The Landscape of FPL Programs

Navigating the regulatory landscape of fully-paid securities lending (FPL) programs requires broker-dealers to adhere to a framework of rules centered on customer protection and transparency. These programs offer retail investors a way to generate extra income by lending their securities, which brokers then re-lend to other market participants, often to settle trades or facilitate short sales. The rapid expansion of these retail-focused programs has led to increased regulatory oversight.

Core Regulatory Obligations

At the core of a broker-dealer’s obligations is FINRA Rule 4330,^[1] which governs the borrowing of fully paid or excess-margin securities from customers. A compliant FPL program must be built upon the foundations of Rule 15c3-3 under the Securities Exchange Act^[2], the “customer protection rule,” a requirement explicitly stated within Rule 4330. This involves ensuring correct customer and non-customer classifications, proper custody and segregation of assets, accurate reserve computations, and adherence to the specific borrowing provisions of 15c3-3(b)(3).

Establishing an FPL Program: Notice, Agreements, and Controls

Before a firm can launch an FPL program, it must provide FINRA with at least 30 days’ advance notice of its intention to first borrow securities. The firm is also obligated to supply any information FINRA requests to assess compliance with both its own rules and federal regulations. Furthermore, it is essential to design comprehensive written agreements and operational controls. While Rule 4330 focuses on required disclosures, programs typically use a Master Securities Lending Agreement (MSLA) tailored for retail clients. This agreement codifies key terms such as collateral, recall procedures, rate setting, compensation splits, and tax implications. A retail-oriented MSLA often includes a condition that enrollment is subject to an appropriateness determination and requires the customer to acknowledge the set of risk disclosures.

Customer Onboarding: Appropriateness and Risk Disclosures

Prior to borrowing from a new customer for the first time, a firm has two key obligations. First, it must determine that the loan is appropriate for that specific customer. This requires “reasonable diligence” to understand the customer’s financial situation, tax status, investment objectives, liquidity needs, and risk tolerance, among other relevant factors. This review must be documented. In cases involving introduced accounts, the carrying and borrowing firm can rely on the introducing broker’s assessment, but the ultimate responsibility for the appropriateness determination remains with the borrowing firm. For customers who qualify for an “institutional account” under FINRA Rule 4512(c)^[3], this appropriateness obligation can be met by following the institutional-suitability framework.

Second, the firm must provide “clear and prominent” written risk disclosures before the initial borrow. These disclosures must include a bold notice that SIPA may not protect the loan transaction and that collateral might be the only recourse if the firm fails to return the securities. The customer must also be informed about the temporary loss of voting rights, the process for selling or recalling securities, how all parties are compensated, how loan rates are set and can change, the types of collateral and associated risks, the potential use of their securities to satisfy short-sale delivery obligations, and tax implications like payments-in-lieu of dividends (PIL). Firms must keep records documenting the delivery and completion of these disclosures. Well-managed programs often reinforce these points in FAQs and on program microsites, explicitly explaining risks such as those associated with PIL versus qualified dividends, the loss of voting rights, and the mechanics of recall.

Ongoing Operational and Supervisory Duties

Ongoing operational duties require firms to supervise their FPL program as they would any other product line. This involves maintaining a robust supervisory system and written supervisory procedures (WSPs) designed to ensure compliance with rules like FINRA Rule 3110.^[4] All customer communications must be fair and balanced, adhering to FINRA Rule 2210^[5], and firms must uphold high standards of commercial honor as mandated by Rule 2010.

Recent FINRA enforcement actions have highlighted that practices such as auto-enrollment without individual appropriateness reviews, misrepresenting customer compensation, and providing weak disclosures on tax impacts can lead to sanctions.^[6] To avoid such pitfalls, firms must maintain a clear paper trail, creating and retaining records of appropriateness determinations and disclosures in accordance with Exchange Act Rule 17a-4.^[7]

Operationally, systems must be in place to honor customer rights, such as processing recalls and sales from the supplemental account holding the loaned shares. Customer-facing dashboards and statements should provide transparency into loan rates, collateral, and accrued interest. It is also crucial that collateralization and custody mechanics, including the use of omnibus collateral accounts, are managed in strict accordance with Rule 15c3-3. Some MSLAs explicitly detail third-party custody and sub-ledgering arrangements to protect the beneficial interests of the lender.

Evolving Regulatory Landscape: SEC Rule 10c-1a and FINRA SLATE

The regulatory environment is set to become even more transparent with the advent of the Securities Exchange Act Rule 10c-1a^[8] and FINRA’s SLATE (Rule 6500 Series).^[9] The SEC rule mandates the reporting of securities loans to a registered national securities association, a role FINRA will fill with its SLATE facility. Starting September 28, 2026^[10], brokers will be required to report detailed, time-stamped loan data—including security identifiers, loan amounts, collateral types, and rates—on the same day for most loans. This new layer of transparency is significant for Rule 4330 compliance because the public availability of granular loan data will make any outliers in customer compensation immediately obvious. If a firm’s compensation to retail clients consistently deviates from market rates for the same security, it will raise red flags. This heightened transparency effectively raises the standard for a firm’s disclosures, its appropriateness rationale, and the “fair presentation” required under other FINRA rules.

