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Scale equipment financing operations with workflow automation

Equipment leasing financial institutions are operating in a market where winners and losers are defined by digital transformation, artificial intelligence, and growing expectations from customers and partners. For many, that means modernizing their processes to keep up with the competition. 

Technology implementation strategy from experts

In a recent industry panel, technology leaders from Measured.ai, DLL, and Tokyo Century shared how IT strategy and AI-driven product development can reshape the way financial institutions approach equipment leasing. Technology is central to equipment leasing companies’ growth, risk management, and competitive differentiation. The importance of technology to success has big implications for how institutions modernize workflows, manage data, and think about automation. 

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Benefits of modernization: Speed and transparency 

For banks or credit unions offering equipment leasing, creating a better experience is the heart of most modernization efforts. New technology can reduce friction, increase clarity, and make progress visible across the lifecycle of a deal, from first contact through credit decisioning, documentation, funding, and servicing.  

Digital transformation can dramatically improve existing processes, making steps faster and more efficient. But a common approach that thwarts modernization efforts is when bankers try to use the same workflows they’ve used for 30 years, just with better tools. There’s a limit to how much can change with that mindset, according to Moto Tohno, Vice President of Information Systems at Tokyo Century. 

Real digital transformation connects people better, Tohno said. It requires rethinking how processes work from the ground up — especially how teams access and use data. “When you get to the right data in a better way,” he said, “you don’t just move faster. You create a better overall experience.” 

Modernization should improve, not cost, relationships 

The enduring competitive advantage businesses will have in the future is the relationships they have with their customers. What differentiates financing firms is “having the customer voice brought into every component of the organizational engineering,” said David Whipple, Chief Technology Officer at Measured.ai.  

That’s a helpful lens for equipment financers evaluating workflow automation. The goal is fewer unnecessary or meaningless touches so that staff have more time to understand customer/member needs and suggest the best solution. For example, how many times does a client need to refill a form with data that they’ve already provided in another conversation? If the answer is more than once, thoughtful innovation could improve workflow. 

Start with the business problem, not the shiny tool 

Technology modernization is all about beginning with the problem, defining success, then selecting the right tool. And panelists suggested that approach also applies to adopting technology powered by AI.  

“Adopting a technology because you’re in a fear of missing out mindset… is exactly the wrong reason,” Whipple said. 

Instead, the panel advised financial institutions to start pursuing AI tools only when you’ve identified the business problem that you’re trying to solve for and have a measurable way to define success. 

For equipment lenders, identifying the problem often starts with a few predictable pain points: 

  • Intake and document collection that relies on email chains 
  • Duplicate data entry across systems 
  • Unclear handoffs between sales, credit, operations, and servicing 
  • Exceptions handled manually with limited auditability 

Digital transformations of equipment lease financing are most effective when it targets these breakdowns specifically. When done intentionally, AI-powered technology implementation can improve not only efficiency and client experience, but transparency at every step of the process. 

“Rather than having subjective decisions,” Whipple said, “Consider how technology can help everybody along the value chain understand why certain decisions and outcomes are being made.”  

Above all, the panel advised organizing data before implementing any machine learning technologies. 

“AI is a layer of your data,” Whipple said. “And if you don't have clean data, you aren't going to get clear conclusions.” 

Modernizing workflows means modernizing roles and governance, too 

Modernization isn’t only technical — it’s operational and organizational. If equipment leasing lenders want new technology to stick, they’ll need to plan for role clarity and cross-functional ownership. As Camtu Vo, Manager of Product Development in DLL Group’s North American Food & Agriculture division put it, it’s not enough to modernize in silos. “I always run everything by legal, risk, and compliance,” she said. “Get the right people in the right room together and have those robust discussions.” 

Key metrics to measure technology ROI 

Panelists stressed that when it comes to determining whether or not your equipment financing processes have been truly improved, measuring activity isn’t the same as measuring improvement. 

For equipment leasing lenders, practical modernization metrics often include: 

  • time from application to decision (and decision to funding) 
  • rework rates (missing fields, doc exceptions, resubmissions) 
  • customer follow-ups required per deal 
  • internal handoff time between teams 
  • quality indicators (errors, overrides, policy exceptions) 

Overall, panelists emphasized that modernizing leasing finance requires more than new equipment. Institutions must define the problem, map the workflows, bring stakeholders along, and measure outcomes. Institutions should avoid using AI as a tool rather than a strategy. They should not deploy any new technology without clear success criteria, and they should not skip the data quality prep that will help them implement new technologies seamlessly.  

Equipment leasing institutions that modernize well will be the ones that use automation to reduce friction and increase transparency while protecting the people-to-people connections that customers still value most. 

 

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Why parallel testing is important in risk governance

Technology change is a constant for financial institutions. Whether the shift involves financial crime monitoring, lending platforms, portfolio risk, or asset/liability management models, new systems promise efficiency and insight but also introduce risk. Parallel testing when implementing new software is one of the most practical ways institutions can manage that risk while maintaining day-to-day operations and model risk management expectations.

What is parallel testing?

Parallel testing is the practice of running a legacy system and a new system simultaneously using the same data. The goal is not speed; the goal is confidence. Running both systems concurrently allows teams to verify data integrity, logic accuracy, and workflow performance without disrupting production. By comparing outputs side by side, institutions can validate that the new system performs as expected before entirely relying on it.

At its core, parallel testing answers a simple question: If the institution relies on the new system today, would outcomes change in a way that introduces risk? For example, alerts generated in a financial crime system, allowance calculations in a CECL process, or data outputs used for regulatory reporting should align closely between systems once configuration and tuning are complete. Differences are expected early in testing, but they should be explainable, documented, and resolved before going live. The new system must perform at least as well as the legacy system to support strong model risk management.

Gap analysis: Explaining system differences

A gap analysis is a natural extension of parallel testing and helps explain why differences appear between systems. By reviewing alerts, calculations, reports, or outputs side by side, teams can identify where the new system behaves differently from the legacy system and determine whether those differences reflect improved risk coverage, configuration issues, or data limitations. Not every gap will require remediation, but every gap must be reviewed during a system conversion. Clear documentation of why a difference exists, how it affects risk coverage, and whether it is acceptable is essential for model risk governance before the institution relies on the new system in production.

