AI in Factoring and Alternative Commercial Finance: Practical Use Cases, Benefits, Legal Risks, and a Risk-Reduction Playbook
Written by: Christopher Friedman, Partner, Husch Blackwell and Alex McFall, Senior Counsel, Husch Blackwell
Across the last several years, we’ve had a front-row seat to how AI is actually being adopted in alternative commercial finance. Our practice routinely advises commercial factors, supply chain finance providers, other commercial finance companies, and the service providers that support them (technology platforms, servicers, data providers, and outsourced operations teams). That vantage point matters: we see what is working, where implementations stumble, and—most importantly—where legal and operational risk tends to surface in the real world.
The industry’s interest in AI is not theoretical. It’s driven by practical pressure: faster onboarding cycles, tighter margins, higher fraud sophistication, and the need to scale without simply adding headcount. But AI also changes how risk shows up. Some of the risks are familiar (vendor oversight, confidentiality, marketing/communications control, and information security). Some are newer in form—even if not new in concept—such as overreliance on model outputs, “confidently wrong” summaries, and unclear data usage rights.
This article is meant to be a practical guide for factors and their partners: where AI fits today, what benefits we see clients achieving, where legal risk tends to cluster, and how to reduce that risk without killing the business value.
Where AI is actually showing up in factoring workflows
In our experience, the most successful deployments are not “AI everywhere.” They’re narrow implementations that strengthen specific workflows—often by making people faster and more consistent rather than replacing human judgment. When AI is positioned as an assistant to underwriting, operations, and portfolio management, the ROI is typically clearer and the control environment is easier to build.
Origination and onboarding are natural starting points. AI tools can accelerate document intake and help organize the initial file: extracting key fields from applications, invoices, POs, shipping documents, and financial statements; populating internal systems; and generating an initial “deal snapshot” for human review. Where we see companies stumble is when these tools are allowed to drift from “intake support” into unreviewed “decision support.”
Underwriting and portfolio monitoring are where AI can add real signal. Many clients use AI-driven analytics to flag dilution trends, dispute patterns, customer concentration shifts, and payor performance changes earlier than they would be caught in a manual process. Used correctly, these tools can improve consistency of monitoring and reduce the likelihood that warning signals get buried in day-to-day volume.
Fraud detection and receivables validation is another strong fit. The objective is not a perfect fraud “yes/no” machine. It’s better for prioritization. AI does well by surfacing anomalies for review: unusual invoice patterns, inconsistent metadata, potential duplicates, atypical shipping behaviors, and classic payment diversion red flags. The best programs treat AI outputs as “risk indicators,” not proof.
Operations and collections are where we see some of the quickest wins. Call summarization, next-step task creation, knowledge-based assistants that retrieve internal policy and procedure guidance, and exception-handling support for cash application can reduce cycle time and help teams stay in process. Here, the recurring pitfall is letting AI draft external-facing communications without an appropriate review gate by a qualified human being.
Disputes and litigation readiness is a quieter but meaningful use case. AI can help organize documents, extract timelines, and identify gaps early, particularly in recurring dispute categories. But it should not be treated as a fact-finder. If the underlying data is incomplete or the tool is asked to “fill in” a story, errors can compound quickly.
Benefits: what factors are getting out of AI
The benefits we see are consistent across shops of different sizes. AI tends to deliver value in three ways.
First, it reduces friction in high-volume administrative work. For instance, work related to intake, summarization, and exception processing allow teams to focus on judgment calls. Second, it improves consistency by standardizing how information is presented internally (credit memos, monitoring notes, and file summaries). Third, it amplifies weak signals by flagging patterns that are easy to miss across a large portfolio.
Those benefits translate into faster onboarding, fewer operational misses, improved monitoring discipline, and, in some cases, meaningful fraud-loss reduction. The important qualifier is that these outcomes depend on two things: (i) disciplined data handling and (ii) a control framework that treats AI outputs as inputs to a process, not the process itself.