Best Practices for Compliance and Avoiding Pitfalls

To avoid common pitfalls cited by examiners, firms should prohibit auto-enrollment at account opening and instead require a documented, opt-in appropriateness review. Marketing materials, client agreements, and actual payouts must be aligned to avoid misstating compensation; firms must clearly disclose how rates are set and what portion of the fee they retain. Tax implications, particularly the difference between PIL and qualified dividends, must be explained in plain English, potentially with event-driven alerts near dividend dates. Finally, firms must address weak supervision by embedding compliant record repositories and supervisory alerts to track approvals, disclosures, and changes to payouts.

Explanatory AI for Loan Management in FPL Programs

For fully paid lending (FPL) programs that utilize AI models for managing loan allocation, rate setting, or recall decisions, the integration of explanatory AI methods like SHAP (SHapley Additive exPlanations)^[11] or LIME (Local Interpretable Model-Agnostic Explanations)^[12] can significantly enhance compliance and build client trust. These techniques offer clear, measurable insights into what influences a model’s outputs, such as the loan rate of a particular security or the reasons behind prioritizing a recall. This aligns directly with FINRA Rule 4330’s requirements for appropriateness determinations and risk disclosures. Firms can document and demonstrate the rationale for decisions on a customer-by-customer basis, even when those decisions are made algorithmically. The ability to translate complex model reasoning into plain-language justifications also supports internal supervisory reviews and regulator inquiries, ensuring that automation enhances, rather than obscures, fiduciary responsibilities.

Distributed Ledger Technologies for Customer Rights and Operational Integrity

Integrating distributed ledger technology (DLT) into operational systems that protect customer rights, such as processing recalls or sales from a supplemental account holding loaned shares, provides clear benefits in terms of transparency, auditability, and speed. By recording loan origination, collateral movements, recall requests, and settlement events on a shared, permissioned ledger, all stakeholders (lenders, brokers, custodians, and regulators) gain access to an immutable, time-stamped record of activity. This reduces operational disputes, ensures that recall instructions are executed in strict sequence, and provides a verifiable trail for regulatory examinations. In the upcoming SLATE regime, which will make loan-level reporting public and open to competitive evaluation, DLT can act as the “single source of truth.” This means it can reconcile internal records with the reported data, reducing discrepancies and enhancing both operational efficiency and customer trust.

The Principles of Rule 4330 and Future Preparedness

In essence, Rule 4330 is built on the principles of individualized appropriateness, clear risk disclosure, and verifiable supervision, with Exchange Act Rule 15c3-3 providing the foundational custody requirements. The upcoming transparency from SLATE will make these principles more enforceable than ever, ensuring firms that fall short on fairness will be easily identified. Preparing for this future means leveraging technologies that directly support these core tenets. Explanatory AI can provide the auditable rationale for algorithmic decisions to satisfy appropriateness and disclosure obligations, while DLT offers an immutable record ideal for verifiable supervision. Therefore, it is imperative for firms to align not just their agreements and workflows but also their technological infrastructure, embedding these tools to meet heightened expectations before the new data reporting requirements take effect.

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[1] https://www.finra.org/rules-guidance/rulebooks/finra-rules/4330

[2] 17 CFR 240.15c3-3

[3] For purposes of rule 4512, the term “institutional account” means the account of:

1. a bank, savings and loan association, insurance company or registered investment company;
2. an investment adviser registered either with the SEC under Section 203 of the Investment Advisers Act or with a state securities commission (or any agency or office performing like functions); or
3. any other person (whether a natural person, corporation, partnership, trust or otherwise) with total assets of at least $50 million.

[4] https://www.finra.org/rules-guidance/rulebooks/finra-rules/3110

[5] https://www.finra.org/rules-guidance/rulebooks/finra-rules/2210

[6] https://www.finra.org/media-center/newsreleases/2025/finra-fines-apex-clearing-32-million-violations-relating-full

[7] 17 CFR 240.17a-4

[8] 17 CFR Part 240 [Release No. 34-98737; File No. S7-18-21] RIN 3235-AN01 https://www.federalregister.gov/d/2023-23052

[9] https://www.finra.org/rules-guidance/rulebooks/finra-rules/6500

[10] https://www.federalregister.gov/d/2025-14459

[11] SHAP (SHapley Additive exPlanations) is an explanatory AI method that provides insights into what influences a model’s outputs. It helps to understand the rationale for decisions made by AI models, such as loan rates or recall priorities in fully paid lending programs.

[12] LIME (Local Interpretable Model-Agnostic Explanations) is an explanatory AI method. It provides clear, measurable insights into what influences a model’s outputs. It can be used in fully paid lending (FPL) programs that utilize AI models for managing loan allocation, rate setting, or recall decisions to enhance compliance and build client trust.