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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.

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.

You might also like this webinar, "Thinking like a local economist: Sharpening lending decisions with market insight."

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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Make the best use of your institution's data

Financial institutions have more data than ever before. Loan pipelines, deposit portfolios, concentration reports, CECL analyses, customer relationships, and operational metrics generate a constant flow of information. Yet many institutions struggle to transform that data into meaningful decisions.

Too often, valuable information remains trapped in disconnected systems, spreadsheets, and static reports. Teams spend hours gathering data, reconciling reports, and answering follow-up questions instead of focusing on strategy and execution.

As financial institutions continue to explore artificial intelligence and advanced analytics, the goal is no longer simply to collect data. The goal is to generate actionable data insights that help institutions manage risk, identify opportunities, and serve customers more effectively.

Key topics covered in this post:

Why data alone is not enough
Reducing the friction between questions and answers
The importance of connected data
Creating a culture of informed decision-making

Why data alone is not enough

For years, bankers have relied on reports to understand performance. A chief credit officer might pull pipeline reports from one system, concentration data from another, and portfolio metrics from a third. Deposit teams may rely on separate reports to monitor account growth, retention, and funding trends. The process often works, but it comes at a cost.

When new questions arise, staff frequently must return to the source systems, export additional data, and create new reports. By the time the answer is available, the opportunity to act may have already passed. But when data is organized effectively and paired with purpose-built technology, it becomes possible to uncover actionable data insights that reveal why something happened and what should happen next.

Emerging tools such as AskAbrigo, an AI-powered banking agent, and Abrigo Connect, a banking intelligence solution, can help institutions by surfacing relevant data, internal policies, economic support, and prior analyses to support more consistent and defensible decisions.

Adopt AI with confidence and control using purpose-built tools.

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Reducing the friction between questions and answers

One of the biggest obstacles to effective decision-making is what some banking leaders describe as "curiosity friction." Curiosity friction occurs when obtaining answers requires so much effort that people stop asking questions. A lender reviewing pipeline activity may want to know:

Which officers are driving production?
Which industries represent growing concentrations?
How are projected closings tracking against strategic goals?
What funding requirements may emerge in the coming months?

Similarly, deposit teams may want to understand:

Which customers are most likely to leave?
What demographic segments are growing?
How many customers have deposit relationships but no loan relationships?
What opportunities exist to deepen existing relationships?

When answering these questions requires multiple reports and manual analysis, curiosity naturally declines. When answers become easier to access, organizations can generate actionable data insights more consistently and make faster decisions.

The importance of connected data

Financial institutions often maintain valuable information across multiple systems. Core systems, lending platforms, deposit systems, risk monitoring tools, internal policy documents, and external data sources each provide part of the story, and value emerges when those data sources are connected.

Consider a scenario in which a financial institution wants to understand its exposure to federal employees during a government shutdown. Answering that question may require connecting payroll deposits, customer relationships, and loan portfolios.

The combination of modern data visualization and artificial intelligence offers the institution a way to view this often disparate data in one place. The institution may identify customers who could benefit from payment accommodations, overdraft protection, or other services designed to support them during a period of uncertainty.

Additionally, modern analytics environments allow users to interact directly with data, explore trends, and investigate exceptions without requiring extensive report development. A credit team reviewing concentration data may want to drill deeper into specific industries, geographies, or borrower segments. A deposit team may want to examine account runoff by demographic group or customer tenure. CECL practitioners may want to analyze historical trends supporting qualitative factor adjustments. Instead of creating a new report each time, users can interact with the information and continue asking follow-up questions.

Creating a culture of informed decision-making

Adopting technology that increases visibility across the organization and reduces manual processes that slow down research goes a long way toward creating a culture that encourages exploration, collaboration, and continuous learning. When lending, finance, deposit, and risk teams can easily share information, they gain a more complete understanding of the institution’s performance and opportunities. Data becomes more valuable when it is viewed across departments rather than within individual silos.

A connected approach allows institutions to identify trends earlier, evaluate potential risks more effectively, and discover growth opportunities that might otherwise remain hidden. Most importantly, teams spend less time creating reports and more time using actionable data insights to improve outcomes.

Start with the foundation

Many institutions assume they must build a massive data strategy before realizing any benefits. In reality, progress often begins with a single use case. The most successful organizations start by organizing their data, connecting key systems, and solving a specific business problem. From there, they continue to expand their capabilities and ask deeper questions. The objective is to create a foundation that supports better decisions over time.

As artificial intelligence and analytics capabilities continue to evolve, financial institutions do not need more reports. They need better ways to understand the information they already have. The future belongs to organizations that can leverage actionable data insights to drive results.

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FAQs

Why do many financial institutions struggle to turn data into decisions?

Many institutions store information across multiple disconnected systems, including core processing platforms, lending systems, deposit systems, and spreadsheets. As a result, staff often spend significant time gathering and reconciling data before they can analyze it. This creates delays and makes it difficult to answer new questions quickly enough to support timely decision-making.

How can AI help financial institutions use their data more effectively?

Purpose-built AI can help institutions analyze large amounts of structured data, identify trends, answer complex questions, and uncover relationships across different data sources. By reducing the manual effort required to create reports and perform analysis, AI can help bankers spend more time evaluating opportunities and managing risk.

What types of data should financial institutions connect for better analysis?

Financial institutions can benefit from connecting lending, deposit, customer relationship, transaction, and risk management data. Combining these data sources creates a more complete view of customers and portfolio performance, helping institutions identify concentrations, retention risks, growth opportunities, and emerging credit concerns.

Do financial institutions need a large-scale data strategy before getting started?

No. Many successful institutions begin with a single use case, such as improving loan pipeline visibility, monitoring deposit retention, or enhancing portfolio risk analysis. Starting with a focused objective allows institutions to build a strong data foundation, demonstrate value, and expand their capabilities over time.

CFPB small business data collection under 2026 rule

Read practical tips for banks and credit unions to manage their 1071 rule data collection processes efficiently so they can stay ahead of deadlines and avoid compliance problems.

This article covers these key topics:

The scope of small business lending data collection: Who and what
Deadlines for collecting and reporting 1071 data
Key data points under 1071 small business lending data regulations
Tips for streamlining small business lending data collection

This article was updated to reflect the May 1, 2026, final rule published by the Consumer Financial Protection Bureau. The new rule revamped small business data collection requirements for lenders.

