The securities lending market is about to enter a radically transformative phase. Starting January 2, 2026, SEC Rule 10c-1a will mandate daily public reporting of all U.S. securities loan transactions via FINRA’s Securities Lending and Transparency Engine (SLATE). This regulatory initiative, born from Section 984(b) of the Dodd-Frank Act, introduces a new level of transparency with implications far beyond compliance.
The Strategic Impact of SLATE Disclosures
Rule 10c-1a represents a pivotal expansion of the SEC’s market visibility framework. SLATE’s daily census of securities loans echoes the goals of broader initiatives like the Consolidated Audit Trail. For securities lenders, SLATE requires reporting to FINRA, which then provides transaction-level data to democratize access to institutions, as well as to retail investors for the use of a set of new AI tools that can trace and understand market behavior at the loan ticket level.
Advanced practices at certain data vendors now include using artificial intelligence in anticipation of extracting patterns of borrower behavior from SLATE’s public data. When applied thoughtfully, these techniques can convert historical anomalies into foresight, allowing securities lenders to reframe their risk assessments and lending strategies.
Interpretive AI and Behavioral Segmentation
A subset of these practices can be described as “Interpretive AI,” i.e., models trained not just to forecast but to explain. These systems are often organized around empirical classifications of securities borrower activity. Commonly, three behavioral regimes of activity can be identified: the Accumulation, Surveillance, and Opposition Periods.
For example, an Accumulation Period in a security would be revealed to an AI when trading desks add many new loans to sell short against an unwarranted rise in stock price, indicating inventory buildup and a possible market tightening. If the security price continues to rise, SLATE metrics would then show large-scale returns of securities from close-outs of losing short positions.
Scenario training for AI models would reveal how borrowing fees reacted when head traders closed out their loans, thereby informing lenders and their agents how much to lower their fees proactively to minimize returns. Advanced analytics can empower lender consultants to monitor these shifts and advise accordingly for rerating, risk rebalancing, or inventory strategies .
Rethinking Loan Pricing: The Goldilocks Rate
Loan pricing is another area poised for refinement. Some analytic platforms now incorporate what is termed the “Goldilocks Rate,” a pricing midpoint that balances lender revenue goals with borrower retention. Unlike borrower-weighted averages that can distort pricing incentives, these rates are optimized across collateral types, loan tenors, and counterparty risk tiers.
Such rate modeling helps reduce early loan returns and unnecessary repricing events, enhancing both profitability and operational stability.
Interpretive AI for Alignment of Strategic Policies and Tactical Timing
AI tools are now being used not only for prediction but also for compliance alignment and strategic execution. These models can help ensure that lending agents’ trading desks adhere to their clients’ board-level policies — whether those policies prioritize a PUSH, i.e., immediate loan execution for a particular security to maximize daily income; or a HOLD, advocating for deferred execution strategies based on forecasted improvements in lending conditions.
For instance, some models are programmed to delay loan releases until the afternoon or the next trading session if projected inventory buffers suggest stronger demand or pricing power. This enables a more nuanced response to evolving market dynamics, transforming reactive operations into forward-aligned trading tactics.
Moreover, the introduction of SLATE will enable interpretive AI models to attribute the performance of a lending program at the individual loan or ticket level, not just in lender-peer groups. The front-end “attention” weighting of AI models can be used to link the derived strategic rationale for each transaction in a performance attribution analysis to its economic impact, giving lenders an auditable, data-driven view into how and why each loan decision was made. In AI modeling terms, the tensors will be aligned with board intent, agency execution, and beneficiary outcome.
The Potential for Data Trusts
In parallel to regulatory disclosures, there is growing interest in establishing “data trusts.” These are secure, structured repositories for data owners — the security lenders — that maintain not only transaction records but the decision logic behind them. As in other financial sectors, these data trusts are expected to become critical infrastructure for managing long-term continuity amid oversight personnel or strategy shifts.
The SLATE initiative underscores a broader industry movement: from opacity to clarity, from reactive compliance to strategic empowerment. While challenges remain, including model governance and data integration, the road ahead is paved with opportunity.
For practitioners in securities lending, data trusts can be used to shift reports from retrospective reviews to real-time rating and forward-looking strategies. Whether through re-rating guidance, inventory management, or governance alignment, the tools and data will soon exist to treat transparency as a tactical advantage and not just a regulatory box to check.
As this next chapter unfolds, market participants who harness these tools will not only keep pace. They’ll set it.