Multi-custodian reporting means aggregating account, position, transaction, and performance data from multiple custodial platforms — Schwab, Fidelity, Pershing, Interactive Brokers, and others — into a single, normalized view. For RIAs serving clients across custodians, this is not a nice-to-have. It is the foundation of accurate household reporting, billing, compliance, and analytics.
Why Multi-Custodian Firms Struggle with Data
The problem is not that custodians fail to deliver data. Most do. The problem is that each custodian delivers it differently — and every difference creates work that falls on your operations team.
- Different custodians use different data formats, account structures, security identifiers, and delivery schedules. What arrives from one custodian at 7am may look nothing like what arrives from another at 9am.
- Schwab sends data differently than Fidelity, which differs from Pershing. File formats, field names, position schemas, and transaction codes are not standardized across custodians.
- Portfolio management systems handle reconciliation but don't normalize the underlying data for analytics. Reconciliation is an operational task. Analytics requires a different kind of normalization — one that makes custodian data joinable with CRM, planning, and operational data.
- Household-level reporting across custodians requires matching accounts, normalizing positions, and reconciling transactions that arrive in different formats. A client with accounts at both Schwab and Fidelity looks like two separate entities until someone — or something — ties them together.
- The result: firms spend hours on manual data work that should be automated. Ops teams build and maintain custom reconciliation workflows. Analysts spend time preparing data instead of analyzing it. Reports lag behind reality.
How Platforms Handle Multi-Custodian Data
Portfolio management systems solve the operational reconciliation problem well. They connect to custodians, pull data, reconcile positions and transactions, and present a unified view inside their platform. That is exactly what they are designed to do. The question for multi-custodian firms is what happens to that data beyond the PMS — and whether it can connect with the rest of the tech stack.
Each of these platforms handles multi-custodian reconciliation within its own ecosystem. What none of them natively provides is a normalized data layer that joins custodian data with CRM activity, financial plan status, and operational data in a single query. That is a different problem — and it requires a different kind of solution.
What Multi-Custodian Firms Actually Need
Solving multi-custodian data is not just about reconciliation. It is about building a foundation that makes the data usable — for reporting, for analytics, for billing, and for the questions that matter most to an advisory firm's leadership.
Normalized data model
Same schema regardless of custodian source. Schwab accounts and Fidelity accounts look identical in the data model — fields, types, and relationships are consistent.
Household-level aggregation
Accounts across custodians grouped by household automatically. A client's Schwab IRA and Fidelity brokerage account resolve to the same household without manual matching.
Automated reconciliation
Position and transaction matching without manual intervention. Discrepancies surface as exceptions, not as undiscovered problems buried in spreadsheets.
Cross-system analytics
Custodian data joined with CRM, planning, and portfolio data in a single query. AUM at the custodian level correlated with financial plan funding status and last advisor touchpoint.
Historical continuity
Complete data history even when adding or changing custodians. New custodian data loads into the same warehouse structure — historical comparisons remain intact.
Real-time or near-real-time feeds
Not batch-delayed data for critical operations. Billing, compliance, and client reporting depend on data that reflects the actual current state of accounts — not yesterday's snapshot.
Multi-Custodian Data: Before and After
The Data Layer Approach
Portfolio management systems — Orion, Tamarac, Black Diamond — reconcile custodian data for operational purposes. That is their job and they do it well. Reconciliation ensures that what your PMS shows matches what the custodian holds. It is essential for trading, rebalancing, billing, and client reporting.
But operational reconciliation is different from analytical unification. Reconciliation makes data accurate within a platform. Unification makes data comparable and queryable across every system a firm uses — portfolio, CRM, planning, compliance, and operational data all in one place.
Milemarker pulls custodian data — alongside portfolio, CRM, planning, and operational data — into a firm-owned Snowflake warehouse. The data is normalized against a wealth management-specific model: same account structure, same position schema, same transaction taxonomy regardless of custodian source. One SQL query can answer "What is the total household AUM across Schwab and Fidelity, correlated with their financial plan funding status in eMoney and their last CRM touchpoint in Salesforce?"
How this differs from what your PMS already does
- Your PMS reconciles for operations. It ensures positions match the custodian and feeds your trading and reporting workflows. That stays exactly as it is.
- Milemarker normalizes for analytics. It takes the same custodian data and maps it to a consistent schema in Snowflake — alongside your CRM, planning tools, and other systems — so you can query across all of them.
- The two work together, not in competition. Milemarker does not touch your PMS workflow. It adds a data layer above the operational stack, not a replacement for it.
- Your data lives in a warehouse you own. Unlike analytics that are bounded by a vendor's infrastructure, Milemarker puts your data in your own Snowflake instance — queryable by your team, portable if platforms change, and available for AI workloads across the full data set.
Custodians Milemarker Connects
Milemarker maintains pre-built integrations for all major custodians used by independent RIAs — with data normalized to the same schema regardless of source.
Beyond custodians, Milemarker connects portfolio management systems, CRMs, financial planning tools, compliance platforms, and operational data sources — all normalized into the same Snowflake warehouse. Custodian data is one input among many, which is what makes the cross-system analytics possible.