Portfolio management in 2025 feels different. Markets are more interconnected, fee pressure from passive strategies keeps margins tight, and firms face heavier compliance and disclosure expectations. At the same time, data and AI tools are finally mature enough to do the heavy lifting—helping teams control risk, cut operational waste, and run efficient strategies without taking extra market risk.
This article walks through what “efficient portfolio management” actually looks like today: practical EPM techniques like derivatives and securities lending, the guardrails regulators and auditors expect, and the AI-powered levers that can reduce manual work, lower total expense ratios, and improve trade execution. You’ll get the tradeoffs up front—where efficiency wins can come at the cost of complexity if governance isn’t tight—and a clear, 90‑day roadmap for making efficiency repeatable and audit‑ready.
If you’re responsible for operations, risk, or portfolio construction, this piece is for you. Expect concrete examples (hedge sizing, collateral standards, liquidity checks), pragmatic AI use-cases (research co‑pilots, automated TCA, stress-testing), and the policies and controls you must have in place so efficiency actually benefits the fund and its investors.
Read on to learn how to tighten costs and risk together—without shortcuts that create regulatory or model risk—and to find a practical pathway from pilot projects to firmwide EPM that withstands an audit.
What efficient portfolio management means today (and what UCITS calls EPM)
Core aims: reduce risk, reduce costs, or generate extra income without raising the fund’s risk level
Efficient portfolio management is a pragmatic set of practices whose objective is simple: deliver the fund’s stated investment outcome while improving economic effectiveness. That can mean lowering unintended risk (through hedges or better diversification), lowering running costs (by improving execution and operational workflows), or generating additional, non‑material sources of income (for example through short‑term lending or optimized cash management). Crucially, any efficiency move must preserve the fund’s risk profile and investment objective — efficiency is an enabler, not a replacement, of the mandate the manager sold to investors.
Techniques: financial derivatives for hedging/efficient exposure, securities lending, repos/reverse repos, total return swaps (TRS)
Managers use a toolkit of market instruments to implement efficiency goals. Derivatives (futures, options, swaps) allow precise hedging and can create exposure more cheaply or quickly than trading the underlying. Securities lending and repurchase agreements (repos) convert idle holdings or cash into incremental revenue or liquidity. Total return swaps and similar contracts let a manager synthetically gain or shed exposure without immediate changes to the fund’s holdings. Each technique can lower transaction costs, improve tracking or offer temporary financing, but all require robust operational infrastructure and clear policy guardrails.
Risk controls: global exposure (VaR/commitment), leverage limits, liquidity, concentration, counterparty risk
Efficiency tools introduce trade‑offs that must be controlled. Managers quantify and limit aggregate market exposure using commitment or value‑at‑risk approaches, enforce explicit leverage ceilings, and monitor liquidity to ensure the fund can meet redemptions in stressed conditions. Concentration limits protect against issuer or sector squeezes, while counterparty risk frameworks (credit limits, diversification, collateralization) reduce the chance that a partner’s failure translates into losses for the fund. Effective control combines quantitative limits with frequent reporting and clearly assigned escalation paths.
Collateral standards: quality, haircuts, liquidity, re-use limits; revenues from EPM must benefit the fund, with clear disclosures
When portfolios use lending, repos or swaps, collateral becomes the operational and legal backbone. Good practice requires high‑quality, liquid collateral, conservative haircut policies, and rules on rehypothecation or reuse. Collateral pools should be actively monitored for concentration and liquidity shifts. Equally important are commercial and governance rules: any incremental revenue earned through efficient portfolio management must be allocated to the fund (not absorbed by the manager) and disclosed to investors in clear, auditable documentation. Transparency and recordkeeping — from trade confirmations to collateral movements — make efficiency both effective and defensible.
Those building or reviewing an EPM programme must therefore balance the upside of lower cost and incremental income with strict operational controls and investor transparency. In practice that balance is enforced through policy, systems and periodic review — a structure that allows managers to pursue efficiency while preserving investor trust. With these foundations in place, it becomes possible to address why efficiency has become urgent for managers operating in today’s competitive and dispersed markets, and what levers can be pulled to respond.
Why efficiency is urgent: fee compression, passive flows, and valuation dispersion
Fees under pressure: passive funds and scale players squeeze active management economics
Competition from large-scale index providers and low-cost platforms has compressed margins across active management. As scale players lower headline fees, active managers face a twofold challenge: defend returns net of fees for clients, and extract enough margin to cover distribution and operational costs. That dynamic forces managers to find productivity gains or alternative revenue sources that don’t undermine the fund’s stated risk‑return profile.