The legal and operational risks that matter most
Confidentiality, data use, and privilege
The single most common “AI problem” we encounter is not an AI problem at all, it’s data handling. If employees paste customer information, payor details, dispute communications, or sensitive financials into unapproved tools, you can create confidentiality issues (including contractual breaches), data retention problems, and, in some contexts, privilege risk.
This is why “which tool are we using?” is not a technical question. It’s a legal and risky question. Enterprise-grade tools can be structured to provide clearer contractual protections around data use, retention, and security. Consumer tools often cannot.
Errors and overreliance (“confidently wrong” outputs)
Generative AI is extremely good at producing readable prose. It is not inherently good at truth. If you ask it to summarize complex files, it may omit key facts, misstate timelines, or present assumptions as conclusions. In factoring, that can show up as misstated aging, incorrect summaries of contract terms, or incomplete dispute narratives.
A practical way to frame this internally is: AI is a first-draft generator. It is not an authority. If your control environment treats it like an authority, you’ll eventually pay for that mistake.
Marketing and communications risk
Many factors operate in a market where trust is central: trust with clients, trust with referral sources, and often trust with bank partners or capital providers. AI can create risk when it is used to generate external-facing statements, product descriptions, or performance claims that are not accurate or not supportable.
This isn’t limited to “advertising.” It includes outreach emails, sales decks, website copy, and even client communications that describe processes (“Our system verifies every invoice” or “We detect fraud in real time”). If you can’t support the claim, don’t let AI write it, at least not without a substantive review.
Bias and decisioning issues
Even when a product is “commercial,” automated systems can create uneven outcomes or rely on proxies that correlate with protected traits or other sensitive characteristics. Whether that becomes a legal issue depends on the product, the customer type, the jurisdiction, and how the tool is used. If AI materially influences approvals, pricing, limits, or declines, the risk posture is different than when AI is used merely to accelerate intake.
The safest operational posture is to assume that any AI system that influences decision outcomes should be testable, explainable internally, and subject to ongoing monitoring.
Third-party vendor risk
Many companies access AI through vendors: onboarding tools, CRM add-ons, fraud solutions, call summarization, and portfolio analytics. That pushes a large portion of your risk into vendor terms and vendor controls. In our experience, the biggest gaps appear in (i) unclear data-use rights, (ii) weak audit/assurance practices, and (iii) inadequate incident response commitments.
If a vendor touches sensitive customer/payor data, treat that vendor as a high-risk vendor regardless of whether the vendor markets itself as “just software.”
Compliance overlay: offers, disclosures, and documentation
AI doesn’t create new legal obligations by itself. But it can magnify compliance risk if it is used in offer flows, document generation, or customer-facing explanations. If your team is using AI to draft term sheets, populate pricing, generate program descriptions, or produce “plain-English” explanations, you need a gate that ensures the output stays consistent with your actual product terms and any applicable state-law requirements.
A practical risk-reduction playbook
Below is the framework we most often recommend. It is intentionally operational. You can scale it up or down depending on your size and complexity.
1) Establish a lightweight AI governance backbone
You do not need an “AI department.” You do need accountability. At minimum, assign (i) a business owner for each AI use case and (ii) a risk owner (often compliance, legal, and/or security) responsible for controls and escalation.
Maintain a simple inventory: what tools you use, what each is used for, what data it touches, and whether it can affect decisions or external communications. That inventory becomes the backbone of training, vendor management, and audit readiness.
2) Tier your use cases by risk
A simple three-tier approach works well:
Tier 1 (low risk): internal drafting and summarizing non-sensitive materials; internal knowledge search.
Tier 2 (medium risk): tools that touch confidential customer/payor data but do not determine outcomes (intake support, monitoring flags, operations support).
Tier 3 (high risk): tools that influence underwriting decisions, pricing, limits, declines, or generate customer-facing communications at scale.
Your controls should increase with the tier.
3) Put guardrails on data
Implement clear rules on what can and cannot be entered into AI tools, and make sure those rules match the tool category. Most programs include: (i) “no sensitive data in consumer AI tools,” (ii) approved enterprise tools with contractual protections, and (iii) access controls/logging so you can verify compliance.