Benefits of running systems in parallel  

From a regulatory perspective, parallel testing demonstrates sound model risk management in banking operations. The OCC handbook describes a model as “a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates.”

Financial institutions are expected to understand how system changes affect risk coverage decision-making and compliance outcomes. Model comparison testing shows that leadership took reasonable steps to prevent gaps, missed activity, or unintended consequences.

This evidence of control is essential in areas subject to heightened scrutiny, such as anti-money laundering, fraud detection, fair lending, and regulatory reporting. Parallel testing regulatory models helps demonstrate that critical functions, such as suspicious activity monitoring, continue without interruption, that reports remain accurate, and that reliable data support risk ratings and model-driven decisions.

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Regulatory expectations for parallel testing

Supervisory Guidance on Model Risk Management (SR Letter 11-7) describes the key aspects of effective model risk management and sets regulatory expectations. The guidance does not explicitly mandate parallel testing when models are implemented, but the expectation is clear. When institutions modify or install new models to reflect new data techniques or performance concerns, regulators expect meaningful evidence that the changes improve results.

The guidance highlights parallel testing, or parallel outcomes analysis, as an essential approach for identifying gaps in new models. If the new or adjusted model does not demonstrate stronger performance, the institution should recognize that further refinement may be necessary before replacing the original model. In practice, this reinforces the case for running models in parallel, supporting sound model governance and defensible decision-making. It demonstrates that financial institutions aren’t changing models for change’s sake.  

The guidance also addresses documentation requirements. Clear records of testing scope, issue resolution, and management approval not only help examiners understand what changed but also provide proof of continuity and control.

Beyond compliance: Testing’s operational benefits

While compliance is often the initial driver, parallel testing delivers operational value across all pillars of the institution. It gives teams time to learn new workflows, identify training needs, and fine-tune processes before the pressure of full adoption.

It also creates space for informed decision-making. Differences between systems can reveal data quality issues, process inconsistencies, or risk assumptions that might otherwise have gone unnoticed. Addressing those findings strengthens the overall program, not just the new technology.

Most importantly, parallel testing protects customers, members, and communities. Whether the institution is monitoring transactions, underwriting loans, or managing portfolio risk, accurate systems support fair, consistent, and timely outcomes.

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Testing systems with a risk-based approach

There is no one-size-fits-all timeline or scope for parallel testing. Institutions should tailor their approach based on size, complexity, product mix, and risk profile. Higher-risk activities typically warrant broader testing and more extended overlap periods, while lower-risk changes may require a more targeted effort.

What matters is intentionality. A defined plan, clear ownership, independent review, and documented conclusions all signal that the institution approached the transition thoughtfully. Download a checklist for parallel testing of AML/CFT systems for more information.

Building confidence before going live

Technology should make complex work more manageable, not more uncertain. Parallel testing helps bridge that gap by allowing institutions to move forward with confidence rather than blind hope. When done well, parallel testing supports continuity, strengthens model risk management, and reinforces trust at every level of the organization.

In an environment where change is constant, taking the time to validate before switching entirely is not a delay; it is a best practice.

 

 

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Changing bank and credit union risk requires leadership 

What former banker and Abrigo Consultant Kent Kirby learned about managing financial institution risk in his 39+ years in all aspects of commercial banking.

Summary: 

Risk: Layered, interconnected, ambiguous

Being retired from banking offers a luxury I seldom had on the job: time to think rather than react. After decades spent moving from one urgent issue to the next, I now find myself reflecting on how differently I understood risk at 25 than I did at 65.

At 25, risk was simple. Would I close the deal? Would I get repaid? Other experienced bankers likely understand that by 65, risk to me had become layered, interconnected, and often ambiguous—less a calculation than a judgment call. I could probably write a book on the subject, but a few lessons stand out for bank or credit union leadership.

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1.  Risk is a team sport.

If anyone claims they fully understand all the risks facing a financial institution today, they’re not being honest. The operating environment has grown exponentially more complex. Products that were standard—and regulator-endorsed—in the early 1980s could invite criminal scrutiny today.

While risks are numerous, they can still be grouped into categories such as credit, operational, fiduciary, legal, compliance, and so on. The key is to rely on subject-matter experts and to make sure they communicate regularly with each other. Early in my career, bankers were poor at understanding risk at the enterprise level. Today, Enterprise Risk Management is an entire industry unto itself.

That said, ERM has too often become a ritual rather than a discipline. Done properly, it is essential, regardless of institution size.

Consider product development. Financial institutions have become highly effective at designing innovative credit products, aided by technology. Too often, however, we fail to ask a basic operational question: Can our systems and people actually support this product? Given the generally antiquated state of bank and credit union infrastructure, the answer is frequently “no.” The result is a patchwork of manual workarounds—prime territory for errors and, therefore, risk in its most basic form.

2.  Risk is not a wall. It’s an amoeba.

As a career credit banker, I once thought of risk as a wall. You could advance only so far before hitting default, and the objective was to avoid that collision.

That’s not how risk works anymore, if it ever did. Risk behaves more like an amoeba, constantly dividing and reshaping itself. New risks don’t replace old ones; they multiply.

Take artificial intelligence. A year ago, it barely registered in most risk conversations. Today, financial institutions feel pressured to deploy something AI-related simply to avoid being left behind. Yet many institutions have not had (or taken) the time to thoughtfully address the ethical, operational, legal, and reputational risks that accompany it.

Compounding this challenge is noise. Early in my career, news arrived twice a day. Today, it updates by the second, amplified by countless “experts” regardless of credibility. We even have a term, deepfake, for the darkest edge of this reality. Managing risk in this environment requires discernment.

Recent tariff debates illustrate this well. Initial reactions forecasted economic catastrophe reminiscent of the 1930s. The reality proved far more nuanced. Risk leaders must constantly balance speed with perspective.

3.  You will never have enough information. Decide anyway.

In all my years across institutions large and small, I never met a banker satisfied with their data. If I had 50–60% of the information I wanted to make a decision, I considered myself fortunate.

Directors and management understand that risk management is about judgment under imperfect conditions rather than about certainty. Waiting for complete information can create its own risk: the risk of delay or paralysis. Effective leaders develop the discipline to act with incomplete data while remaining flexible enough to adjust as new information emerges. The real skill lies in openly acknowledging uncertainty and deliberately managing it, since you can’t eliminate it.

