is more complex because the transaction itself is often legitimate. The cardholder made the purchase. The product was delivered. The service was provided.

What makes first-party fraud challenging is that financial institutions often have limited visibility into events that occur after a transaction is authorized. Determining whether a dispute stems from confusion, buyer’s remorse, family misuse of a card, or deliberate fraud often requires careful analysis and collaboration across multiple parties.

This complexity creates both operational and reputational risks for financial institutions.

 

Balancing consumer protection and abuse

Consumer protections remain one of the most important safeguards in the payments ecosystem. Cardholders need confidence that unauthorized transactions can be resolved quickly and fairly.

However, institutions also face growing pressure to identify situations where those protections may be misused.

The challenge is not simply detecting fraud. It is distinguishing between legitimate disputes and cases where consumers knowingly abuse the chargeback process. Making that distinction requires more than transaction-level review. It increasingly demands a holistic understanding of customer behavior, dispute patterns, and emerging fraud trends.

As first-party fraud evolves, institutions may need to expand their use of behavioral analytics, risk scoring, and historical dispute analysis to identify potentially abusive activity.

 

Data and analytics play a critical role

Financial institutions have long relied on analytics to identify suspicious transactions before losses occur. The same approach can help address first-party fraud.

Patterns such as repeated disputes, frequent claims involving delivered merchandise, or unusual chargeback behavior may indicate elevated risk. While no single data point proves fraud, combining transaction data with customer history can help institutions make more informed decisions during the dispute process.

Advanced monitoring capabilities also enable institutions to identify emerging trends earlier, allowing fraud teams to adapt controls as customer behavior and fraud tactics evolve.

 

Education as part of the solution

Many friendly fraud cases begin with misunderstandings rather than malicious intent. Consumers may not recognize a merchant name on their statement, forget about a recurring subscription, or fail to realize a family member made a purchase using a shared payment method. In these situations, proactive customer education can help reduce unnecessary disputes before they occur.

Clear communication about transaction descriptions, recurring payment disclosures, and dispute processes can improve customer understanding while reducing operational burdens for institutions and merchants alike.

The next phase of fraud risk

As payment volumes continue to grow and commerce becomes increasingly digital, first-party fraud is likely to remain a significant challenge across the financial services industry.

For financial institutions, the issue extends beyond chargeback management. It represents a broader risk management challenge that affects operational efficiency, customer relationships, and the overall integrity of the payments ecosystem.

Organizations that invest in data-driven fraud detection, strengthen dispute management processes, and leverage behavioral analytics will be better positioned to navigate this evolving threat. The goal is not to limit consumer protections. It is to ensure those protections remain effective while reducing opportunities for abuse.

Friendly fraud may begin with a disputed transaction, but its implications reach far beyond a single chargeback. For financial institutions, understanding and addressing first-party fraud will be an increasingly important component of modern fraud risk management.

 

Learn more about current fraud trends with our 2026 Abrigo Fraud Survey results.

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FAQs

What is friendly fraud?

Friendly fraud is a type of first-party fraud where a consumer disputes a legitimate transaction with their card issuer after receiving goods or services. Abrigo Fraud Detection supports financial institutions with fraud detection software that helps identify patterns tied to disputed transactions, chargeback abuse, and emerging fraud risk.

What is chargeback fraud?

Chargeback fraud is a form of first-party fraud where a cardholder disputes a legitimate transaction to receive a refund after goods or services were provided. Abrigo Fraud Detection supports financial institutions with fraud detection software that helps identify dispute patterns, repeated claims, and unusual chargeback behavior tied to potential abuse.

What is a friendly fraud chargeback?

A friendly fraud chargeback is a disputed transaction in which the cardholder may have made the purchase but later claims the transaction was unauthorized or unsatisfactory. Abrigo Fraud Detection helps banks and credit unions evaluate friendly fraud chargebacks using customer history, transaction data, behavioral analytics, and historical dispute analysis.

How can financial institutions prevent friendly fraud?

Financial institutions can help prevent friendly fraud by combining customer education, clearer transaction communication, stronger dispute workflows, and data-driven fraud detection. Abrigo Fraud Detection supports this approach with fraud detection software for banks and credit unions that helps identify emerging trends, repeated disputes, and potentially abusive chargeback behavior.