Data collection under CFPB's new 1071 deadlines

The Consumer Financial Protection Bureau’s (CFPB) small business data collection rule, often referred to as the 1071 rule, is set to be the most significant effort of data collection and reporting for financial institutions in nearly 50 years. Banks and credit unions must prepare to meet the rule’s requirements by understanding what data must be collected, when it needs to be collected, and how to streamline the process to ensure compliance.

This article describes the scope of the CFPB small business lending data regulations and offers practical tips for banks and credit unions to manage their data collection processes efficiently. Understanding the CFPB rule issued May 1, 2026, and preparing adequately will help your financial institution stay ahead of deadlines and avoid compliance problems.

Access more small business loan data requirements resources and guidance

1071 resources for lenders

The scope of small business data collection

The CFPB’s small business data collection rule implements Section 1071 of the Dodd-Frank Act, which directs the bureau to collect certain demographic data from small business lenders. The primary goal of the federal rule is to facilitate fair lending enforcement and identify the credit needs of women- and minority-owned businesses.

Which types of credit are considered “small business loans” under 1071?

The rule requires that lenders collect and report data for all small business credit applications from any business with $1 million or less in gross annual revenue in its preceding fiscal year. Credit transactions covered by the rule include applications or requests for:

Term loans (secured or unsecured)
Lines of credit (secured or unsecured)
Credit cards (private-label and not private-label)

Agricultural loans are excluded from the rule’s definition of credit types. In addition to merchant cash advances, loans of $1,000 or less are also excluded from the types of credits lenders must track under 1071. Requests for additional credit tied to an existing loan and transactions that extend, renew, or amend an existing credit do not count as originations when determining whether an institution is covered. However, requests for additional credit amounts on an existing account are considered covered applications that must be tracked.

One important distinction is that financial institutions qualify as covered institutions based on the total covered credit transactions for small businesses, rather than on the covered applications received from them. In the example provided by the CFPB, that means that if in both 2028 and 2029, Financial Institution B received 1,100 covered applications from small businesses and originated 900 covered credit transactions for small businesses, then for 2029, Financial Institution B is not a covered financial institution.

Which lenders must collect 1071 data

Under this rule, covered financial institutions that originated at least 1,000 covered credit transactions for small businesses in each of the two preceding calendar years must collect and report demographic data on applicants for certain small business credit. Lenders that must comply include not only banks, credit unions, and savings associations, but also:

online lenders
platform lenders
community development financial institutions
lenders involved in equipment and vehicle financing (captive financing companies and independent financing companies)
commercial finance companies
government lenders
nonprofit lenders

Excluded are Farm Credit System lenders and motor vehicle dealers. Merchant cash advances are excluded from covered transactions, so those providers are no longer required to collect data. Other types of excluded transactions are described below.

Compliance deadlines for 1071 small business lending data regulations

The final 1071 rule is effective on June 30, 2026. Covered financial institutions have a 1071 compliance date of Jan. 1, 2028. The first reporting deadline for getting data to the CFPB is June 1, 2029. Lenders will want to use the time before 2028 to assess their coverage status, update workflows, prepare controls for data collection, and monitor implementation updates from the CFPB.

To prepare for the deadlines, lenders may begin gathering the otherwise protected demographic information one year before the collection deadline as long as they do so in compliance with the 1071 rule. This head start can help institutions ensure timely compliance and address any challenges in advance.

Financial institutions close to the 1,000 originations threshold

A financial institution that did not originate at least 1,000 covered credit transactions for small businesses in each of calendar years 2026 and 2027 but subsequently originates at least 1,000 of them in back-to-back calendar years will have to begin tracking 1071 application data in the following year. As a result, smaller-volume institutions close to the compliance threshold will want to monitor volume so they can plan and prepare for compliance requirements.

Key data points under 1071 small business lending data regulations

Covered banks, credit unions, and other creditors will need to collect and maintain more than a dozen pieces of data for each application by a covered small business, and they will have to report the annual data to the CFPB the following June. The small business lending data points cover a wide range of details related to the credit transaction, the business’s attributes, and demographic data. The 2026 final rule removed application method, application recipient, denial reasons, pricing information, and number of workers from the prior rule's requirements.

Required 1071 data collection points are:

Unique identifier: an alphanumeric identifier beginning with the institution's Legal Entity Identifier (LEI)
Application date (the date the application was received or shown on the application form)
Purpose of the credit (e.g., purchase, working capital, construction, etc.)
Amount applied for (the initial amount or credit limit requested)
Credit type
Guarantee type
Action taken on the application (originated, approved but not accepted, denied, withdrawn, or incomplete)
Date of action taken
Amount approved or originated
Census tract
Gross annual revenue (for the applicant's preceding fiscal year)
NAICS code (a 3-digit North American Industry Classification System code)
Time in business
Business ownership information:
a) Whether the applicant is a minority-owned and/or women-owned business. LGBTQI+-owned business status has been removed entirely.
b) The number of principal owners, and for each principal owner the person’s:
i) Ethnicity (choosing only one answer from the aggregate categories of “Hispanic or Latino” or “Not Hispanic or Latino)
ii) Race (choosing one or more from among five aggregate categories: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, or White)
iii) Sex (male or female). This option replaces a freeform “sex/gender” field in the previous final rule.

Appendix E in the CFPB’s final rule shows a sample form for collecting applicant-provided demographic information about principal owners. The sample form reminds applicants they are not required to provide the information, and the lender cannot discriminate based on a person’s race, ethnicity, or sex, or even on whether the person provided the information. The final rule notes that financial institutions can use different language “that better suits their customer relationships, provided the notice informs the applicant of the statutorily required information.”

Tips for streamlining small business lending data collection

Given the scope of effort needed to collect and report data by the CFPB deadlines, some financial institutions are already taking action. In fact, if you are a covered lender and must comply beginning Jan. 1, 2028, we recommend beginning your work immediately and giving yourself at least nine months of testing. For those who may feel overwhelmed by the tasks ahead, the following steps can help organize and streamline the data collection process:

Understand the rule and related requirements. Make sure others involved in lending are familiar with the Dodd-Frank section 1071 regulations and the specific requirements for CFPB small business data collection.
Review existing data collection practices. Identify what data is already being collected and where gaps exist. Some data may be available within the financial institution’s systems, while other data points will need to be obtained from applicants.
Assess current systems currently used for data collection and reporting. Determine whether these can be leveraged for 1071 data collection and whether new or updated systems are needed.
Assess the current lending process (i.e., how information is gathered). This assessment likely will require reviewing the institution’s credit culture if certain required data points are missing from the current application process.