4.  The most dangerous risk is the one you rarely see: culture.

Culture isn’t monolithic. It is a system of shared beliefs, values, and behaviors. Every employee carries multiple cultures—family, community, political, and religious—most of which are voluntarily adopted.

A financial institution’s culture, by contrast, is usually imposed. As a result, it is often poorly internalized and easily ignored. To make matters worse, institutional culture is frequently expressed through dense, elegant documentation. (How many pages is your credit policy?) Complexity plus lack of ownership leads to confusion, inattention, and, whether acknowledged or not, chaos.

Culture is foundational to an institution’s survivability. To manage it effectively, think of the hiss of a snake: sss.

Simple: If your culture is not simple, it will be ignored.
Succinct: If it is not succinct, it will be misunderstood.
Sustainable: If it is not sustainable, it will fail when it matters most.

Most importantly, culture must be reinforced through incentives. Goals, performance evaluations, and compensation decisions matter far more than mission statements. These may not be glamorous tools, but they are effective ones.

So yes, I may have felt smarter at 25. But what experience has taught me is that the discipline of risk is ultimately the discipline of leadership. In a world where risk keeps multiplying, the best financial institutions will be led by those who ask better questions, sooner, and create organizations capable of responding thoughtfully rather than reactively.

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This article covers these key topics: 

AI assistance for the CECL calculation is moving from theoretical to practical

For community financial institutions, the conversation around the future of artificial intelligence (AI) in banking is no longer theoretical. Leaders are asking practical questions about how AI helps banks operate more efficiently, where it delivers measurable value, and how it can be applied while maintaining transparency and trust. Nowhere is transparency more important than in a community financial institution’s CECL calculation.

Strengthening CECL processes with AI

Advances in automation and AI are creating new opportunities for teams to strengthen their CECL processes while maintaining the governance the standard requires. Now that the initial CECL implementation period is behind us, banks and credit unions are entering a new phase of figuring out how to manage their calculations most efficiently. The impact of AI on CECL processes will be most visible through enhancements that make complex processes easier to execute, explain, and defend.

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The evolving role of AI in banking and why it matters for CECL

Across the industry, AI is helping banks reduce manual effort, improve consistency, and find insights more efficiently. In areas like CECL, where accuracy, governance, and documentation carry significant weight, these benefits are especially meaningful.

Most community financial institutions (CFIs) have already made the foundational CECL decisions: 

  • How reasonable and supportable forecasts should be applied 
  • What governance framework supports consistent qualitative adjustments 

But making those decisions was only the beginning. Many institutions are discovering that CECL’s real challenge lies in execution. Manual workflows, disconnected systems, and spreadsheet-driven processes can limit an institution’s ability to fully leverage the insight CECL is meant to provide. As portfolios grow and regulatory expectations mature, execution becomes the primary challenge. This is where many of the advantages of AI in banking begin to take shape, especially when paired with purpose-built CECL solutions.

Using automation and AI to strengthen CECL execution

One of the most immediate benefits of AI in banking is its ability to reduce friction in operationally intensive processes. When it comes to CECL, automation streamlines data ingestion, accelerates calculations, and standardizes workflows across portfolios and reporting periods. These capabilities help support more reliable reporting cycles and enable teams to manage documentation requirements more effectively.

For decision-makers, this is where AI begins to deliver tangible return on investment. Faster close cycles, fewer errors, and greater confidence in results all contribute to stronger operational outcomes and better use of expert time. CECL teams no longer need to spend excessive time navigating tools or managing workarounds. Instead, they can focus on understanding results and making informed decisions. 

Platforms that incorporate AI will evolve from calculation engines into end-to-end systems that support analysis, documentation, and review—without sacrificing human control or judgment.

Maintaining oversight and trust as AI adoption grows

Any discussion about the future of AI in banking must address governance and control. AI should not select methodologies, determine forecasts, or apply qualitative adjustments. Those responsibilities must remain firmly within management's purview. Where AI adds value in CECL is by supporting execution around established management decisions.

One of the most resource-intensive parts of the process is documentation. Allowance results must be supported by clear, regulator-ready explanations that answer questions such as:

  • Why did the allowance change this period? 
  • How were economic conditions incorporated? 
  • Which assumptions had the most impact? 

AI offers a practical way to improve consistency and ensure compliance when answering these questions. Generative AI can help transform structured CECL data into complete, standardized narratives, making explanations easier to produce, review, and maintain across reporting periods. The result is stronger documentation quality with fewer opportunities for omission or unfounded assertions.

When used thoughtfully within well-governed systems, AI becomes a natural extension of modern CECL platforms. It reinforces process discipline, supports audit readiness, and helps institutions operate more efficiently without compromising transparency or control. This approach reflects the broader future of AI in banking: responsible innovation that strengthens oversight, improves outcomes, and builds confidence with regulators and stakeholders.

The broader impact of AI on CECL and banking strategy

Looking ahead, the future of AI in banking will be shaped by usability and integration. Institutions that combine CECL expertise with modern automation and applied AI will be better positioned to reduce risk, improve efficiency, and communicate results with confidence.

For CECL teams, this means seeking solutions that simplify execution, support consistent analysis, and help derive greater value from the decisions they have already made. These capabilities reflect a broader shift across banking, where AI is becoming a practical tool for improving efficiency, accuracy, and insight across core processes.

The future of CECL closely mirrors the future of AI in banking as a whole. Progress will continue to be driven by thoughtful innovation that improves outcomes while maintaining strong governance and professional judgment.

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Managing deposits proactively

Deposit behavior—how long funds stay, how sensitive they are to pricing, and where they ultimately flow—can signal risks and opportunities. Understanding and acting on those signals can help financial institutions strengthen margins, liquidity, and long-term performance. 

two men and woman around a desk

Rethinking balance sheet risks

After an extended period of rate volatility, margin compression, and liquidity pressure, financial institutions are once again being challenged to adapt. Bank leaders are looking for ways to manage risk and drive growth. Increasingly, they are finding those strategies through a deeper understanding of deposit behavior and its impact on balance sheet performance. 

