Most private credit managers don't choose to run across multiple loan administration platforms. It happens: you close a deal where the borrower's agent is already on Black Mountain, you inherit a vehicle that was built on Allvue, and a regional bank servicer you've used for three years delivers flat files with their own schema. Suddenly your reconciliation workflow has three distinct data sources with three distinct formats, none of which were designed to talk to each other.
The normalization problem is not about preference for one platform over another. Both Allvue and Black Mountain are capable systems. The problem is purely the field naming and structure conventions they use, and what it takes to map each one to a common position schema that can support cross-platform reconciliation.
How Allvue and Black Mountain structure their exports differently
The structural differences between the two platforms' standard exports show up in a few specific dimensions that matter for reconciliation.
Facility and tranche identification. Allvue typically organizes positions around facility-level records with a separate tranche identifier. Black Mountain flattens this into a single loan record per tranche, with the facility relationship stored as a reference field. For a fund running revolvers with multiple tranches, this structural difference means a single position in your portfolio may appear as two records in one platform and one record in the other. Reconciliation against a position count is meaningless until you normalize the structure.
Outstanding balance fields. Both platforms report outstanding balance, but they differ on what outstanding means when there is an unfunded commitment. One convention includes only the drawn balance. Another includes the drawn balance plus a utilization rate against the total commitment. If you're comparing current position balances across platform outputs without normalizing this distinction, you will produce spurious reconciliation breaks on every revolver in the portfolio.
Payment date fields. Scheduled payment due date, last payment received date, and next payment due date are all commonly present in loan administration exports. The field names differ across platforms, and the date reference conventions sometimes differ: one platform may use the period end date for a payment that was received in the following period, while another records the actual receipt date. For cash flow reconciliation purposes, this distinction changes the quarter in which a payment is attributed.
None of these differences are errors in either platform. They are legitimate design choices. The normalization layer's job is to resolve them into a consistent schema regardless of source.
The flat-file servicer problem
Allvue and Black Mountain are structured enough that their exports, while different, are at least documented and consistent release to release. The harder integration problem is the file-based servicer: the regional bank, the sub-servicer, the specialist administrator that delivers a CSV or Excel file whose schema is undocumented, whose column names change occasionally, and whose definitions sometimes shift without notice.
We've seen file-based servicer exports where the outstanding balance field was named "Current O/S," "Curr Outstanding," "Outstanding Balance (MM)," and "Bal_Curr" across four tape deliveries from the same servicer over 18 months. Each name change was incidental, not intentional, but each one required a mapping update to avoid a broken ingestion run.
Handling this robustly requires a field mapping configuration that is schema-based rather than column-position-based, with a validation step that checks expected fields on each tape delivery before attempting to process it. When a field name changes, the validation catches it before the data enters the normalization pipeline. The alternative is a silent misalignment that produces clean-looking position data with values mapped to the wrong fields.
What the field mapping configuration actually contains
A field mapping configuration for a loan tape source defines, at minimum: the source field name as it appears in the raw tape, the target field in the normalized position schema, any transformation required (unit conversion, date format standardization, string to enumerated value mapping), the validation rule for the field (required, optional, expected value range), and the conflict resolution priority when multiple sources provide the same field for the same position.
The conflict resolution priority is the part that requires the most thought. For a position that appears in both the Allvue export and the servicer flat file, both sources report outstanding balance. Which one wins? In most cases, the servicer tape is more authoritative for actual payment activity. The loan administration system may be more current on facility terms. The right answer is usually source-dependent and field-dependent: servicer tape wins on balance, Allvue wins on covenant terms, and discrepancies above a tolerance threshold get flagged as reconciliation breaks rather than resolved automatically.
The normalization run in practice
When all three sources are configured and current tapes have been delivered, the normalization run processes each source through its field mapping, produces a normalized position record for each facility in each source, and then performs a cross-source merge to produce the unified position view.
The merge step is where the cross-platform reconciliation actually happens. For each facility that appears in multiple sources, the normalized records are compared field by field. Fields where all sources agree are merged into the unified record without flagging. Fields where sources disagree above tolerance are flagged as reconciliation breaks and held for review before the unified record is finalized.
A fund running 80 positions across three sources, with roughly 20 fields per position, will typically produce somewhere between 5 and 25 reconciliation breaks per quarter on the initial normalization runs. Most of those breaks will be systematic: the same type of discrepancy affecting the same type of position across the same source pair. Identifying and resolving the systematic breaks in the first two quarters establishes a stable baseline where the ongoing break rate drops significantly.
What this looks like in an actual onboarding
The first thing we do when a fund is running across multiple platforms is a tape audit: collect the last two quarters of tapes from each source, run them through the normalization pipeline without trying to resolve anything, and review the break report.
The break report from the audit run is the most informative document in the onboarding. It shows which sources disagree most frequently, which fields produce the most breaks, and which breaks are systematic versus one-off. From that, we build the conflict resolution rules and the tolerance thresholds for the ongoing reconciliation workflow.
The field mapping configuration for a three-source portfolio typically takes one onboarding session to establish for Allvue and Black Mountain, and two to three sessions for file-based servicers where the schema needs to be inferred from the tape rather than read from documentation. After that, the ongoing maintenance is primarily triggered by servicer format changes, which we catch at the validation step before they affect the position data.
The goal is not to eliminate source diversity. The goal is to make it invisible at the position view layer: the IC report shows one consistent view of each position regardless of which platform originated it, and the ops team has a clear, auditable record of how every discrepancy was resolved. That's achievable even when the underlying systems have genuinely different data models. It just requires doing the normalization work explicitly rather than assuming it away.