This post was updated to reflect new compliance deadlines finalized by the CFPB on May 1, 2026.
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.
Takeaway 1
The effective date of the CFPB's new rule based on Section 1071 of the Dodd-Frank Act is June 30, 2026, and the compliance deadline is Jan. 1, 2028. The initial reporting deadline is June 1, 2029.
Takeaway 2
To implement the 1071 requirements, covered financial institutions should begin by understanding the rule and determining their volume of small business loan originations in either 2026 and 2027 or 2025 and 2026.
Takeaway 3
The 2026 final rule excludes several types of small business credit. But the CFPB left the door open to future expansions of data requirements based on its learnings from initial 1071 reporting.
Final rule
Effective dates & compliance dates for rule 1071
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
What are the goals of 1071?
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
Which lenders are "covered financial institutions" in the 1071 rule?
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
Which credit transactions are excluded from 1071?
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.
Abrigo Advisory
Most problem loans do not become problems overnight. Warning signs often appear months or even years before a credit deteriorates, but they are easy to dismiss when performance still appears acceptable or when annual reviews become routine.
This webinar will explore why lenders and credit teams often miss early signs of deterioration. We will discuss practical ways to strengthen ongoing monitoring, revisit original underwriting assumptions, and identify small changes before they become larger credit problems.
You will learn:
- Why delayed risk recognition remains a common credit weakness
- How covenant fatigue, small red flags, and routine annual reviews can cause lenders to overlook meaningful signals
- Why original underwriting assumptions should be tested throughout the life of the loan
View the entire webinar series here.
Some of the most important credit risks never appear directly on a financial statement. Management quality, business strategy, customer concentration, succession planning, and organizational resilience can all determine whether a borrower performs as expected or begins to deteriorate.
This webinar will examine the qualitative mistakes lenders often make when assessing borrowers. We will discuss how to evaluate the people, strategy, and operating risks behind the numbers, and why strong financial results can sometimes mask deeper vulnerabilities.
You will learn:
- Why experience does not always equal management competence
- How relationship bias, key-person dependencies, and succession risks can weaken borrower assessment
- How customer, vendor, guarantor, and revenue concentration can affect repayment risk
View the entire webinar series here.
Financial statements are the foundation of credit analysis, but they do not always tell the full story. Reported earnings, ratios, projections, and management adjustments can create a sense of confidence that is not supported by the borrower’s actual cash flow, liquidity, or financial flexibility.
This webinar will explore common financial analysis mistakes that lead lenders to misread credit risk. We will discuss how to look beyond surface-level performance, challenge assumptions, and identify warning signs that may be hidden inside the numbers.
You will learn:
- Why earnings do not always translate into repayment capacity
- How liquidity, cash flow, and working capital issues can be missed
- When management adjustments, projections, and ratios deserve closer scrutiny
View the entire webinar series here.
What if your next loan review could be faster, more consistent, and more insightful without adding hours to your team’s workload? Join Abrigo and two financial institution leaders, Hannah Primes of Seacoast Bank and Sam Patton of Old National Bank, for a practical conversation on how they use Abrigo’s AI-powered Loan Review Assistant to strengthen review processes and get more value from every review.
In this webinar, Hannah and Sam will share real-world examples of how AI is helping their teams improve workflows, enhance review effectiveness, and increase consistency across reviews. They will discuss what has worked, lessons learned, and how thoughtful prompting can help loan review teams uncover meaningful insights faster while maintaining accuracy and confidence.
You will learn:
- How Seacoast Bank and Old National Bank are using Abrigo’s AI-powered Loan Review Assistant in their workflows
- Practical ways to maximize the value of AI during the review process
- Tips for building successful prompts that generate more accurate and useful outputs
- How AI can help save time while improving consistency across reviews
- Ways to enhance review effectiveness
- What real institutions have learned from implementing AI in loan review
Artificial intelligence is becoming a priority across financial institutions, with growing pressure from boards and leadership teams to move from exploration to implementation. While the industry continues to highlight AI’s potential, the real challenge is operational. Financial institutions are not struggling to find use cases. They are struggling to determine which ones they can confidently implement and stand behind in a regulated environment.
This session focuses on how community banks and credit unions are actually approaching AI adoption today, where implementation is gaining traction, and why some initiatives move forward while others stall. We will examine how institutions are evaluating AI through the lens of explainability, governance, and risk, and what that means for day-to-day decision-making in lending, fraud, and compliance.
You will learn:
- Where AI is delivering practical value today and why some use cases move forward while others stall
- A clear framework for evaluating AI based on explainability, control, and defensibility
- Practical steps to move from experimentation to real implementation without increasing risk
- Understanding of the AI Readiness Assessment
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.
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.
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.
Explore Abrigo's AI solutions
Visit the AI HubWhy 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 AdvisoryFAQs
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.
Leadership shapes every aspect of BSA management—from front-line alerts to boardroom decisions. Yet while leadership is often emphasized, followership is frequently overlooked. Effective leadership is not one-directional; it depends on how people receive, interpret, and respond to guidance.
This session explores leadership through the lens of followership, examining how follower styles influence outcomes, how trust and accountability are co-created, and why self-awareness as a follower is essential to becoming a stronger leader. Grounded in leadership theory and practical application, the webinar will provide actionable strategies you can apply whether you’re influencing from the front lines or guiding executive decisions.
Key takeaways
- Understand how followership influences leadership effectiveness and organizational outcomes
- Recognize how trust, accountability, and influence are built collaboratively
- Apply practical leadership and followership strategies to real-world BSA challenges
This session is best for program and team managers and Vice Presidents.
