In the high-volume, fast-moving environment of securities lending, operational timing is everything. Among the most common friction points is the misalignment between the return of loaned securities and the delivery of client-sold assets, which can cause settlement fails, regulatory exposure, and reputational risk. To address this, a leading Agent Lender partnered with Fairman Consulting to design and implement a transit account (also known as a “wash account”)—an elegant, st

In the intricate world of securities lending, timely and accurate recalls—particularly in anticipation of corporate actions, settlements, or beneficial owner sales—are essential for maintaining market integrity and client satisfaction. However, Agent Lenders often face operational and systemic challenges when managing recall fails, leading to reputational risk, settlement costs, and strained relationships with beneficial owners. To address these issues, a major Agent Lender p

In today’s rapidly evolving financial landscape, efficiency and risk mitigation are central in the Securities Lending environment. One area under increasing scrutiny is the collateral management framework used by Agent Lenders. Traditionally, collateral schedules—used to define eligible collateral types—have been highly granular, sometimes overly complex, and prone to operational inefficiencies due to the multiple collateral schedules agreed at a client-by-client basis. In re

On an agency lending desk, traders negotiate loans in seconds, operations teams manage collateral movements, and reporting teams prepare regulatory filings like SFTR. Each of these functions assumes one thing: that the data driving them is reliable. But how do we know? That’s where data quality dimensions  meet reality. Validity, completeness, consistency, accuracy — these aren’t abstract ideas. They’re lenses through which we test whether the data aligns with business expect

On an agency lending trading desk, data is more than numbers on a screen — it’s the foundation for trust between counterparties, operational efficiency, and regulatory compliance. When a trader agrees to lend securities, the desk and operations book the trade, risk teams monitor exposure, and reporting teams file to regulators. Each step relies on data being correct, consistent, and available. If the data fails, so does the business. This is where data quality dimensions come

Discovering data quality issues is only the first step. The harder, and more valuable, work is deciding what to fix, when, and how . On an agency lending desk, where loan trades move quickly, collateral is rebalanced daily, and regulators expect accurate reports by T+1, not every issue can (or should) be solved at once. The challenge is turning findings into actionable improvements  that deliver real business value. Identifying and Prioritizing Opportunities The process begin

In securities lending, numbers are never just numbers. A single misreported loan, a mistimed recall, or an error in collateral valuation can ripple through markets, unsettle clients, and damage reputations built over years. Conversely, when data is accurate, timely, and consistent, it becomes a quiet but powerful engine — building trust, enabling smarter decisions, safeguarding against risk, and powering efficiency. This is why data quality isn’t a back-office detail. It’s a

On an agency lending desk, traders, operations, risk teams, and regulators all rely on data behaving exactly as expected. But rules and frameworks alone aren’t enough. To realize the benefits of strong data quality, firms need to deploy it into daily operations  — managing rules, monitoring issues, and embedding accountability. Done right, this shifts the business from reactive firefighting  to proactive assurance . Managing Data Quality Rules: Preventing Errors at the Source

Improving data quality is not about cleansing spreadsheets in isolation; it begins with aligning data efforts to what the business values most. In securities lending, this means connecting data improvement to outcomes like maximizing revenue, managing counterparty risk, or meeting regulatory obligations. A robust framework provides both the structure and discipline to do this effectively. 1. Start with Business Needs and Critical Data Every improvement effort should begin by