The New Era of Balance Sheet Optimization in Investment Banking

Operational change is in the air

Balance sheet optimization for sell side institutions is fundamentally aimed at enhancing capital efficiency and managing systemic liquidity risk. Since we are in the midst of tectonic transformation of the industry’s foundational operating models, it has become paramount for treasurers to maintain balance sheet discipline. These are just a few recent trends exerting pressure on banking operations to adapt and modernize.

Structural market and technological shifts

• Digital assets. US policy via the GENIUS Act has cleared the path for the mainstreaming of stablecoins and tokenization of real-world assets.
• AI agents. Banks are actively deploying agents across the entirety of operations; including looking for aberrations in settlements and automating client service, responding to client inquiries (like unsettled shares) immediately, and reducing the wait time compared with traditional human client service representatives.
• Faster affirmations and real-time intelligence. Sell side clients are asking for real-time cash positions and real-time settlement workflows.
• Standard Settlement Instructions (SSIs). Banks are pushing buy side firms to add SSIs to trades immediately, often requiring allocations. The goal is automatic SSIs, similar to the automated checkout experience of major e-commerce platforms.
• Blockchain-enabled intraday collateral movements. With volatility in bank balance sheets, all day, every day, and certainly at the end of quarters, banks need to ensure liquidity until they get a transaction completed, especially when dealing in $20 billion to $30 billion sticky transactions.
• Repo markets are moving toward central clearing. This will send data volumes soaring via intraday margin data, collateral movement data, and stress and exception data.
• Massive US banking consolidation is on the horizon. We could potentially see shrinking from 4,500 banks to as few as 1,000 in three or four years.

Modern treasury operations must remain nimble to swiftly adapt to change. The best middle-office platforms enable more tailored and efficient collateral management by supporting the configuration of settlement cycles for transfers of different securities. Another structural market force that has made operational change necessary is the increasing exposure to private market asset classes. A key element of balance sheet management, especially for firms dealing with derivatives and securities financing (which may include hedging or exposure management related to private assets) is liquidity preparedness to meet margin and collateral calls.^iv

Managing private markets exposure

Stout balance sheet optimization is critical when tying up capital in opaque private market asset classes. Regulations have empowered banks to regain market share in the private asset markets, another clarion call to pay tech debts.

Recently, the private market systemic risk debate has gathered vocal momentum. US banks have exposure to $300 billion in loans (total is $1.2 trillion when including hedge funds, private equity firms, and pension funds) while the collapses of subprime auto lender Tricolor Holdings and auto-parts supplier First Brands Group sent up red flags.^v Balance sheet optimization protects sell side firms against private market exposure by addressing the illiquidity and operational complexity inherent in these assets, preventing the balance sheet from becoming "dead-ended," and managing the capital charges associated with these investments.

Guard against dead-ending balance sheets with data management

Dead-ending the balance sheet occurs if a bank lends against a security it cannot rehypothecate in some way, shape, or form. Treasury cannot use it in the repo marketplace, lend it out for funding; it can't use it for the purposes of funding the balance sheet. This is the polar opposite of balance sheet efficiency, reducing the ability to execute new, revenue-generating positions without expanding total assets.

This golden source of data is key to ensuring information is accurate across every asset class, dataset in any time zone, currency, etc. Such airtight data management enables greater transparency into client inventory, as well as centralized margining and collateral tools to optimize liquidity, allowing for automated call calculations that give you the agility to rehypothecate collateral.

Optimized funding strategies for capital efficiency

A key component of balance sheet optimization is collateral optimization: funding a bank’s trading book in the most dynamic, opportunistic manner. This is an especially desirable value proposition in a time when everything is accelerating, including funding markets, intraday repo, and settlement times.

A treasury team that can swiftly, and at a given point in time, grab precise collateral inventory and valuation data will be able to deftly conduct a liquidity/cost analysis of all the pools of eligible collateral to determine which will yield the most benefit for funding. Since funding markets are fluid, different assets at different periods of time are easier to fund than others. Knowing the score means that treasury can then choose the right funding execution, whether it be repo, securities lending, or posting as margin on derivatives. A bank’s collateral-optimized funding makes it more resilient and compliant, while cranking up capital efficiency.

Better data management for ABF

Asset-based finance (ABF) strategies are dominating the private debt space right now, as 16 of 23 major North American private credit managers now have dedicated ABF capabilities. As a recent ABF Journal article noted, “Investment banks must understand how standard versus bespoke documentation affects transaction execution timelines and financing certainty.”^vi ABF is edging direct lending from its private credit mantle because of its pools of mortgages, credit card receivables, and auto loans backed by real collateral.

However, ABF strategies come with pools of tens or hundreds of thousands of small loans, each that come with hard-to-ingest loan tapes of borrower data including FICO scores, geographic location, risk grade, principal amount, interest rate, maturity, etc. These loan tape spreadsheets are not standardized, with a multitude of different formats from the different originators, the servicers, or the agent banks that hold these securities. In addition to managing loan origination data, balance sheet optimization for ABF is reliant on middle office systems that can track structures and unstructured collateral valuation as well as residual value data and servicing and performance data.

For ABF, valuation is slower and less standardized, making it harder to mark-to-model or mark-to-market consistently. Institutions may therefore hold more capital conservatively, reducing balance sheet efficiency. But those that normalize unstructured data and execute precise, transparent valuations for ABF can put that capital to work.

Data is the hidden driver of capital efficiency

Banks are on a quest for superior balance sheet optimization to prevent such strategies from becoming a detriment to capital efficiency. Poor data quality acts as a choke point, delaying settlement, sometimes stretching transactions to 7, 10, or even 20 days. Delayed settlement means banks cannot charge financing until the transactions are settled.

Nobody, whether buyer, bank, or regulator, wants to work under a cloud of unsettled securities. Banks and prime brokers with automated data quality and governance mechanisms and ready-to-use data models and pipelines can instill trust in data integrity across functions. Treasury managers then can have transparency into cash balances and liquidity requirements to stay flexible amid fluctuating markets and increased regulations.