Technological solutions for efficient 1071 data collection

Automation plays a critical role in streamlining CFPB small business data collection. Software solutions designed for data collection and analysis can help lenders focus on the borrowers and winning deals while ensuring compliance with the 1071 small business application data regulations. These tools can also make it easier to review and submit the information to the CFPB efficiently. Abrigo’s product team worked with the CFPB throughout the rulemaking process and has built 1071 compliance into its small business loan origination software.

While ease of data access is important, in general, if the institution doesn’t employ the firewall exception, CFPB prohibits underwriters or any employee responsible for the disposition or “making a determination” on an application from accessing certain demographic data. Abrigo’s software integrates 1071 compliance features such as built-in firewalls and user permission controls to help maintain fair lending and compliant reporting.

Preparing for regulatory changes

While organizing the data collection process is crucial, it’s also important for financial institutions to take broader steps to prepare for these regulatory changes. These include educating staff and revising policies and procedures to align with the 1071 small business lending data regulations. In addition, given legislative and legal efforts to continue making changes to the final rule, lenders should monitor related developments and activities.

1071 Data risk management and compliance strategies

Compliance with the 1071 small business lending data regulations will require coordination across multiple departments. To mitigate risks associated with non-compliance, financial institutions should:

Create a formal project plan and timeline for compliance efforts.
Plan for the training of all relevant staff involved in data collectors, reporting, and underwriting.
Establish consistent lending processes to promote data accuracy and compliance.
Consider the formality of the current borrower application process and identify any culture changes needed.
Automate processes to reduce manual errors and speed processes as well as provide tracking and timing evidence of when the demographic data was obtained.
Develop internal controls, including those that validate and test the data collected.
Track and report exceptions, particularly those related to pricing, fees, and loan structures.

Some financial institutions will need to formalize their small business loan application process. Others may decide to balance small business relationship lending with a risk-based pricing model to mitigate unintended disparate treatment among lenders and branches.

For institutions facing challenges, 1071 questions, or staff resource constraints, engaging experienced consultants can help. CFPB 1071 consultants can establish reporting and monitoring processes and recommend any needed policy changes.

The CFPB’s 1071 small business lending data regulations represent a momentous change in how financial institutions must collect and report data. By understanding the requirements, preparing in advance, and leveraging technology, banks and credit unions can navigate the changes with compliance. Start planning now to make sure your institution is ready for a smooth data collection process under the 1071 rule.

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FAQs

What is 1071 data collection?

1071 data collection is the process that covered lenders must follow to collect and report specific small business credit application data to the CFPB. The rule requires information about the application, credit decision, business, location, revenue, ownership, and certain demographic details to support fair lending oversight.

Who must gather data for the CFPB’s 1071 rule?

Financial institutions must gather data for the CFPB’s 1071 rule if they originated at least 1,000 covered small business credit transactions in each of the two preceding calendar years (either 2026 and 2027 or 2025 and 2026 for initial coverage determination). Covered institutions can include banks, credit unions, online lenders, commercial finance companies, nonprofit lenders, government lenders, and certain equipment or vehicle financing lenders.

What information is required when collecting small business loan data?

Section 1071 requires lenders to capture application, credit decision, business, location, revenue, ownership, and demographic information. Required fields include application date, credit type, amount requested, action taken, Census tract, gross annual revenue, NAICS code, time in business, and certain principal owner demographic details.

When do lenders need to collect data for complying with Dodd-Frank Section 1071?

Lenders covered by the rule must begin collecting data for complying with Dodd-Frank Section 1071 by Jan. 1, 2028. The final rule is effective June 30, 2026, and the first CFPB reporting deadline for collected data is June 1, 2029.

How can banks and credit unions prepare for small business lending data reporting?

Banks and credit unions can prepare for small business lending data reporting by assessing coverage status, reviewing current workflows, identifying data gaps, updating systems, and training staff. Strong internal controls, testing, firewall protections, and automated data collection and validation can help reduce errors and support compliant 1071 reporting.

New timelines for small business loan data collection and reporting

The Consumer Financial Protection Bureau (CFPB) in 2026 issued a final 1071 rule that extends the section 1071 compliance date for all covered financial institutions to Jan. 1, 2028. The new rule for collecting data on small business loan activities replaces a 2023 rule framework and its tiered implementation deadlines.

You might also like this one-page PDF with key dates and details on complying with the 1071 rule.

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This post was updated to reflect new compliance deadlines finalized by the CFPB on May 1, 2026.

Final rule

As they do with any new requirement, financial institutions want to know when the CFPB 1071 rule is effective and when they must begin collecting and reporting data on their small business lending activities.

The effective date of the Consumer Financial Protection Bureau’s (CFPB) new rule was August 29, 2023.

However, the CFPB recently finalized later deadlines for compliance and reporting the data collected about small business loan applications. With an initial compliance due date of Jan. 1, 2028, for all covered financial institutions, lenders with higher volumes of originations should begin preparing now.

The CFPB’s small business data collection rule implementing Dodd-Frank 1071 has gone through many iterations in recent years. Court cases and changes to the rule have delayed compliance dates numerous times, in much the same way compliance with the current expected credit loss (CECL) model was delayed by several actions.

In the case of 1071, the CFPB in 2025 undertook a comprehensive review of the rule and finalized its changes on May 1, 2026, so financial institutions now have a clearer picture of deadlines and requirements.

Lenders should continue to monitor ongoing regulatory and legislative efforts to revise collection and reporting deadlines and requirements. But for now, the new 1071 compliance and reporting deadlines are as follows:

Type of 1071 deadline	Deadline
Data collection efforts must begin	January 1, 2028
Report data to CFPB	June 1, 2029

Source: CFPB

How to stay ahead of compliance

Despite the seemingly long runway to prepare, it's not too early to get a handle on the new requirements and how they will affect a bank or credit union. With the changes, many financial institutions face the most significant data collection and reporting effort in nearly 50 years. Given this scope, lenders need to begin assessing now how and when they will comply.