How can banks and credit unions detect friendly fraud?

Banks and credit unions can detect friendly fraud by reviewing repeated disputes, delivered-merchandise claims, unusual chargeback behavior, customer history, and broader behavioral patterns. Abrigo Fraud Detection provides fraud detection software for banks and credit unions that supports data-driven monitoring and trend identification.

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Lessons for credit unions from minority depository institutions

Minority depository institutions help expand access to financial services and capital in communities that have historically faced barriers to financing. By supporting local businesses, entrepreneurs, and consumers, these institutions contribute to economic growth while helping strengthen the financial well-being of the communities they serve.

Balancing sound risk management and lending growth

June's designation as MDI Awareness Month by the National Credit Union Administration offers an opportunity to recognize the important role these institutions play in expanding access to capital. It also highlights a broader challenge shared by many community-focused credit unions: how to responsibly grow small business lending while maintaining sound risk management practices.

For many credit unions, small business lending remains a significant opportunity to deepen member relationships, strengthen local economies, and diversify loan portfolios. The mission-driven approach often associated with minority depository institutions is one familiar to credit unions heavily invested in their communities. It underscores the importance of understanding borrowers beyond traditional credit metrics and finding responsible ways to support entrepreneurs who may face barriers to financing.

As credit unions seek to serve more small business members, visibility into borrower performance and portfolio risk can help institutions make informed lending decisions while maintaining sound credit practices.

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Traditional underwriting does not always tell the full story

Small business owners often present unique underwriting challenges. Some businesses have limited credit histories, inconsistent revenue patterns, seasonal cash flow fluctuations, or relatively short operating histories. As a result, traditional underwriting methods may not always provide a complete picture of a borrower's financial health or future potential.

This challenge is particularly relevant for credit unions serving diverse and underserved communities, where entrepreneurs may have strong local relationships and business prospects but less conventional financial profiles.

Relationship banking continues to play a critical role in these situations. Local market knowledge, ongoing member engagement, and a broader understanding of a borrower's business operations can provide valuable context alongside traditional financial analysis.

Better visibility supports better lending decisions

Supporting small business lending growth requires a clear understanding of both individual borrowers and overall portfolio performance. Credit unions looking to expand in this area often face a common challenge: balancing growth goals with operational capacity.

As institutions work to expand access to capital responsibly, technology can help provide the visibility and consistency needed to support sound lending decisions. Solutions that centralize borrower information, streamline document collection, and create a more consistent lending process can help institutions serve more businesses without losing the relationship-focused approach that defines community lending.

Access to cash flow trends, borrower financial performance, and portfolio analytics can help lenders identify strengths, emerging risks, and opportunities that may not be immediately visible through manual processes alone. Better visibility allows credit unions to evaluate borrowers more consistently while improving the efficiency of loan review and approval workflows.

Purpose-built loan origination software can also help institutions streamline routine tasks. Rather than spending valuable time rekeying data or tracking documents across multiple systems, lenders can focus on understanding member needs and creating new offerings to meet them. Community-focused financial institutions have long differentiated themselves through personal service and local expertise, and efficient lending workflows help preserve those advantages while supporting growth.

For credit unions serving diverse or underserved markets, data-driven insights can help ensure lending decisions remain both prudent and responsive to member needs.

Supporting communities through streamlined lending

Expanding lending opportunities does not mean lowering credit standards. Strong credit policies, thorough documentation, and ongoing portfolio monitoring remain essential components of a sound lending program. Credit unions must continue to effectively monitor concentrations and maintain consistent underwriting standards across all lending activities.

At the same time, institutions can use data and analytics to improve decision-making and identify potential concerns earlier. Better borrower visibility and portfolio monitoring support a proactive approach to risk management, helping lenders respond to changing credit conditions while maintaining confidence in portfolio performance.

Combining relationship banking with data-driven decision-making

MDI Awareness Month serves as a reminder that expanding access to capital and supporting local economic growth requires both strong community relationships and sound lending processes. Credit unions that combine the two may be better positioned to identify opportunities that traditional processes could overlook. By combining local expertise with better data visibility and efficient workflows, credit unions can strengthen member relationships, improve lending consistency, and support long-term portfolio health.

 
This blog was written with the assistance of ChatGPT, a large language model. It was reviewed by Abrigo subject matter experts.

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