Turning Insight into Action: Improving Data Quality in Securities Lending

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 begins with data profiling — a full-scale analysis of large datasets to uncover the scope and frequency of issues. For example:

• How often are ISINs missing from collateral records?

• Are settlement dates consistent across custodians and agent lenders?

• Which SFTR fields fail validation most frequently?

At the same time, stakeholder interviews bring out the lived experience of data consumers. A trader may flag misaligned counterparty codes that delay deal booking. Operations might highlight recurring exceptions in collateral substitutions. Risk managers may note gaps in exposure reports that complicate liquidity monitoring.

By combining profiling with stakeholder feedback, the business builds a clear picture of which issues matter most and why.

Setting Goals for Data Quality Improvement

Preliminary findings form the foundation for specific goals. These can range from quick fixes — like adding validation checks to a booking screen — to long-term initiatives, such as redesigning a legacy system to enforce referential integrity.

The mix usually includes:

• Quick wins: Low-cost, high-impact changes (e.g., fixing dropdowns, enforcing field validation).

• Strategic programs: Root-cause initiatives that address systemic weaknesses (e.g., governance models, integration redesign, automation of reconciliations).

Every goal must be anchored in business value: faster settlements, fewer client disputes, better regulatory compliance, lower operational risk.

Overcoming Barriers

Improvement isn’t always straightforward. Common barriers in securities lending include:

• System constraints: Legacy “black box” applications that make it hard to see, let alone correct, the data inside.

• Complexity: Interconnected data feeds between agent lenders, custodians, and counterparties make it difficult to isolate root causes.

• Ongoing projects: New system rollouts or regulatory initiatives may compete for resources.

• Cultural resistance: Without top management buy-in, data quality is often seen as “someone else’s problem.”

To succeed, teams must tie data improvements directly to business outcomes. A loan desk manager is more likely to support change if it’s framed as “reducing settlement fails by 20% and saving X hours of manual reconciliation” rather than simply “cleaning up fields.”

Making the ROI Case

Not every issue deserves equal attention. The key is to weigh return on investment (ROI) for each fix, considering factors such as:

• Criticality: Does the data feed client reporting, risk management, or regulatory obligations?

• Scope: How much data is affected, and how old is it?

• Impact: Which processes, clients, or regulators rely on it?

• Risk: What’s the potential exposure if the issue isn’t fixed?

• Cost: How expensive is remediation vs. the cost of workarounds?

A good rule of thumb: preventing issues costs less than correcting them later. For example, adding validation at trade capture is far cheaper than unwinding failed settlements or re-submitting SFTR reports.

From Insight to Competitive Advantage

The end goal isn’t just “better data.” It’s stronger performance across the lending lifecycle:

• Traders can negotiate with confidence, knowing their systems reflect reality.

• Operations can settle loans faster and with fewer breaks.

• Risk teams can monitor exposures accurately in real time.

• Clients gain trust in reports and valuations.

• Regulators see timely, compliant submissions.

When data issues are prioritized, justified, and fixed at the root, a securities lending program doesn’t just avoid risk — it builds efficiency, trust, and competitive edge.

Please note that this article was written with the DAMA-CDMP course material as a backbone relating it to the securities lending market.