In addition, the CFPB has made it clear it may expand 1071 reporting requirements over time, so smaller-volume lenders will want to monitor 1071 rule developments. The final rule described that the bureau is taking an incremental approach to “better serve the statutory purposes of section 1071 in the long term.” It said:

“Such an approach will start with core lending products, core providers, and core data points….Over time, as the Bureau and financial institutions learn from early iterations of data collections, the Bureau could consider amending the rule.”

Abrigo has helped thousands of bank and credit union staff members learn more about 1071 and how to prepare for it through educational webinars, podcasts, and whitepapers. The company, which provides lending and compliance solutions to more than 2,400 financial institutions, has 1071 lender resources to help financial institutions capture small business loan data, store it, and report it to the CFPB to comply with the required timelines.

CFPB 1071 resources include Abrigo's small business loan origination software for automating 1071 data collection and reporting. It has built-in data firewalls and permissioning features that allow covered financial institutions to collect the required data and file it with the CFPB in compliance with the new rule. Abrigo's 1071 reporting capabilities mean banks and credit unions can collect all required data fields in a borrower-facing form, access pre-built reports, and easily enforce firewall requirements to limit access to 1071 personal data.

Below are important details on 1071 compliance, including which financial institutions must comply, what the changes involve, and important 1071 compliance dates.

Fair lending regulations

Before discussing 1071 compliance dates and detailed requirements, it’s helpful to understand the rule’s goals and which financial institutions it affects.

The final rule implements section 1071 of the Dodd-Frank Act by amending the Equal Credit Opportunity Act (ECOA), or Regulation B (Reg B). The CFPB small business lending data collection regulations are being included as subpart B of Reg B and aim to support and enforce the fair lending requirements. CFPB intends the data collected by lenders on each small business credit application to facilitate enforcement of fair lending laws, especially those related to minority-owned and women-owned small businesses. Reporting on the data is also expected to help creditors, communities, and governmental entities identify small business owners’ needs and credit opportunities.

While the 2023 final rule for small business lending data collection meant lenders would have to collect more than 80 pieces of data per application, the 2026 final rule has streamlined collection and reporting. This final rule removes the discretionary data points for application method, denial reasons, pricing information, and number of workers from the prior rule's requirements. It also narrows the reporting categories for race, ethnicity, and sex of principal owners, and eliminates the need to determine LGBTQ+ ownership status.

Covered lenders & credit types

The rule outlines that any company or organization engaged in lending activities may be covered by the requirements. Farm Credit System lenders and motor vehicle dealers are excluded, but banks, credit unions, savings associations, online lenders, commercial finance companies, non-profit lenders, and government lenders are among those that will need to determine whether they meet the origination threshold for compliance.

To be subject to the rule’s requirements at all (i.e., to be considered a “covered financial institution”), a company or organization must have originated at least 1,000 covered credit transactions in each of the preceding two calendar years.

Institutions can use origination counts from either 2026 and 2027 or from 2025 and 2026 for the initial determination of whether it is a covered financial institution. Institutions that aren’t covered initially are required to begin tracking and reporting the small business lending data once they meet the threshold of 1,000 covered originations in two preceding calendar years.

What is a covered transaction

The CFPB generally describes it as a request for any of the following:

loans
lines of credit
credit cards

One change from the 2023 rule is that the 2026 final rule excludes from the list of covered transactions the following:

merchant cash advances (MCAs)
agricultural lending
loans of $1,000 or less.

That $1,000 threshhold will be adjusted for inflation every five years.

For purposes of determining whether a financial institution is covered by the rule, requests for additional credit on an existing loan are not counted as originations. They are, however, covered transactions as they relate to tracking data for small business loan applications by covered financial institutions.

Defining "application" for a covered transaction

For data collection and reporting, financial institutions must track applications they receive for covered transactions, as opposed to solely tracking originations. What is an application under the CFPB 1071 rule? It is an oral or written request for a covered credit transaction that is made following the procedures used by a financial institution for the type of credit requested. This means that lenders must track data not only related to approved and booked credit but also applications that are for more than $1,000 in credit and are any of the following:

withdrawn
incomplete
denied
approved by the lender but not accepted by the applicant

Would you like to stay up to date on CFPB 1071 implementation?

A re-evaluation, extension, or renewal request on an existing business account is excluded from the definition of covered applications as long as the request seeks no additional credit. Inquiries and prequalification requests are also excluded.

Excluded small business credit types

As noted earlier, in addition to loans under $1,000, the final rule excludes merchant cash advances and agricultural lending from the list of reportable transactions. Other types of transactions excluded from the CFPB’s requirements to report on applications include:

trade credit
public utilities credit
securities credit
incidental credit
insurance-premium financing
factoring
leases
consumer-designated credit used for business/ag purposes, such as taking out a home equity line of credit or charging business expenses on their personal credit cards
purchases of originated covered credit transactions
applications with potential HMDA and section 1071 overlap: CFPB does not require reporting under section 1071 (transactions would only be reportable under HMDA)

A final component of the rule that is useful in understanding the various deadlines for 1071 reporting is the CFPB’s description of what constitutes a small business. An applicant or borrower is considered a small business if it had $1 million or less in gross annual revenue for its preceding fiscal year before applying. That threshold was lowered from the earlier rule framework’s definition of $5 million in annual revenue.

Abrigo can help you navigate 1071 deadlines and compliance. In addition to our 1071 resource page for lenders, which has updated information to help prepare for the new requirements, Abrigo’s Loan Origination Software already has all required data fields in a borrower-facing collection form, access to pre-built reports, and the ability to export for CFPB reporting. Your financial institution can comply with 1071 while streamlining the origination process and ongoing customer management by working with a trusted partner of 2,400 institutions. Talk to a specialist to learn more.

Implement 1071 with confidence and control. Abrigo Advisory Services can help.

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Nontraditional credit pathways are the new competition

While credit unions continue to post solid lending results, a growing share of borrowing activity is occurring outside credit union channels. Younger consumers are actively using credit, but they are increasingly choosing nontraditional pathways to access it. As a result, institutions may need to look beyond funded loan volume to understand whether they are capturing future borrowers.

