Sourcing and reconciling data across custodians and vendors

Custodians. These are the banks that legally hold your assets and produce the official record: State Street, BNY, JPMorgan, Northern Trust, Citi. Their overnight position and price files are frequently treated as the "golden source" for holdings, though not always for prices.

Reference and entity data. Static descriptive data: identifiers (ISIN, CUSIP, FIGI), issuer hierarchies, ratings, maturity, coupon, day-count conventions. A single wrong day-count convention silently misprices a whole sleeve of bonds.

For a grounding in how instruments are identified across these feeds, the Association of National Numbering Agencies overview of the ISIN standard is a clean free reference.

Why the same bond looks different

Four honest reasons a price breaks across sources:

1. Snapshot time. Bloomberg may stamp 4:00 PM ET, the custodian may use a 3:00 PM regional close.

2. Clean vs dirty price. Clean price excludes accrued interest; dirty price includes it. Mix these up and you get a break equal to the accrued.

3. Contributor set. ICE and Bloomberg poll different dealers.

4. Convention. Price per 100 face vs price in fractions (Treasuries often quote in 32nds).

Rule one of reconciliation: never compare two numbers until you have normalized time, price type, and convention.

Building the reconciliation waterfall

A reconciliation waterfall is a sequence of checks, cheapest and most certain first, that a position must pass. Each stage either clears the position or routes it to an exception queue. The point is to eliminate the easy 95 percent automatically so analysts only touch the genuine 5 percent.

Stage 1: Existence and identifier match

Does the custodian hold the position your book says it holds? Match on identifier. Watch for the classic trap: CUSIP is US and Canada only, ISIN is global, FIGI is Bloomberg's open identifier. A position keyed on CUSIP in one system and ISIN in another will look like a break when it is just a mapping gap.

Stage 2: Quantity reconciliation

Compare face amount or share count. Quantity breaks are usually corporate actions (a bond called, a partial paydown on a mortgage-backed security) that one system processed and the other did not. Quantity breaks are more dangerous than small price breaks because they compound.

Stage 3: Price reconciliation with tolerance bands

Here is the core. You do not flag every difference. You set a tolerance and only escalate breaks that exceed it.

A simple worked example. Suppose your tolerance policy is: flag any bond price break greater than 0.50 percent OR greater than 25 basis points of yield, whichever is tighter for that asset class.

A 0.642 percent break exceeds the 0.50 percent band, so it goes to the exception queue. Had the custodian shown 100.90, the break would be 0.30 percent, below tolerance, and it clears silently.

Tolerances should be tighter for liquid instruments (a US Treasury breaking 0.50 percent is alarming) and wider for illiquid ones (a private high-yield bond legitimately varies more between vendors). Set them per asset class, not one global number.

Stage 4: Cross-vendor triangulation

When a price breaks against the custodian, do not assume either is right. Pull the third and fourth sources. If Bloomberg, Refinitiv, and ICE cluster around 101.2 and only the custodian shows 100.55, the custodian file is the likely culprit (often a stale price carried over from the prior day). If the three vendors themselves disagree widely, you have a genuinely hard-to-price instrument and need a documented pricing hierarchy.

A pricing hierarchy is a written, governed rule: for asset class X, use source A; if A is missing or stale, fall back to B, then C. Auditors and regulators expect this to be documented, not improvised.

Governance and data-quality metrics

You cannot manage what you do not measure. The data function tracks its own quality with a small set of hard metrics. These are governed under frameworks influenced by BCBS 239 (the Basel Committee's principles for risk data aggregation, still the reference standard for data governancedata governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization.View full definition → in 2026) and, in Europe, expectations from ESMA and national regulators around NAV accuracy for funds under UCITS and AIFMD.

Core metrics to track daily:

• Completeness. Percentage of positions with a valid price. Target is typically 100 percent for liquid books; anything under flags stale or missing feeds.
• Break rate. Number of positions exceeding tolerance divided by total positions. A rising break rate signals a feed or mapping problem.
• Stale price rate. Percentage of prices unchanged for N consecutive days when the market was open. A corporate bond with an identical price for five straight days is suspicious.
• Timeliness. Percentage of feeds delivered before the NAV cutoff.
• Time-to-resolution. Median hours to clear an exception. This is the metric that shows whether your process actually protects the books or just logs problems.

These are internal operational metrics, not the invented kind. Set your own targets from your own baseline. A common practice is to review break rate and stale rate trend lines weekly rather than obsessing over a single day.

A NAV mispricing you could have caught

NAV is Net Asset Value, the per-share value struck for a fund, usually daily. It is the number investors buy and sell on, so a wrong price is not academic: it can mean investors transact at the wrong value, triggering compensation.

Consider a real-shaped scenario (illustrative, not a specific event). A fund holds an illiquid convertible bond. The vendor feed misses it one night, so the accounting system carries forward yesterday's price. The stale price rate metric spikes for that one position. The waterfall's Stage 3 does not catch it (price did not change, so no break), but the stale price check in the quality layer does. That is why completeness and stale checks sit alongside break tolerance, not instead of it. A break-only process is blind to prices that are wrong precisely because they did not move.

This is the practical lesson: a single tolerance band is not a reconciliation program. You need existence, quantity, break tolerance, cross-vendor triangulation, and stale detection working together.

Key Takeaways

• Four sources, four prices, all legitimate. Bloomberg (BVAL), Refinitiv (LSEG), ICE Data Services, and your custodian differ on snapshot time, contributor set, and convention. Normalize time, clean vs dirty, and quoting convention before comparing anything.
• Build a staged waterfall. Existence and identifier match, then quantity, then price with per-asset-class tolerance bands, then cross-vendor triangulation. Clear the easy majority automatically so analysts touch only real exceptions.
• A documented pricing hierarchy is a governance requirement, not a convenience. Regulators under BCBS 239, UCITS, and AIFMD expect written fallback rules.
• Track quality with hard metrics: completeness, break rate, stale price rate, timeliness, and time-to-resolution. Trend them; a rising break rate is an early warning of a broken feed or mapping.
• Break detection alone is blind to stale prices. A price that is wrong because it never updated will pass a break check. Run stale detection in parallel to protect the NAV.