Most RIAs use multiple custodians. Most technology platforms make it harder than it should be to see a unified picture across all of them.

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.

Orion

Strong custodian connectivity for portfolio management. Reconciliation is built in and covers all major custodians. But analytics (Summit) are bounded by the Orion ecosystem — cross-custodian data does not extend to CRM, planning, or operational analytics outside Orion's own tools.

Envestnet Tamarac

Deep custodian feeds with automated reconciliation. Rebalancing across custodians is a genuine strength. But data stays within Tamarac infrastructure for analytics purposes — cross-system joins to non-Tamarac tools require additional work.

Black Diamond (SS&C)

Multi-custodian performance reporting and billing. Strong for the portfolio layer. But no native cross-system analytics connecting custodian data to CRM, financial planning data, or operational reporting outside the Black Diamond environment.

Addepar

Best-in-class multi-custodian aggregation including alternatives. Designed for HNW and UHNW segments with the cost and implementation complexity that reflects. Most compelling for firms with significant alternative allocations or institutional-grade reporting requirements.

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.

01

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.

02

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.

03

Automated reconciliation

Position and transaction matching without manual intervention. Discrepancies surface as exceptions, not as undiscovered problems buried in spreadsheets.

04

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.

05

Historical continuity

Complete data history even when adding or changing custodians. New custodian data loads into the same warehouse structure — historical comparisons remain intact.

06

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

Without a unified data layer

Schwab data in one format. Fidelity in another. Manual matching required.

Household reports require ops intervention to combine custodian views.

Analytics limited to one custodian at a time within each platform.

Billing reconciliation takes days — custodian data doesn't map cleanly.

Adding a new custodian means building another reconciliation workflow from scratch.

With Milemarker's data layer

All custodian data normalized into one schema. Same fields, same types, regardless of source.

Automatic household aggregation across all custodians — no manual matching.

Cross-custodian analytics joined with CRM and planning data in a single query.

Billing reconciliation in minutes — data is already normalized and comparable.

New custodians connect through pre-built integrations and map to the existing schema automatically.

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.

Schwab

Fidelity

Pershing (BNY)

Interactive Brokers

TD Ameritrade (legacy)

Raymond James

LPL Financial

RBC

+ 130 total integrations across the advisory tech ecosystem

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.

Frequently Asked Questions

Why is multi-custodian data so difficult to manage?

Each custodian delivers data in different formats, on different schedules, using different account structures and security identifiers. Schwab's data files differ structurally from Fidelity's, which differ from Pershing's. Portfolio management systems reconcile this data for operational purposes, but they do not normalize it into a single schema suited for cross-system analytics. The result is that firms must either do significant manual work to create a unified view, or accept reporting that is siloed by custodian.

Can I get multi-custodian reporting from my portfolio management system?

Portfolio management systems like Orion, Tamarac, and Black Diamond handle custodian reconciliation well — that is their job. They can produce household-level performance reports across custodians within their platform. But their analytics and reporting stay within the PMS ecosystem. They do not join custodian data with your CRM, financial planning tools, or operational data sources for cross-system analytics. That is a separate data layer problem.

How does Milemarker normalize data across different custodians?

Milemarker pulls custodian data — accounts, positions, transactions, and performance — into a firm-owned Snowflake warehouse and maps it against a wealth management-specific data model. Every custodian's data arrives in Milemarker's standard schema: the same account structure, the same position fields, the same transaction taxonomy, regardless of the source custodian. This means a single SQL query can aggregate across Schwab and Fidelity accounts in the same household without any per-custodian translation work.

Does Milemarker replace my portfolio management system for custodian data?

No. Milemarker is a data and analytics layer, not a portfolio management system. Your existing PMS continues to handle daily reconciliation, trading, rebalancing, and client reporting. Milemarker connects the same custodian data — alongside your CRM, planning tools, and other systems — in a normalized Snowflake warehouse for cross-system analytics and reporting. The two work together, not in competition.

How long does it take to connect multiple custodians through Milemarker?

Milemarker maintains pre-built integrations for all major custodians — Schwab, Fidelity, Pershing, Interactive Brokers, and others. Connection timelines depend on custodian data delivery setup and your firm's credentialing process, but most custodian connections are live within a standard implementation timeline. Milemarker handles the data normalization — your team does not need to build or maintain custodian-specific ETL pipelines.

What if I add or change custodians?

Adding a new custodian to Milemarker means activating a pre-built integration and normalizing the incoming data against the same schema already in place for your existing custodians. Historical data from the new custodian loads into the same warehouse structure, so it is immediately comparable to data from your other custodians. Changing custodians does not require rebuilding your analytics — the data model stays consistent even as your custodial relationships change.

Related guides

Part of the Wealth Operations series:

• Custodian Data Aggregation: Unified Reporting Across Schwab, Fidelity & Pershing

• Client Data Consolidation for Wealth Management: Unified Household Views

• AI-Generated Reporting for Wealth Management: Ask Claude, Get Real Answers

• Data Reconciliation for Wealth Management: Ending the Daily Spreadsheet Shuffle

• Data Migration for RIAs: Moving CRM, Custodian & Portfolio Data Without Breaking Everything

• Data-Driven M&A Due Diligence for RIAs: Evaluate Acquisitions with Confidence