This article covers these key topics:

Consumer lending still looks strong on paper
Younger borrowers are active, but borrow differently
Relationships require time that automation can free up
Investigative questions around engaging with younger members

Consumer lending still looks strong on paper

Credit unions continue to report healthy lending performance across several core measures. According to recent NCUA data, federally insured credit unions have continued to grow loans, assets, and membership while maintaining relatively stable credit performance. For many institutions, these results reinforce confidence that consumer lending remains healthy and resilient.

Those numbers primarily measure activity that has already reached the institution. They do not show how many consumers considered borrowing but chose another provider. They do not reveal which financing decisions occurred before a member ever visited a credit union website. And they do not capture borrowing activity that happens through channels outside the traditional application process.

In other words, traditional lending metrics can only tell us what entered the funnel, not what bypassed it.

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Younger borrowers are active but borrowing differently

Research from TransUnion shows Gen Z consumers are becoming credit active earlier and at higher rates than previous generations at similar ages. At the same time, the Consumer Financial Protection Bureau has documented the rapid growth of Buy Now, Pay Later financing and other point-of-sale credit options. Taken together, these trends suggest that younger consumers are not avoiding borrowing, but are increasingly encountering credit in new places.

A consumer shopping online may be offered financing at checkout. A large purchase may come with installment-payment options embedded directly into the buying experience. In many cases, the financing decision is made before the consumer actively shops for a loan. This creates a challenge for credit unions.

Historically, lenders competed when a borrower decided they needed credit. Today, that decision often occurs within a retail, digital, or fintech environment where the credit union may never be considered.

Relationships still matter, but they require time

The growth of alternative lending channels does not necessarily mean younger consumers no longer value financial guidance. Many borrowers still seek trusted advice when making major financial decisions, comparing financing options, or evaluating the long-term impact of borrowing. That has long been one of the credit union movement's strengths.

As borrowing channels become more fragmented, that strength may become even more important. While adopting digital lending can be a draw for younger members, credit unions are unlikely to out-fintech every fintech or outspend every digital lender. What they can offer is a combination of trusted relationships, financial guidance, and personalized service that many alternative lenders cannot easily replicate.

Automating can help free up lenders

Many lenders continue to spend significant portions of their day gathering documents, tracking down information, managing workflows, and completing other administrative tasks. But every hour spent on manual processes is an hour not spent engaging members, answering questions, or identifying borrowing needs before those needs are met elsewhere.

Investing in the right technology can help free lenders from routine administrative work, allowing them to spend more time on business development and customer relationships.

For credit unions seeking to engage younger consumer lending borrowers, lending efficiency can create capacity for the conversations and guidance that strengthen member relationships.

Get curious about new generations of members

Current lending performance is supported in part by long-established member relationships. Many credit unions continue to benefit from strong member engagement, with members maintaining borrowing relationships for years or decades. These consumers are often more likely to return to familiar institutions when financing needs arise. If younger consumers increasingly encounter credit through alternative channels, credit unions may need to find ways to engage them earlier in the borrowing journey. This starts with asking a different set of questions:

Where are members borrowing when they do not come to us?
What percentage of borrowing decisions never enter our application funnel?
Are lenders spending enough time building relationships before borrowing needs arise?
How would we know if younger consumer lending borrowers were disengaging before loan volume begins to decline?

These questions may provide a more forward-looking view of lending performance than funded volume alone. Combining streamlined lending operations with the relationship-focused approach that has always differentiated credit unions may better equip credit unions to reach younger consumer lending borrowers before financing decisions are made elsewhere.

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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FAQ

Why should credit unions be concerned about borrowing activity outside their institution?

Borrowing activity that occurs outside the credit union may signal changing consumer preferences and decision-making habits. If members increasingly choose financing options offered through retailers, fintechs, or digital platforms, credit unions may have fewer opportunities to build lending relationships that can lead to future products and services.

Why are younger consumers using alternative credit options?

Younger consumers increasingly encounter credit at the point of sale through Buy Now, Pay Later programs, embedded financing offers, and digital lending platforms. These options are often integrated directly into the purchasing experience, making them convenient and immediately accessible. While traditional loans remain important, many younger borrowers are exploring multiple credit channels depending on the purchase and situation.

Does strong loan growth mean a credit union is successfully reaching younger borrowers?

Not necessarily. Loan growth is an important measure of performance, but it only captures activity that reaches the institution's lending funnel. A credit union can experience healthy loan growth while still missing opportunities to engage younger consumers who are obtaining credit through alternative providers before ever considering a traditional loan application.

How can credit unions strengthen relationships with younger borrowers?

Building relationships with younger borrowers often starts before a loan application is submitted. Financial education, personalized guidance, proactive outreach, and convenient lending experiences can help credit unions remain relevant throughout the borrowing journey. Establishing trust early may increase the likelihood that consumers consider the credit union when future financing needs arise.

What role does lending automation play in member engagement?

Lending automation can help reduce the time lenders spend on administrative and manual tasks. By streamlining workflows, gathering information more efficiently, and accelerating decision-making, credit unions can create more capacity for lenders to focus on member conversations, financial guidance, and relationship-building activities that differentiate the institution from many alternative lending providers.

Shift toward trade-based business is a good thing for CFIs

AI is starting to influence career choices, and recent reporting suggests a growing number of young adults are moving away from white-collar tracks and toward skilled trades they see as more resilient. This shift could lead to more startups, more independent contractors, and more equipment-heavy Main Street businesses. For community financial institutions, that is a signal to look more closely at trade-based business lending.

Simpler processes for greater performance.

Equipment leasing software

The new generation of business owners

A Harvard Kennedy School survey found 59% of 18 to 29-year-olds view AI as a threat to their careers, while employment for young adults in AI-exposed jobs has fallen 16%. The same report said vocational-based community college enrollment has risen nearly 20% since 2020. NPR reporting has pointed in the same direction, describing a “toolbelt generation” and rising interest in vocational paths tied to HVAC, electrical, and wind-turbine work.

When more electricians, plumbers, HVAC technicians, welders, and contractors enter the market, one of their first steps is often equipment financing: a truck, a trailer, a compressor, a lift, or a set of specialized tools that allows them to take on jobs and bill customers. The bank or credit union that can engage a trade-based business customer is financing the machinery behind a revenue stream.