Deposits remain the cornerstone of bank strategy, according to Rob Newberry, Senior Consultant at Abrigo, even as the dynamics around them evolve. “The deposits are the foundation of bank funding,” Newberry says. “Depending on how your institution is growing, you have to have enough funding to continue to fund the loan growth that you have. Are you growing at the pace of your deposit growth, or are you outgrowing it?” 

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Why deposit behavior has become a strategic priority 

For many banks, regulatory requirements or periodic ALM reviews have historically driven the use of deposit modeling. But that approach overlooks how deposit behavior directly influences liquidity planning, loan growth capacity, pricing decisions, and ultimately profitability. 

“Accurate modeling enhances risk management and strategic planning,” Newberry says. “One of the biggest things we want to understand from an analytics perspective is how long those deposits are going to be around, because that impacts your ALM assumptions and a lot of other decisions downstream.” 

The need for accurate modeling is particularly acute for community and regional banks, where funding options may be more constrained and customer concentration risks are higher. Newberry points to demographic exposure as an often-overlooked factor that can impact smaller institutions. “A good chunk of your deposit balances may be represented by people over seventy years old,” he notes. “How much longer are those deposits going to be around, and how does that wealth transfer to the next generation?” 

Understanding decay, stability, and longevity 

At the center of modern deposit analysis is decay—the rate at which balances naturally run off over time. While the concept is familiar, Newberry emphasizes that its strategic implications are often underestimated. 

“Decay rates measure the rate at which deposit balances diminish over time,” he explains. “It’s exactly like prepayments on the loan side. Deposits decline because customers withdraw funds, move money internally to other accounts, or shift balances to different investment types.” 

By pairing decay rates with weighted average life and effective duration, banks gain a clearer picture of how reliable their funding really is under changing market conditions. This distinction becomes especially important when separating core balances from surge balances, which are the funds that are more likely to leave when rates or conditions change. 

“Surge balances are an inflow of deposits triggered by an event, and they’re likely to flow back out relatively quickly,” Newberry says. “These balances are usually rate sensitive and can move at any time.” 

Failing to identify surge behavior can leave institutions exposed, particularly if temporary liquidity is mistakenly treated as long-term funding. 

Pricing strategy, cannibalization, and margin risk 

Deposit pricing remains one of the most visible tools banks use to compete for funding, but it is also one of the most dangerous if used without insight. Newberry cautions that raising rates to attract new money often triggers internal movement rather than true growth. 

“When you raise rates on a new account, you have to understand how much ‘old’ money is moving into that account,” he explains. “That internal transfer increases your interest expense, and sometimes you’re paying a lot more than you realize for the next ten million dollars.” 

This concept of marginal cost is critical in an environment where margins are already under pressure. “Sometimes paying up for deposits might actually destroy your margin instead of strengthening your balance sheet,” Newberry says. “It becomes a balancing act between growth and profitability.” 

Aligning deposit pricing behavior with loan repricing by using beta and lag thoughtfully can help institutions protect net interest margin while remaining competitive. 

Who’s in charge of deposits? 

Ultimately, the value of deposit analytics lies in how effectively insights are translated into action. That requires clear ownership and consistent focus. 

“One of the first questions we ask when we work with banks is: who is actually in charge of your deposits?” Newberry says. “You might have multiple people in charge of loans, but on the deposit side, it’s often fragmented. Someone really must be focused on deposits every day if you want to be successful.” 

By integrating deposit behavior, pricing dynamics, and demographic trends into ALM and forecasting processes, banks can plan ahead. In an uncertain environment, proactive institutions are better positioned to compete against a growing list of competitors while strengthening their long-term resilience. For more insights on deposit strategy, register for the ABA’s March webinar, From Rates to Results: Turning Economic Shifts into Strategy, designed for senior financial leaders seeking to move beyond reactive management and toward a more data-driven approach. 

This blog was written with the assistance of ChatGPT, an AI large language model, and was reviewed and revised by the subject-matter expert.

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A 'gray rhino' for financial institution lenders?  

The changing nature of insurance companies' investment portfolios poses emerging credit risk that financial institution lenders should understand and monitor.

This article covers these key topics: 

Insurance: Managing and posing credit risk

For lenders, insurance is one of those necessities we secure upfront and rarely think about—until a crisis hits. Most seasoned bankers have experienced the sinking feeling of seeing a collateral property on the news engulfed in flames or destroyed in a natural disaster, followed by a too-quick drive to the office to confirm that the insurance policy is active and lists us as loss payee.

The fear of loss that’s behind that scramble is one reason why insurance has always mattered in credit. But there’s a quieter, potentially more dangerous insurance-related risk emerging, and it may catch the banking industry unprepared.

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Insurers’ investments: An emerging risk to credit

At its core, insurance is a simple business: collect premiums, invest the funds, and pay claims. Three key risk points in this business model are well known to financial institutions:

  1. Affordability: Rising premiums strain cash flow for small businesses or tenants that have borrowed from a financial institution. The strain can force these borrowers to choose between paying bills or maintaining insurance coverage on the collateral securing a loan.
  2. Coverage gaps: Higher deductibles or new exclusions can make it difficult for insureds to return property to operational status after a loss, leaving repayment at risk.
  3. Dropped policies: Some insureds or insurers may cancel coverage entirely, the latter even across entire states or regions, posing risk to lenders with insured collateral.

Growing private market investments among insurers

A fourth credit risk related to insurance, however, is a “gray rhino,” the kind of problem that’s right in front of you, but you don’t see it). It’s the changing nature of insurance companies’ investment portfolios.

Like financial institutions, insurers must manage liquidity to meet claims. Unlike banks and credit unions, insurance companies have far more latitude in their investment choices. Increasingly, they’re reaching for yield through private credit and private equity, either directly or via funds.

Private credit investments account for more than 35% of total U.S. insurers’ investments, according to a Nov. 14, 2025, Financial Times article. In addition, private equity investors own 139 insurers as of mid-2025, according to the National Association of Insurance Commissioners (NAIC) Capital Markets Bureau. These investments account for 7.8% of insurers’ total assets in the U.S. While still small, the number is growing.

Riskier, illiquid positions

The contrast with the investment approach of traditional financial institutions is stark. Banks and credit unions have a fiduciary duty to depositors, prioritizing liquidity and stability. Private equity and credit funds, by contrast, prioritize yield, accepting illiquidity and higher risk of investing in nontraditional or alternative assets to achieve investment returns. As these investment strategies permeate insurers’ portfolios, they import vulnerabilities foreign to the conservative world of regulated finance.

