Recent geopolitical turbulence and market volatility have exposed a fundamental truth: when disruption accelerates, operational visibility becomes competitive advantage. Firms with real-time line of sight across positions, counterparties, and collateral manage client risk proactively.
From our position connecting 150+ institutions processing $6.5 trillion daily, the gap between those firms and those on fragmented systems has never been more visible. The latter discover exposure after damage is done.
Agent lenders and collateral managers are today facing unprecedented complexity: tit-for-tat tariffs and the Middle East conflict driving market volatility, North America operating T+1, Europe’s October 2027 deadline approaching, and all the while beneficial owners benchmarking agent lenders against automated competitors delivering capabilities manual processes cannot match.
Over the last 26 years, including six years as global head of trading services at JP Morgan, I’ve watched operational infrastructure adapt to economic forces, regulatory evolution and technological innovation. But the conversations I’m having with clients confirm we’ve reached an inflection point. 2026 will be the year securities finance firms either build AI-ready and ledger-based infrastructure or accept a permanent competitive disadvantage.
Three converging forces
Regulatory acceleration: North America compressed settlement in May 2024. Europe is set to follow in October 2027 across 24 CSDs, while APAC is moving in the same direction. Regulators globally are signalling further acceleration, creating implementation demands that manual processes cannot absorb.
Client expectations: Beneficial owners are benchmarking capabilities, such as sub-30-minute settlement, real-time collateral optimisation, predictive fail prevention and transparent cost-per-trade analytics. Automated custodians are delivering these as standard, and they are increasingly being treated as baseline requirements.
AI’s competitive divide: Custodians, agent lenders and collateral managers gaining market share are those with data infrastructure that makes AI viable. Models trained on fragmented data produce unreliable outputs, whereas connected automation generates clean, real-time data enables predictive analytics, autonomous optimisation and intelligent exception handling.
What North America revealed
May 2024’s T+1 transition stratified custodians into three cohorts.
Firms with automation infrastructure found go-live unremarkable. Connected data built for T+1 has since become their foundation for AI deployment, enabling intelligence, visibility and predictive capabilities that power their competitive advantage.
Others added headcount, achieving compliance at permanently elevated cost. Staffing grew 15-18%. Those firms cannot extract AI value from manually reconciled data.
A third cohort delayed investment. By the time they recognised automation as non-negotiable, they were implementing under stressed conditions, the most expensive timing.
The lesson? Connected infrastructure delivered competitive capabilities, real-time visibility, automated exception management, and clean data architecture for AI. With Europe’s October 2027 deadline now 18 months away, firms face this same investment decision again, only this time with greater complexity.
Line of sight in volatile markets
Recent volatility has crystallised what forward-thinking operations leaders understand: real-time visibility becomes risk management during disruption.
Operations teams managing securities lending must monitor regulatory interventions with same-day implementation, collateral movements across time zones, recall deadlines colliding across settlement cycles, margin calls, counterparty exposure concentrations, corporate actions spanning currencies.
Legacy infrastructure discovers these sequentially – often after intervention windows close. Automated infrastructure sees situations real-time, aggregates data across markets, flags emerging risks before crystallization.
Securities Finance 3.0: the operating model shift
The industry has evolved beyond pre-trade/post-trade distinctions. What’s emerging: integrated workflows where securities lending, cash management, and collateral management operate as unified lifecycles, automation spans execution through settlement, and real-time data enables AI to improve outcomes continuously.
Even association rebrandings reflect this reality: PASLA’s transformation to Securities Finance Association Asia Pacific (SFAAP) confirms an industry moving from securities lending as standalone activity to integrated discipline spanning equities, fixed income, and collateral.
Firms that understand this and are building Securities Finance 3.0 models gain manifold advantages over their competitors: enterprise liquidity management rather than asset-class silos, predictive operations rather than reactive firefighting, and scalable growth rather than linear headcount expansion.
During my tenure at JP Morgan, we built the Tokenised Collateral Network as a way to improve the settlement friction that often hampers the efficient movement of collateral. It has taken longer than expected, but we are now seeing industry-wide adoption of DLT to facilitate the tokenisation of assets to instantaneously transfer collateral.
With the exchanges and CSDs (e.g. Nasdaq and DTCC) facilitating this from execution to settlement, this will only accelerate. Of course, institutions still need to make the decision about the optimal use of assets, which comes back to the quality of real-time data and AI-powered decision making.
The AI virtuous cycle
The question isn’t whether AI offers value, the business cases are proven. The question is: how do we build the required data infrastructure?
LLMs trained on standardised and real-time post-trade data deliver predictive capability: anticipating settlement stress, identifying collateral opportunities before deadlines, flagging risk concentrations before margin calls.
Leading securities finance firms are already investing in this business-critical infrastructure of tomorrow. They understand the ROI case: it starts delivering immediate P&L benefits – enhanced STP, reduced fails, lower costs. And the future-proofing case: simultaneously creating the foundation AI requires. Firms delaying will find themselves structurally locked out of AI-enabled capabilities that define advantage through 2030.
The question has moved on from whether to act on AI – it’s how to sequence the investment. The firms answering the sequencing question correctly share a common pattern, four compounding stages that transform compliance investment into structural competitive advantage.
- Stage 1 – Automate and reduce cost. Eliminate manual workarounds, achieve high STP rates, and free capital from legacy processes.
- Stage 2 – Invest in connectivity. Deploy the freed capital into clean, real-time, enterprise-wide data infrastructure, the foundation AI requires.
- Stage 3 – Enable AI at scale. With connected data in place, deploy predictive analytics and agentic workflows across the full trade lifecycle.
- Stage 4 – Surface new revenue. AI-powered insight identifies opportunities previously invisible to manual operations, and the cycle begins again.
Each stage funds the next. Firms that complete the cycle operate at a structurally different capability level.
The question isn’t whether to build Securities Finance 3.0 operating models. It’s whether institutions will do so proactively with proven solutions, or reactively under stressed conditions.
Firms answering correctly aren’t just preparing for accelerated settlement, they’re positioning themselves to capitalise on compounding benefits from the virtuous cycle. By leading with connected infrastructure, real-time intelligence, and AI-enabled operations, they are building the foundations that will define competitive advantage in the years to come.