Growth constraints: AUM up, but revenue and margin expansion lag (distribution and product mix matter)
Assets under management can grow while economics stagnate if growth is concentrated in lower‑fee products or if distribution costs rise faster than net revenues. Successful firms focus on product mix, distribution efficiency and unit economics: shifting flows toward higher‑value strategies, reducing per‑account servicing costs, and automating routine workflows are the practical levers that protect margins as AUM scales.
Volatility and dispersion: higher P/E vs history, uneven markets raise the bar for risk and cost discipline
“The current forward P/E ratio for the S&P 500 stands at approximately 23, well above the historical average of 18.1, suggesting the market may be overvalued; combined with high-debt environments and increasing dispersion across stocks and sectors, this raises the bar for risk and cost discipline.” Investment Services Industry Challenges & AI-Powered Solutions — D-LAB research
Higher valuation multiples and greater cross‑sectional dispersion mean managers must be more selective and cost‑conscious: a single large drawdown or messy execution can wipe out fee‑era gains. In practice that translates into tighter risk budgets, lower turnover where appropriate, smarter use of derivatives for targeted exposures, and rigorous transaction‑cost analysis to protect performance after fees.
Together, fee pressure, distribution realities and a more demanding market environment make efficiency not just a nice‑to‑have but a competitive necessity. That reality is why managers are now pairing classical EPM techniques with new technology—so they can both defend margins and improve investor outcomes without changing the fund’s mandate. In the next part we look at how modern tools accelerate those efficiency levers and where to start piloting them.
AI-powered levers that make portfolio management efficient
Advisor co-pilot: research summarization, rebalancing drafts, compliance checks (≈50% lower cost/account; 10–15 hours/week saved)
AI co‑pilots augment portfolio teams by automating information synthesis, drafting rebalancing trades, running pre‑trade compliance checks and preparing client communications. That reduces manual research time, speeds decision cycles and lowers per‑account servicing costs—freeing portfolio managers and advisors to focus on judgmental tasks that require human oversight.
“50% reduction in cost per account (Lindsey Wilkinson).” Investment Services Industry Challenges & AI-Powered Solutions — D-LAB research
“10-15 hours saved per week by financial advisors (Joyce Moullakis).” Investment Services Industry Challenges & AI-Powered Solutions — D-LAB research
Risk and liquidity intelligence: early warnings, stress tests, hedge selection, collateral optimization within UCITS/EPM rules
Machine learning and scenario engines pull together market, position and funding data to generate early‑warning signals and automated stress tests. These tools can recommend hedge candidates, quantify collateral impacts under different shocks, and score portfolio liquidity in near real time — all while keeping decisions constrained to policy limits such as global exposure and collateral quality standards.
Execution efficiency: best-ex analytics, slippage and turnover reduction, derivative hedge sizing, automated TCA
Execution‑focused AI reduces cost leakage by selecting venues, timing trades and sizing orders to minimize market impact. Algorithms that combine historical slippage, current orderbook state and broker performance can lower turnover and refine derivative hedge sizing. Automated transaction‑cost analysis (TCA) feeds back into the investment process so actions are continuously improved and justifiable in audit trails.
Client-at-scale: personalized reports and education (higher engagement, lower churn), automated meetings and inquiries
GenAI scales investor servicing: hyper‑personalized reporting, automated meeting summaries and intelligent chat interfaces answer routine queries and surface portfolio insights. The result is higher client engagement at lower incremental cost, better retention metrics and a more consistent investor experience across large client bases.
Combined, these levers let managers cut operating expenses, protect net returns and deliver differentiated client experiences without changing the fund’s stated mandate. The next step is ensuring these capabilities are deployed inside a governance framework that preserves auditability, model discipline and regulatory compliance.
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Governance that keeps EPM safe and audit‑ready
Model risk: backtesting, drift monitoring, human-in-the-loop, explainability for investment and risk models
Models that drive hedges, liquidity scoring or automated trade suggestions must sit inside a formal model‑risk framework: documented purpose and assumptions, independent validation and regular backtesting, continuous performance and drift monitoring, and clear escalation routes when outputs deviate from expectations. Supervisory guidance emphasises independent model validation and lifecycle controls — including human‑in‑the‑loop checkpoints for material decisions — so results are auditable and defensible (see Federal Reserve SR 11‑7 on model risk management: https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm).