The problem intensifies during economic downturns, when illiquid positions become even harder to unwind.

In addition, the recent bankruptcies of Tricolor and First Brands highlight the consequences—potential fraud aside—of weak oversight. Unlike banks and credit unions, private investors often lack the robust monitoring systems designed to prevent such failures.

In short:

  1. Private credit and private equity are risky investments.
  2. They now represent a significant portion of insurers’ portfolios.
  3. Private equity ownership of insurers is rising and could reshape both operations and risk management.

This evolving insurance landscape demands attention from credit risk professionals.

Insurance risk-concentration management planning

As with any concentration in your portfolio, financial institutions need a plan to deal with insurers and related risks to credit. Such a plan could include the following elements:

1. Assess exposure to insurers

Start by understanding your aggregate exposure to various insurers. The data exists; it’s just not always tracked. Begin with your largest relationships and work down the portfolio. Over time, aim to capture exposure data through normal credit events (renewals, new loans, reviews). You can’t control which insurers your clients use, but you can measure concentrations and prepare mitigation strategies.

2. Perform credit analysis as if you were underwriting insurers

Next, conduct financial and ownership reviews of your top insurance counterparties. Retrieve financials via company websites or the SEC’s EDGAR database. Examine 13F-HR filings for insights into investment holdings. Your loan review team—or your best credit analyst—should lead this effort to ensure objectivity and rigor. Monitor insurers on an ongoing basis

Check each insurer’s state-level activity through insurance commission websites and monitor the news for post-event changes in pricing or coverage. Track ownership changes closely—these often precede shifts in risk appetite or investment behavior.

Leverage your front-line intelligence: conversations with customers and insurance agents can surface early warning signs. Insurance may not be the most exciting topic, but it’s becoming one of the more critical.

3. Monitor insurers on an ongoing basis

Check each insurer’s state-level activity through insurance commission websites and monitor the news for post-event changes in pricing or coverage. Track ownership changes closely—these often precede shifts in risk appetite or investment behavior.

Leverage your front-line intelligence conversations with customers and insurance agents can surface early warning signs. Insurance may not be the most exciting topic, but it’s becoming one of the more critical.

4. Understand risks in “non-traditional” insurance

Don’t overlook specialized insurance such as crop coverage, where multi-year events (like droughts) can depress future claims due to declining average yields. Build those scenarios into your stress testing.

5. Have a plan of action

If a major insurer in your concentration pool changes terms, withdraws coverage or exhibits adverse changes in financial position, use your data to act proactively. Reach out to affected customers with potential alternatives rather than waiting for a crisis. Doing so builds trust—and resilience.

Insurance: No “get it and forget it” checkbox for lenders

Insurance cannot be a “get it and forget it” checkbox. It’s an integral component of credit risk management. Understanding your exposure—both macro and micro—can make the difference between a contained issue and a systemic problem.

Now’s the time to start asking the uncomfortable questions before the gray rhino charges.

 

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What are Chinese money laundering networks? 

Executive Summary:

Chinese money laundering networks (CMLNs) pose a high and evolving risk to U.S. financial institutions due to their role as professional money laundering service providers for transnational criminal organizations, including major drug cartels. These networks exploit international currency controls by matching cartel-generated U.S. cash with Chinese nationals seeking to bypass capital outflow restrictions, using mirror transactions, intermediaries (e.g., students and money mules), shell companies, real estate, healthcare-related fraud, and trade-based money laundering to integrate illicit proceeds into the financial system. 

  • Risk Rating: High— driven by the scale of illicit funds, use of legitimate businesses and accounts, and increasing regulatory focus. 
  • Control Impact: Elevated— Reinforce enhanced customer due diligence, risk-based transaction monitoring, targeted typology training, and ongoing alignment with FinCEN advisories regarding Chinese money laundering networks.

The growth of Chinese money laundering networks

Chinese money laundering networks (CMLNs) are emerging as one of the most significant threats to the U.S. financial system through illicit finance. These networks are linked to the movement of billions of dollars in drug trafficking proceeds and other criminal gains. Institutions across the U.S. are increasingly exposed to risk, often unknowingly, as these networks exploit the banking system to launder illicit funds.

A recent advisory from the Financial Crimes Enforcement Network (FinCEN) highlights how Chinese money laundering networks are facilitating drug trade proceeds for powerful organizations like the Sinaloa Cartel, Gulf Cartel, and Cartel de Jalisco Nueva Generacion (CJNG). But the risks go beyond narcotics. These networks are also laundering profits from human trafficking, healthcare fraud, illicit gambling, and even illegal marijuana grow operations across several U.S. states.

As anti-money laundering/countering the financing of terrorism (AML/CFT) professionals evaluate their institution's risk, understanding what Chinese money laundering networks are and how they operate is critical to protecting customers and maintaining compliance.

Understanding Chinese money laundering networks

Chinese money laundering networks are organized groups, often composed of Chinese nationals or former foreign citizens, that act as professional money launderers. They specialize in converting illicit U.S. currency into usable funds through various underground banking methods. These networks capitalize on capital flow restrictions in both Mexico and the People’s Republic of China (PRC):

  • Mexico restricts how much U.S. currency can be deposited in its financial system, which creates a challenge for cartels looking to repatriate profits.
  • China limits how much money citizens can move abroad, making it difficult for individuals to invest in foreign assets legally.

CMLNs offer a mutually beneficial solution: cartels require a means to launder large amounts of U.S. dollars, while wealthy Chinese nationals seek to access those funds to circumvent China’s currency controls.

 

How do Chinese money laundering networks operate?

Although the operations can be complex, a simplified overview reveals a three-step process:

  1. Mirror transactions – U.S. dollars are collected from the cartels. A CMLN associate in Mexico quickly transfers an equivalent amount in pesos to a cartel account, creating the illusion of a legitimate exchange.
  2. Use of intermediaries – Students on visas, money mules, and brokers, many unaware of their involvement in illegal activity, facilitate the movement of funds. Chinese students in the U.S. are often targeted due to employment restrictions and financial need.
  3. U.S. cash to China – CMLNs sell the dollars to Chinese nationals via social platforms. Buyers transfer renminbi to Chinese-based operators, paying a premium. The U.S.-based network then delivers the cash to the buyer locally.