The Bureau of Labor Statistics projects that electricians will grow 9% from 2024 to 2034, heating, air conditioning, and refrigeration mechanics and installers will grow 8%, and plumbers, pipefitters, and steamfitters will grow 4%. Overall employment in installation, maintenance, and repair occupations is projected to grow faster than average over the decade.

Why equipment finance fits the borrower profile

For banks, trade-based business lending is especially attractive because equipment finance ties the credit decision to a tangible, income-producing asset. A truck, trailer, skid steer, or commercial HVAC unit does more than sit on a balance sheet; it helps the borrower generate the revenue that supports repayment. That gives lenders a financing structure that matches the way the business actually operates.

Equipment lending is often a better fit than a generic unsecured loan. Many newer trade businesses do not need broad corporate borrowing capacity on day one, but they do need the specific asset that helps them complete jobs, take on larger contracts, and move faster than their competition. A financing program built around the equipment purchase can meet that need without forcing the borrower into the wrong product.

Moving early to stay ahead

Banks that update underwriting, documentation, and product design to support this growing pool of borrowers will be a step ahead of their competitors. The first institution to build trust with a new contractor or small trade owner is often the one that gets the next request for a line of credit, a deposit account, treasury services, or a second piece of equipment.

Instead of chasing a trend, trade-based business lending is a strategic way to align the balance sheet with where the next generation of business owners is likely to emerge. As more young workers choose trades that feel stable in an AI-shaped economy, banks that understand the borrower’s tools, cash flow, and growth path will be better positioned to serve them.

Next steps for community financial institutions

The moral of the story is that AI is changing where people see opportunity. Some of that opportunity is moving into the trades, creating a pipeline of borrowers who are more asset-dependent, more local, and more relationship-driven than many banks and credit unions may expect.

AI is also speeding up financial institutions' workflows and changing borrowers' expectations regarding speed and digital capabilities. Modernizing their processes can keep community financial institutions competitive and help them allocate more time to personal relationships with members and customers.

In addition to saving time and creating happier customers and partners, an automated equipment finance operation also helps the organization with:

Risk reduction: Automated audit trails and compliance checks reduce manual errors and documentation gaps.
Improved analytics: Integrated platforms centralize data across contracts, assets, and vendors—giving executives better insight into profitability, risk exposure, and performance trends.

Unlock growth beyond CRE. Learn about the equipment financing opportunity in this webinar.

Improve monitoring for emerging credit risks

AI improves credit risk monitoring by analyzing portfolio data in real time and helping teams quickly identify trends, exceptions, and potential risk exposures. Learn why traditional monitoring falls short late in the cycle and why modernizing processes helps with AI adoption.

This article covers these key topics:

A new phase for credit risk monitoring
Why traditional monitoring falls short late in the cycle
Modernizing processes yields data accessibility
What AI looks like in day-to-day credit risk management
Judgment remains central; AI strengthens it

A new phase for credit risk monitoring

Credit risk monitoring is entering a new phase. The fundamentals haven’t changed; sound judgment, defensible assumptions, and clear communication still matter.

But late-cycle conditions are exposing the limits of periodic, backward-looking reporting. The combination of modern data visualization and artificial intelligence (AI) offers a practical way to see emerging risk sooner, ask better questions in real time, and connect allowance work to day-to-day credit monitoring.

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Why traditional monitoring late cycle falls short

Credit risk has always been a science and an art. Institutions vary widely in their approach to credit risk modeling and monitoring. But many traditional credit risk models and processes share a common limitation: they rely on periodic data pulls, “black box” third-party models, and static assumptions. And in many cases, analysis is limited to retrospective/historical review.

Processes that rely entirely on past loss rates, monthly delinquency positions, and/or instrument-level probabilities of default (PDs) from a third party that haven’t been backtested against your own experience or that of named peers are increasingly insufficient this late into a credit cycle.

There are real advantages to evaluating or re-evaluating your approach to credit monitoring and adjacent process in the current environment. AI and modern visualization tools can help leadership charged with managing and monitoring credit risk by providing real-time data and trends, relevant industry data, and consolidating inputs and outputs from critical models such as allowance, stress testing, ALM, and deposit-related tools.

Moving beyond allowance in a vacuum

The allowance is the one area of credit modeling that directly impacts financial statements. The process is subject to external audit and examination. For these reasons alone, it’s common for institutions to modernize the process. As we all know, some choices can have a cascading effect throughout an organization, and this area is one of them.

When the allowance is managed in a spreadsheet or in a vacuum, the exercise becomes one of data entry, simple historical loss rates, storage, filed away spreadsheets, and canned reports. It becomes challenging to communicate inputs, assumptions, and results to anyone within the organization removed from the actual creation of the “answer.” To simply view output trends becomes a time-consuming exercise for everyone involved.

There are also approaches that may seem advanced, such as some third-party provided PD and LGD that haven’t even been backtested against your own experience, but they can’t be audited/reviewed. Nor can the default rates be explained by leadership. This severely limits the value of the entire process and leadership’s ability to let the allowance process become an integral part of credit risk monitoring.

Modernizing processes yields data accessibility

As leadership thinks about AI, it’s important to consider that one critical step in realizing the benefits is to begin modernizing processes in such a way that the inputs and outputs to key processes are accessible. For example, if the allowance is designed thoughtfully and not isolated to a spreadsheet environment or the result of a black box model, anyone in management could, at any moment and without request, observe through real-time visualization tools the following key allowance and credit monitoring trends:

Segment-level allowance level trends (obvious)
Segment-level realized default rate trends relative to default rate assumptions used in the allowance
Various economic scenarios and resulting segment-level allowance levels and underlying default rate expectations
Allowance change attribution (drivers of change – balance, forecast, qualitative, etc.)
Input and assumption trends
Qualitative factor allocation trends
Relevant industry data and trends for relativity (coverage ratios, default rates, loss rates, loan growth, etc.)

That visibility can turn the allowance from a quarterly (or monthly) output into an always-on monitoring lens—one that leadership can review, discuss, and challenge without waiting on a report run.