Cyber and data controls: ISO 27002, SOC 2, NIST 2.0; golden data sources, lineage, entitlements, and audit trails
Strong EPM requires the same information‑security and data governance rigour as any critical financial process. Adopt recognised frameworks (ISO/IEC 27002 for controls: https://www.iso.org/standard/54533.html; SOC 2 principles for service controls: https://www.aicpa.org/interestareas/frc/assuranceadvisoryservices/soc2report.html; and NIST Cybersecurity Framework guidance: https://www.nist.gov/cyberframework) to design access, encryption, monitoring and incident response.
Operationally, that means establishing a single “golden” source for positions, prices and collateral; maintaining automated lineage so every P&L or risk number traces back to inputs; enforcing least‑privilege entitlements for trade and data workflows; and keeping immutable audit trails for trades, collateral flows and model decisions so internal and external audits can reconstruct events end‑to‑end.
Policy hygiene: EPM revenues accrue to the fund, SFT/TRS disclosures, counterparty limits, leverage caps, prospectus alignment
Clear written policy prevents legal, reputational and regulatory problems. Policies should codify where EPM fits the fund’s mandate, require that any incremental revenues from securities‑lending, repos or TRS accrue to the fund (and be documented), and mandate required disclosures. In the EU context, securities‑financing and reuse rules and reporting requirements (see ESMA on SFTR) must be reflected in procedures and reporting: https://www.esma.europa.eu/policy-rules/post-trading/sftr.
Policy hygiene also sets quantitative guardrails (counterparty credit limits, collateral quality and haircut schedules, aggregate leverage caps and concentration thresholds) and ties them to prospectus disclosures and investor communications. Governance should require periodic policy review, board or risk‑committee sign‑off for material changes, and pre‑deployment legal and compliance checks for new EPM tactics.
Finally, integrate governance into everyday operations: automated checks that block out‑of‑policy trades, centralised dashboards for real‑time compliance monitoring, and runbooks for stressed liquidity or counterparty events. Those processes make EPM not only efficient but auditable and resilient — essential before scaling pilots into production and rolling improvements into client reporting.
A 90‑day plan to operationalize efficient portfolio management
Days 0–30: EPM audit, baselines and bottleneck mapping
Start with a short, focused audit: catalogue all instruments and SFTs in scope (derivatives, securities lending, repos, TRS), document collateral practices and identify legal/operational owners. Capture baseline performance and cost metrics (transaction‑costs, turnover, realized tracking error, and a simple measure of market exposure such as commitment or VaR) so future improvements are measurable. Map every data feed and report used for trading, risk and investor communications; highlight single points of failure, manual workarounds and reconciliation gaps. Finish the phase with a prioritized list of quick wins (data fixes, a blocked policy gap, or an execution change) and a clear sprint plan for the pilot phase.
Days 31–60: pilot co‑pilot workflows, automate ingestion, deploy playbooks and backtests
Run narrow pilots that prove value without risking the whole fund. Deploy an advisor co‑pilot on a small sample of accounts to automate research summaries, draft rebalances and run pre‑trade compliance checks. Automate ingestion for the highest‑value datasets (positions, prices, collateral, trade blotters) and connect them to risk and execution analytics. Institute hedge and liquidity playbooks for common scenarios and backtest them against historical intraday or trade data to compare slippage and risk outcomes. Ensure pilots include: automated TCA, a simple model‑validation loop, and daily exception reporting to compliance. Use pilot results to refine controls, cost‑benefit assumptions and the rollout checklist.
Days 61–90: scale operations, codify policy and track KPIs
Move winning pilots into production and scale them across strategies and client segments. Codify EPM policies — revenue allocation, counterparty limits, collateral standards, leverage and disclosure rules — and secure required signoffs. Build central dashboards that show the new baseline and improvement trends for core KPIs (TER, turnover, TCA/slippage, aggregate exposure, collateral quality and short‑term liquidity). Train front‑office, operations and compliance teams on new workflows, and formalise change control and incident runbooks. Close the 90 days with a governance pack for senior management that includes measured impact, residual risks, and a phased roadmap for further automation or product expansion.
Delivering measurable efficiency in 90 days hinges on disciplined scope, rapid automation of critical data flows, tightly scoped pilots and clear governance — together these elements turn one‑off experiments into repeatable, auditable improvements ready for broader adoption.