Throughout this process, the networks exploit shell companies, straw buyers, real estate, luxury goods, and in some cases, trade-based money laundering (TBML). In New York, adult day care centers have been connected to healthcare fraud and CMLN activity, while grow house operations tied to CMLNs have been discovered in states from California to Maine.

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FinCEN Financial Trend Analysis

In August 2025, FinCEN published a Financial Trend Analysis (FTA) assessing SAR filings related to suspected CMLN activity between 2020 and 2024. The majority of the filings were submitted by financial institutions, with others filed by Money Service Businesses (MSBs), casinos, security firms, insurance companies, and other entities, totaling approximately $312 billion in suspicious activity. The analysis identified or confirmed the following activity:

  • CMLNs use U.S.-based Chinese nationals to perform cash deposits, often with an unknown source of funds. The funds are generally debited through same-day transfers to internal or external accounts.
  • CMLNs use TBML to facilitate funds movement. Funds are deposited from various entities using different methods (e.g., cash, wire transfers, P2P), and they are used to purchase high-end luxury goods or to pay down large credit card balances.
  • CMLNs recruit Daigou Buyers. Daigou means “buying on behalf of” and refers to an arrangement in which buyers use messaging platforms to connect Chinese consumers with products. The products are then sold for a profit to replenish accounts.
  • Human Trafficking and Human Smuggling activity was linked to CMLN networks. The activity involved funds movement to businesses typically associated with labor or sex trafficking, such as massage parlors, spas, escort services, and restaurants and bars.
  • CMLNs possibly use adult daycare centers and may also be associated with healthcare fraud, elder abuse, and illicit gaming activity. These filings identified activity involving senior facilities in New York that allegedly defrauded Medicaid, Medicare, and private insurance companies. In addition, the filings noted excessive or unnecessary movements unrelated to typical operational activity.
  • CMLNs facilitate real estate transactions using illicit proceeds. The purchases are often intended to benefit individuals in the PRC who wish to move wealth to the U.S.
  • CMLNs use Chinese students to facilitate financial activities.

 

Key red flags for financial institutions

No single red flag confirms illicit activity, but multiple risk indicators, when combined, should prompt enhanced due diligence. Institutions asking what Chinese money laundering networks are and how to detect them should consider the following red flags:

  • Inconsistent wealth: Chinese nationals depositing large amounts of cash without employment history that supports the volume.
  • Unexplained transfers: Incoming international wires from like-named accounts, inconsistent with the customer’s profile.
  • Unusual real estate purchases: High-value purchases with unclear or unverifiable sources of funds.
  • Suspicious business activity: Business accounts operated by Chinese nationals with little to no expected activity (e.g., no inventory purchases).
  • Geographic mismatches: Rental income or business transactions originating from locations inconsistent with the customer’s operations.
  • Healthcare business risks: Adult day care and home healthcare providers receiving significant Medicare/Medicaid reimbursements and quickly withdrawing funds or transferring them to personal accounts.

AML/CFT programs should also track businesses in the electronics, telecommunications, or luxury goods industries, as these sectors are known to be exploited by CMLNs.

How financial institutions can respond

To reduce exposure to CMLNs, financial institutions must ensure their AML programs are comprehensive, data-driven, and tailored to evolving risks. Institutions should:

  • Implement ongoing transaction monitoring with thresholds appropriate for geographic and business risk.
  • Conduct robust customer due diligence (CDD), especially for international students, cash-intensive businesses, and real estate clients.
  • Train staff to recognize complex money laundering methods, including TBML and underground banking systems.
  • Stay informed of typologies outlined in regulatory alerts, including those from FinCEN and interagency task forces.

Understanding what Chinese money laundering networks are also requires awareness of how legal businesses can be manipulated. Legitimate enterprises can be used as fronts, making it vital for compliance teams to investigate both customer behavior and broader transactional patterns.

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AI resources for bankers

 

Final thoughts

Financial institutions play a critical role in safeguarding U.S. financial system from illicit activity. As cartels and transnational actors increasingly rely on CMLNs to launder funds, financial institutions must remain vigilant to evolving typologies and ensure their staff are equipped to detect suspicious behavior.

By asking what Chinese money laundering networks are and understanding their operations, institutions can enhance their AML/CFT frameworks, minimize regulatory risk, and help stop the flow of funds that support drug trafficking, human exploitation, and organized crime.

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The basics of CUSO partnerships for credit unions

Here’s how forward-thinking credit unions can expand their member business lending with CUSOs by aligning strategy, structure, and compliance with long-term goals.

How CUSOs can improve processes and help grow member business lending

Each year, credit unions deliver significant savings to members through lower interest rates on loans. Member business lending (MBL), however, brings a distinct set of challenges. Whether your credit union is seeking to scale its technology, diversify income streams, or expand product offerings, one increasingly viable and sustainable path forward is to partner with credit union service organizations (CUSOs).

CUSOs provide credit unions with a cost-effective, and innovation-friendly model for growing the MBL portfolio. They allow institutions to preserve their member-first identity while remaining agile in a competitive market.

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The case for CUSO collaboration

CUSOs are uniquely structured entities that are owned by credit unions and exist to provide services that might otherwise be too expensive or complex to develop in-house. Under NCUA regulations, federal credit unions can invest in or loan to CUSOs that primarily serve credit unions and are limited to approved activities such as loan origination, technology services, and financial counseling.

By engaging in collaborative ventures like CUSOs, credit unions can reduce duplication of services and achieve operational efficiencies that directly benefit members.

Credit unions are leveraging CUSOs for a variety of strategic purposes. Some partner with lending-focused CUSOs to expand into new verticals like commercial or indirect lending. Others outsource technology, compliance, or data analytics functions to CUSOs, reducing operational overhead while maintaining control over the member experience. Many CUSOs also offer consultative training to help internal staff become more comfortable with business lending practices.

CUSOs are particularly useful for smaller institutions that might struggle to afford or implement advanced solutions independently. Rather than build a loan origination platform or fraud detection system in-house, for example, a credit union can invest in a CUSO that offers the service and immediately bring the benefits to their members.