On top of yielding real-time visualization, communication, and quality of the output, the organization of inputs, assumptions, and underlying data enables financial institutions to now experience real benefits from AI. It is no longer a difficult lift and paves the way to move beyond theory and into tangible benefits.

What AI looks like in day-to-day credit risk management

Let’s take the above example one step further. While viewing the real-time data, anyone in management may see something that stands out to them and prompt AI to “list all of the loans that have downgraded between December and March” or “summarize all delinquencies in Commercial by industry code.” The point: you can now react to what the data is showing with instant answers, without data pulls, spreadsheets, or difficult-to-communicate requests to others in the organization.

Once that foundation is in place, AI stops being theoretical and becomes usable, starting with simple, high-value questions that connect what you’re seeing to what needs attention.

Real-time portfolio and concentration monitoring

CRE exposures, relationship concentrations, geographic risks, loan-structure anomalies, exception tracking, and borrower-level stress are just a few examples of rapidly evolving items that may require frequent threshold mapping, tracking, and monitoring.

Traditional reporting can be time-bound (periodic) and relatively rigid, often proving difficult or requiring custom work to drill down into the details. AI-powered monitoring systems can not only track concentrations continuously but also allow user interaction in a way that wasn’t possible without report-writing skills or specific requests of those with report-writing skills. They allow users not only to drill down into the underlying data, but also to ask questions beyond the data shown.

For risk and finance teams, AI-powered environments offer new time-saving abilities and avenues of understanding. Imagine you’re reviewing your daily dashboard, specifically, utilization, and you notice it’s increasing beyond historical trends. You prompt, “list the loans with the largest increase in utilization with a 6-month trend of their respective days past due.” You notice that a few loans with increasing utilization have gone from zero to 5,10, or 15 days past due. AI then asks you, “Would you like this to be included in your dashboard in the future?”

You’ve avoided pulling 6 months of loan files, organizing data, and writing formulas in a spreadsheet (or requesting that someone else do this). Instead, you get immediate information and have improved the shared dashboard for others in your organization. Just as important, this approach helps teams move upstream—spotting patterns that often show up before delinquency forces the conversation.

When it comes to borrower-level stress, it generally, doesn’t appear overnight. Often, there are subtle changes early on:

Slower prepayment patterns
Higher/increasing utilization
Industry performance decline (external data)
Economic pressures
Deposit/cash depletion

Instead of waiting for delinquency metrics to materialize, AI provides efficient ways to identify potential trends and research their specifics before taking action. Ultimately, this type of monitoring strategy improves mitigation options.

Judgment remains central; AI strengthens it

Credit risk management still requires experienced practitioners to interpret results, challenge assumptions/recommendations, and to consider qualitative information in decision-making. AI can efficiently provide information in a way that offers visibility, clarity, and insight into current and emerging risk patterns.

Institutions that choose to silo important credit risk functions into spreadsheets, black box third-party tools, and/or stagnant software risk falling behind. There must be a credible path for AI to, in real time, access inputs, outputs, and peripheral data in order to realize tangible benefits.

For leadership, the objective remains similar. Lead your teams with vision. Produce reliable and defensible channels of information. Efficiently (or autonomously) distribute the information so that everyone is making decisions with similar and sound knowledge. AI simply provides a more efficient and powerful set of tools to achieve that objective.

Adopt AI with confidence and control. Abrigo Advisory Services can help.

Abrigo AI Advisory

FAQs

What is AI in credit risk management?

AI in credit risk management uses advanced analytics and natural language interaction to help financial institutions monitor portfolio performance, identify emerging risks, and analyze large volumes of credit data more efficiently. It supports decision-making by providing faster access to insights while keeping human judgment at the center.

How does AI improve credit risk monitoring?

AI improves credit risk monitoring by analyzing portfolio data in real time and helping teams quickly identify trends, exceptions, and potential risk exposures. This allows institutions to investigate issues sooner instead of relying solely on periodic reports and historical performance reviews.

Why are traditional credit risk monitoring methods becoming less effective?

Traditional monitoring approaches often rely on static reports, historical loss data, and periodic reviews that may not capture changing risk conditions quickly enough. In a late-cycle environment, emerging risks can develop between reporting periods, reducing visibility and delaying response times.

How can AI help identify emerging borrower stress?

AI can help detect early warning indicators such as increasing credit utilization, declining industry performance, reduced deposit balances, and changing payment behavior. Identifying these signals before delinquency occurs gives institutions more time to evaluate and mitigate potential credit risks.

What role does data accessibility play in successful AI adoption?

Data accessibility is a foundational requirement for effective AI implementation. When credit risk, allowance, and portfolio data are centralized and readily available, AI tools can generate meaningful insights, answer questions quickly, and support real-time monitoring across the organization.

Can AI replace human judgment in credit risk decisions?

No. AI is designed to enhance, not replace, human expertise. Credit professionals remain responsible for interpreting results, challenging assumptions, incorporating qualitative factors, and making sound risk management decisions based on a complete understanding of the institution's portfolio.

Rising losses after a credit stress lull

Recent industry data shows rising delinquencies, increasing charge-offs, and higher provision expense across multiple loan categories. While today's conditions are nowhere near the levels experienced during the Great Recession, the direction of the trends is noteworthy. More importantly, the pressure is not concentrated in one segment of the portfolio. Instead, signs of stress are appearing across consumer, commercial, and real estate lending.

During a recent Abrigo webinar, Dean Rohne, principal in the Financial Institutions Group at Doeren Mayhew, examined industry trends and discussed how credit unions can better understand and monitor emerging risk. His message was straightforward: The industry is seeing a meaningful shift in credit conditions, and institutions that understand where risk is building will be better positioned to manage it.

Credit performance is moving away from recent norms

One challenge in evaluating current credit risk is that many credit union leaders naturally compare today's performance to the unusually strong years that followed the pandemic. However, Rohne suggested taking a longer view. Looking beyond the pandemic helps remove the distortion created by stimulus programs, elevated savings balances, and excess liquidity that temporarily suppressed delinquency and losses.

The same pattern appears in charge-off data. Net charge-offs across credit unions have increased from historical levels that generally hovered around 40 to 50 basis points to levels closer to 80 basis points today. Provision expense has also increased significantly as institutions adjust reserves to reflect changing portfolio performance.