4) Keep a human review gate where it matters
For Tier 2 and Tier 3 uses, require human review before outputs drive external actions. In practice, that often means: no AI-generated customer communication without review; no AI-based flags treated as “proof”; and no changes to payment instructions, UCC actions, or dispute letters without a structured verification step.
5) Validate and monitor that is proportional to risk
You don’t need a complicated validation regime to reduce risk. You do need proof that the tool performs acceptably on your data and your edge cases, and you need ongoing monitoring for drift. Define what “good enough” means, test it on representative samples, and re-test on a schedule or when the workflow changes.
6) Contract for auditability and accountability
Your vendor contracts should clearly address data ownership and permitted use, retention/deletion, security commitments, incident response timelines, subprocessor controls, and what assurance reporting (e.g., SOC reports) you will receive. For higher-risk tools, push for audit rights or a workable substitute.
7) Train your people like it’s a control environment
Most AI incidents are the result of well-meaning employees using tools in the wrong way. Training should focus on practical “dos and don’ts,” verification expectations, what requires escalation, and which tools are approved for which data categories.
Conclusion
AI is already improving speed and signal detection across origination, underwriting support, monitoring, fraud detection, and operations in the factoring industry. The factors who will benefit most are the ones who adopt AI like a finance company and not like a software demo: defined use cases, disciplined data handling, clear human review gates, proportionate validation, and vendor contracts that match the risk.
About the Authors
Christopher Friedman, Partner, Husch Blackwell
Chris is a nationally recognized thought leader in fintech, consumer finance, and alternative commercial finance, serving a go-to legal advisor for companies navigating the complexities of the financial services industry. His insights into the financial services industry and regulatory trends have been featured in Bloomberg, National Mortgage News, Dodd-Frank Update, RESPA News, and other leading publications. Chris is a frequent speaker for the International Factoring Association, the Online Lender’s Alliance, the American Bar Association, and the Conference on Consumer Finance Law, among other trade groups. He also brings a wealth of experience addressing litigation risk and is the co-author of the “Settlement” chapter of the American Bar Association’s Class Action Strategy and Practice Guide, as well as the former co-editor-in-chief of the ABA’s Class Action and Derivative Suits Newsletter.
Chris represents a diverse range of clients, including fintech-based small business finance companies, commercial factors, reverse factors, supply chain finance companies, purchase-order finance companies, and embedded finance companies. He also represents consumer fintech companies, buy-now-pay-later companies, bank and non-bank consumer lenders, and service providers. With deep industry knowledge and a business-first approach, Chris helps clients—from startups to established institutions—structure and scale their operations while ensuring compliance with a quickly-evolving state and federal legal landscape.
Alex McFall, Senior Counsel, Husch Blackwell
Alex has built a reputation for delivering comprehensive and sophisticated representation to a diverse range of clients, including banks, credit unions, fintechs, mortgage companies, and commercial and consumer finance companies. With a multifaceted skill set encompassing regulatory compliance, licensing, and litigation, Alex consistently achieves exceptional outcomes in high-stakes disputes and provides strategic counsel on intricate regulatory matters.
Alex is a trusted advisor to financial services clients operating in a heavily regulated landscape. She possesses a deep understanding of the industry’s complexities, allowing her to guide clients through various challenges, particularly in areas such as new product launches, money transmission, commercial finance, unsecured consumer lending, retail installment sales, and comprehensive regulatory compliance and licensing issues.
One of Alex’s areas of knowledge lies in advising alternative commercial finance companies, offering invaluable guidance on successfully navigating the ever-evolving disclosure and licensing regulations. Her knowledge of these intricate frameworks empowers clients to stay on top of regulatory changes, mitigating risks and ensuring compliance while minimizing the burden on their operations.
The views expressed in the Commercial Factor website are those of the authors and do not necessarily represent the views of, and should not be attributed to, the International Factoring Association.