 

Aligning CUSO strategy with your credit union’s goals

A successful CUSO strategy starts with clarity. Before making any investment or partnership decisions, credit unions should define their objectives. The end goal might be to offer a smoother member business lending experience, enhance noninterest income, improve digital capabilities, or deliver new products like insurance or wealth management.

Credit unions that partner with CUSOs often begin by identifying gaps in their current service delivery or areas where member demand exceeds institutional capacity. The next step is selecting or forming a CUSO that complements those needs. Next, determine if the scope of the relationship will be ownership, partnership, or full acquisition.

Legal structure, compliance implications, and governance responsibilities should all be considered. According to the NCUA, CUSOs must maintain independent financial records and provide annual reports to both the NCUA and state supervisory authorities if they are federally insured. Ensuring your institution’s internal oversight keeps pace with the partnership is essential for long-term success.

 

Funding and risk oversight

Investment in a CUSO requires thorough due diligence and financial modeling. Institutions looking to grow with CUSOs should develop clear financial projections, understand expected return timelines, and evaluate the impact on balance sheet strength.

Ongoing risk oversight is also key. The NCUA emphasizes that credit unions must monitor CUSO activities to ensure safety and soundness are not compromised. Even minority ownership stakes can pose reputational or regulatory risk if a CUSO fails to meet compliance expectations. To mitigate this, credit unions should implement periodic reviews, audit procedures, and performance benchmarks for any CUSO relationship.

Grow with CUSOs: A scalable model for the future

By choosing to grow with CUSOs, credit unions gain access to industry expertise, advanced technology, and scalable service models that align with both growth and member impact. And because CUSOs are built on collaboration and shared success, they represent the kind of values-driven innovation the credit union movement was founded on.

Whether you’re seeking to expand your loan portfolio, enhance digital experiences, or boost operational efficiency, now is the time to explore how your institution can grow with CUSOs.

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This article covers these key topics: 

AI adoption challenges tied to technology and data

Many financial institutions face AI adoption hurdles because of their legacy core systems, manual workflows, and fragmented data. The right technology partner can help put banks and credit unions on the path to speed and precision.

Systemwide challenges to adopting AI

For some financial institutions, the promises of artificial intelligence (AI) seem unattainable due to practical challenges. Technology and data considerations, such as legacy core systems, manual workflows, and fragmented data environments, often stand in the way.  

How can banks and credit unions overcome tech hurdles to modernize operations and tap the potential of AI to keep pace with customer expectations, increasing fraud risk, and regulatory complexity? Implementing AI isn’t simple, but the right technology partner can help make the path clear and practical. 

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Legacy core systems can challenge AI implementation

Even though most believe AI will have a large impact on banking, banks and credit unions identify a number of barriers to adopting AI, according to a recent Abrigo survey of nearly 300 bankers. While concerns range from compliance to budget to return on investment, nearly a third of bankers identified data quality or data accessibility as adoption obstacles.  

Technology and related issues around data are an understandable challenge for many financial institutions looking to tap AI’s benefits. Many institutions still rely on decades-old core systems. Often such systems weren’t designed to support the data volume or flexibility that modern AI requires. In general, these aging infrastructures often lead to siloed data, limited integration options, and rigid architectures that slow innovation. 

How financial institutions can ease the path to AI: 

  • Look for cloud-based solutions that can integrate with legacy core systems, allowing institutions to modernize incrementally without undergoing a full core replacement. 
  • Seek out platforms that centralize data across departments to improve visibility, reduce duplication, and enable faster, more strategic decisions. Platforms that can share data across lending, credit risk management, balance-sheet management, and compliance simplify the complexity of managing multiple data stacks.  
  • Choose partners with a proven track record of helping community financial institutions navigate modernization in a regulated environment. For example, with more than 2,400 financial institution customers, Abrigo understands the operational and regulatory realities institutions face and helps them modernize without disruption. “Our AI-powered suite is designed to be modular, so banks and credit unions can adopt AI at their own pace, starting where it will have the most impact and scaling over time,” said Ravi Nemalikanti, Abrigo’s Chief Product & Technology Officer. 

Reliance on manual processes hinders AI adoption 

Manual processes are another challenge facing some banks and credit unions looking to benefit from AI technology. Manual workflows limit scalability and delay getting the kinds of strategic insights that AI could generate in real time. The impacts include missed opportunities, increased regulatory risk, and staff tied up with redundant data entry, disjointed compliance tasks, and repetitive reviews instead of strategic thinking or innovation.   

How to clear the roadblock to AI: 

  • Look for automation capabilities in high-impact areas like lending, credit risk review, and fraud detection to streamline operations and reduce the risk of human error. “Lending and financial crime are the areas where banks feel the most friction, and they’re also the best starting points for AI to make a meaningful impact,” Nemalikanti said. 
  • Choose lending solutions that automate data entry, credit analysis, and compliance tracking to improve consistency and save time. 
  • Work with partners that offer expert guidance or advisory support to help assess existing workflows and identify automation opportunities that improve efficiency without adding headcount. Optimizing an existing process can be a good first step toward layering in AI. 

Data quality and accessibility can block AI efforts   

AI models are only as strong as the data behind them. For many financial institutions, years of collecting data in spreadsheets, core extracts, and siloed systems have resulted in fragmented datasets. Those are not only difficult to reconcile on a regular basis but are also nearly impossible to analyze effectively and provide data-driven strategies.  

However, building a data warehouse internally can quickly turn into a complex, expensive endeavor that requires technical expertise many community institutions don’t have. “It takes effort to get data warehousing 100% right,” said Nathan Myers, Vice President of Integration and Client Care at Abrigo. “And unfortunately, in some cases, data that is not 100% right is 0% useful.” 

How a technology partner can help: 

  • Some data platforms eliminate the need for expensive in-house data warehousing and reduce the burden of reconciling inconsistent data across systems. 
  • Solutions like those on the Abrigo platform can naturally improve data accuracy as users engage with them daily. “The benefit of a strong data platform is that it pulls data from a suite of analytical and operational software that actually uses the data every day,” Myers said. “Since users actively and consistently use these tools, the underlying data will naturally be more reliable and accurate without any additional effort required for financial institutions.” 
  • Tools on a unified platform simplify integration and data cleansing, and those that provide user-level access controls and AI-driven reporting make it easier to surface insights and visualize data for measuring performance and risk.  
  • Some technology partners can provide strategic guidance on how to build a clean, actionable data foundation. Abrigo, for example, has decades of experience helping banks and credit unions with changes that require data integrity such as: 
  1. Adopting the current expected credit loss (CECL) accounting changes. 
  2. Automating transaction monitoring for anti-money laundering efforts. 
  3. Implementing small business lending data collection requirements under the CFPB’s 1071 rule). 