These trends matter because they directly affect profitability. Higher losses require larger provisions, which put pressure on earnings and increase uncertainty around forecasting and budgeting. More importantly, they signal that the industry is operating in a different credit environment than it was just a few years ago.

You might also like this resource: “A banker’s guide for CECL compliance and backtesting.”

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Credit cards are often the first place stress appears

Among consumer lending products, credit cards are showing some of the clearest signs of pressure. Credit card delinquency has climbed to levels not seen for several years, with losses following a similar trajectory. While every institution's portfolio will differ, the trend is significant because credit cards frequently serve as an early indicator of borrower stress.

When household budgets tighten, consumers often rely more heavily on revolving debt. Eventually, higher balances, elevated interest costs, and competing financial obligations can begin to affect repayment performance.

Because of this dynamic, credit card portfolios often deteriorate before other segments of the consumer portfolio show meaningful weakness. Rising credit card delinquency may be providing an early signal about broader pressures affecting members.

For credit unions, monitoring these trends can offer valuable insight into how consumer financial health is evolving across the membership base.

Used auto, commercial, and real estate portfolio challenges

Industry delinquency rates for used vehicles have remained elevated, generally hovering around 1.0% to 1.1% in recent years. While rising delinquency is concerning on its own, many institutions are also facing a second challenge: increased loss severity.

Vehicle values surged during and immediately after the pandemic, creating unusually favorable conditions for lenders. In many cases, repossessed vehicles retained enough value to significantly reduce losses. That dynamic has shifted.

As used vehicle values normalize, some borrowers who are deeply underwater on their loans are choosing to surrender vehicles. When collateral values have declined, the resulting loss can be substantially larger than what institutions experienced just a few years ago.

For credit unions with significant auto concentrations, understanding both delinquency trends and collateral value trends is becoming increasingly important. A portfolio may appear manageable based solely on delinquency metrics while still producing larger-than-expected losses when defaults occur.

Commercial lending

Commercial loan delinquency across credit unions has approached or exceeded 1% in recent periods, while business bankruptcy filings have increased nationally. Taken together, these indicators suggest that some business borrowers are facing growing financial strain.

The challenge with commercial portfolios is that deterioration can develop gradually before becoming visible through traditional delinquency reporting. That makes proactive monitoring especially important. Credit unions should evaluate risk ratings, industry concentrations, geographic exposure, borrower performance, and emerging trends that could affect repayment capacity.

As Rohne noted during the webinar, effective credit risk management requires more than reviewing delinquency reports. It requires identifying potential weaknesses before they become actual losses.

Real estate

Real estate lending presents a different story. Delinquencies are increasing across the industry, yet many credit unions have not experienced a corresponding increase in losses. In many cases, strong property values and borrower equity have helped limit loss severity. The question is whether those conditions will continue.

Future performance will depend heavily on local market conditions, housing supply, and property values. Markets with persistent housing shortages may continue to support collateral values even if delinquencies rise. Other markets could experience greater pressure if economic conditions weaken or home prices soften.

This is one reason why broad national statistics only tell part of the story. Credit unions should understand how local economic conditions influence the specific risks within their own real estate portfolios.

Understanding the story behind the numbers

Delinquency rates and charge-offs are important, but they rarely tell the complete story. Credit unions should examine portfolio performance through multiple lenses, including credit score migration, vintage analysis, concentration analysis, loan-to-value trends, debt-to-income ratios, and changes in underwriting quality. These tools help institutions identify where risk is changing and why.

For example, a portfolio may show stable delinquency today while credit scores across the borrower base are steadily deteriorating. Likewise, a particular loan vintage may be driving losses while newer originations perform well. Understanding those distinctions can lead to more informed lending decisions and stronger reserve estimates.

This analysis becomes especially valuable when supporting CECL assumptions. Institutions that can clearly explain where losses are occurring, what is driving them, and how conditions are changing are often better positioned to support their allowance methodology.

As Rohne described it, credit unions should focus on telling their "credit risk story" using data that explains both current performance and future expectations. The objective is to build the monitoring, reporting, and governance processes necessary to identify emerging risks early and respond effectively. Institutions that understand where risk is building today will be in a stronger position to manage tomorrow's challenges.

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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A growing risk

Learn how Abrigo Fraud Detection prevents friendly fraud.

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The next phase of fraud risk

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CFPB small business data collection under 2026 rule

This article covers these key topics:

Data collection under CFPB's new 1071 deadlines

Access more small business loan data requirements resources and guidance

The scope of small business data collection

Which types of credit are considered “small business loans” under 1071?

Which lenders must collect 1071 data

Compliance deadlines for 1071 small business lending data regulations

Financial institutions close to the 1,000 originations threshold

Key data points under 1071 small business lending data regulations

Tips for streamlining small business lending data collection

Technological solutions for efficient 1071 data collection

Preparing for regulatory changes

1071 Data risk management and compliance strategies

Automate 1071 data collection so you can spend more time talking with borrowers and less time keying data.

FAQs

New timelines for small business loan data collection and reporting

You might also like this one-page PDF with key dates and details on complying with the 1071 rule.

Takeaway 1

Takeaway 2

Takeaway 3

Effective dates & compliance dates for rule 1071

How to stay ahead of compliance

What are the goals of 1071?

Which lenders are "covered financial institutions" in the 1071 rule?

What is a covered transaction

Defining "application" for a covered transaction

Would you like to stay up to date on CFPB 1071 implementation?

Which credit transactions are excluded from 1071?

Implement 1071 with confidence and control. Abrigo Advisory Services can help.

Nontraditional credit pathways are the new competition

This article covers these key topics:

Consumer lending still looks strong on paper

See the benefits of embracing innovation.

Younger borrowers are active but borrowing differently

Relationships still matter, but they require time

Automating can help free up lenders

Get curious about new generations of members

You might also like this NCUA exam preparation guide

FAQ

Shift toward trade-based business is a good thing for CFIs

Simpler processes for greater performance.

The new generation of business owners

Why equipment finance fits the borrower profile

Moving early to stay ahead

Next steps for community financial institutions

Unlock growth beyond CRE. Learn about the equipment financing opportunity in this webinar.

Improve monitoring for emerging credit risks

This article covers these key topics:

Unlock valuable insights for strategic decision-making,
all in one place.