Building a technology roadmap to AI readiness 

Even with the right tools, many institutions still ask: Where do we begin? “Financial institutions need AI that’s transparent, explainable, and compliant,” Nemalikanti said. “At the same time, leaders worry about disrupting day-to-day workflows or overextending resources with large-scale transformations.” 

How to move from frozen to AI-forward: 

  • Start with a partner that offers readiness assessments to identify gaps in your institution’s infrastructure, processes, and data maturity. Download this AI readiness checklist to prepare for responsible, successful AI implementation.  
  • Look for modular, assistive AI capabilities from a single provider that can deliver early wins—such as AI-generated CECL narratives, fraud case summaries, or SAR narrative suggestions. These solutions align with a phased AI strategy, allowing your institution to test, learn, and scale adoption over time. 
  • Seek partners who can help you prepare for the next wave of innovation. Abrigo, for example, has helped financial institutions for more than 20 years as they adapt to changing needs such as adopting the current expected credit loss model (CECL), offering Paycheck Protection Program (PPP) loans efficiently, and adopting small business lending data collection requirements under the CFPB’s 1071 rule.  

As Nemalikanti explained, the real value of a technology partner like Abrigo comes “from our deep understanding of where our customers spend the most time and what tasks consume them day to day. With that insight, we can embed AI into the repetitive and deterministic areas, freeing our customers to focus their energy on the work that truly drives impact.” 

Address tech complexity and build AI for scale 

With increasing regulatory scrutiny, evolving customer demands, and competitive pressure from all sides, financial institutions can’t afford to wait on adopting AI. But they also can’t afford missteps. 

By addressing legacy technology, simplifying manual processes, and improving data readiness, institutions can set the stage for meaningful innovation. And banks and credit unions don’t have to navigate AI on their own. The right technology partner will help institutions prepare, pilot, and progress in their AI implementation. 

Access Abrigo's AI Hub for solutions, resources, and important information to help your institution.

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5 key trends that are shaping the current state of equipment leasing

Economic conditions, regulatory expectations, and borrower behaviors are reshaping how equipment leasing lenders operate in 2026. Read on to learn how rapid technology shifts and persistent cost pressures are requiring institutions to reevaluate risk strategies, digitize delivery models, and formalize governance frameworks.

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The rising importance of credit quality discipline

With elevated interest rates and sector-specific slowdowns, equipment leasing lenders are focusing more on consistency and transparency in decision-making rather than faster credit approvals. Delinquencies have risen in transportation and small-ticket segments, triggering more intensive monitoring and risk-based segmentation. The Equipment Leasing & Finance Foundation (ELFF) Confidence Index has hovered below the 50-point threshold throughout early 2026, with participants citing concerns about credit quality and borrower demand.

Speed to decision and improved workflows are still important, but lenders are investing in data accuracy, consistency, and visibility to mitigate risk. Institutions that can track portfolio health in real time—by segment, collateral type, and geography—are better equipped to adjust pricing and exposure levels when warning signs emerge.

Embedded finance becomes an operational requirement

Dealer and vendor expectations have shifted. Embedded finance—once viewed as a differentiator—is now expected. Businesses want financing options built directly into the buying process, with minimal disruption or delay.

According to McKinsey & Company, embedded finance enables institutions to offer real-time approvals, increase conversion rates, and strengthen partner relationships. For equipment leasing lenders, this means rethinking legacy processes and adopting flexible digital tools that support automated quoting, instant approvals, and API-based integrations with dealer platforms. Those who maintain manual, disconnected workflows may struggle to remain competitive.

New technologies reshape asset valuation and risk

Electrification, automation, and AI are rapidly changing how equipment is designed, used, and valued. These technologies enhance productivity, but they also introduce uncertainty in terms of collateral value, depreciation, and residual forecasting.

Manufacturers like Caterpillar are advancing intelligent, connected machinery that operates on shorter innovation cycles. This dynamic makes it more difficult to apply traditional residual models, especially for high-tech assets where software updates, battery lifespan, and AI capabilities can significantly affect long-term value.

In response, equipment leasing lenders are refining their asset management strategies. This includes adjusting recovery assumptions, enhancing market data inputs, and offering more flexible lease terms based on usage or performance metrics.

Replacement demand overtakes expansion

Businesses remain cautious about large-scale capital investment, but aging fleets and rising maintenance costs are driving continued demand for equipment replacement. According to the Equipment Leasing & Finance Foundation’s U.S. Economic Outlook, replacement demand is expected to be the primary driver of equipment investment in 2026, while expansion-related investment remains constrained due to high interest rates and economic uncertainty.

The Foundation’s report emphasizes that while total equipment and software investment is expected to grow modestly, most activity will focus on upgrading or maintaining essential equipment, particularly in transportation and construction sectors where productivity and compliance pressures are high.

For equipment leasing lenders, supporting replacement activity means offering lease structures that prioritize flexibility, reliability, and cash flow predictability over long-term growth financing.

Governance and compliance shift from reactive to strategic

The growing use of automated decisioning tools and AI-driven processes has increased regulatory scrutiny. Financial institutions are now expected to demonstrate control over their models, data inputs, and decision outcomes—especially in credit underwriting.

The Office of the Comptroller of the Currency (OCC) outlines expectations for model risk management, while the FDIC emphasizes the reputational and operational risks of weak governance. The NIST AI Risk Management Framework also provides structure for implementing responsible AI systems, including transparency and bias mitigation. Equipment leasing lenders are formalizing governance frameworks to meet these expectations. This includes documented model validation procedures, regular audits of automated workflows, and controls that ensure fairness in credit decisioning.

Moving forward with purpose

In 2026, equipment leasing lenders are expected to deliver speed and automation without compromising risk management, data quality, or regulatory alignment. The current environment demands clear operational priorities: digital integration, credit discipline, and defensible governance.

Institutions that execute well on these fronts will be positioned to serve their borrowers more effectively while maintaining institutional resilience.

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