The Authorization Rejects Dashboard gives you a detailed, month-over-month view of transactions that were rejected before ever reaching the card network — things like invalid account numbers, MOP/network mismatches, and other formatting or validation failures. This is distinct from the Declines dashboard, which covers transactions the issuer reviewed and declined; rejects are caught earlier, typically at the gateway or verification step.
Use this dashboard to pinpoint which reject reasons are driving volume, which payment method (MOP) they cluster around, and how that volume is trending month over month.
Acquirer Data Availability
What appears on this dashboard depends on which acquirers send Optimized Payments the underlying data. If an acquirer is not listed below, its activity will not appear here — that is a gap in source data coverage, not a true zero. Within your own account, coverage also depends on which of these acquirers you process with.
Acquirers with authorization reject data currently available:
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Chase
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Braintree
Date Range
Defaults to Last 6 months including this month. Controls which months or periods appear as columns in the pivot table below. Click the field to pick a different range.
Date Granularity
Defaults to Month. Controls the column breakdown of the pivot table — at Month granularity, the table shows one column per calendar month (Feb-2026, Mar-2026, etc.); other granularities roll the same reject volume up or down accordingly.
Reject Pivot Table
This is the core of the dashboard: a fully expandable/collapsible pivot table that breaks reject volume down through five nested levels, from broadest to most specific.
|
Level |
Field |
What it represents |
|---|---|---|
|
1 |
Reject Category |
The highest-level grouping of the reject (e.g., Verification, Authorization) |
|
2 |
Reject Reason Group |
A broader reason bucket within the category (e.g., Invalid Account Number, Do Not Honor, Invalid Other) |
|
3 |
Reject Reason |
The specific reject reason (e.g., Invalid Cc Account Number, Does Not Match Mop, Invalid Mop For Division) |
|
4 |
MOP Group |
The card network or payment method family tied to the reject (e.g., Visa, Discover, Union Pay, Mastercard, JCB, Other) |
|
5 |
MOP |
The specific method of payment within that group (e.g., Maestro under Mastercard, Citi FDR Private Label under Citi FDR) |
Each level can be expanded or collapsed independently using the –/+ toggle to its left, and every level rolls up into a bold "[Value] total" subtotal row, with the top-level Reject Category rolling up into a grand total for the whole table. The table carries two measures — Reject Count and Reject Amount — and both appear in the columns to the right, one set per period in the selected Date Range, with subtotals for both at every grouping level.
Available Measures
|
Measure |
Description |
|---|---|
|
Reject Count |
Total number of rejected authorization transactions for the row in the selected period. |
|
Reject Amount |
Total dollar value of those rejected transactions for the row. |
Read the two together, because they often diverge. A reject reason with a modest count but a large amount is a small number of high-value transactions failing — a different problem, and usually a more urgent one, than a reason producing many low-value rejects. Ranking by count alone will hide the first case.
Use the expand/collapse structure to go from a high-level view ("how many rejects came from Verification issues this month?") down to a root cause ("which specific MOP is driving the Invalid Account Number rejects?") without leaving the table.
Filtering Tips
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Date Range: Narrow to a specific month or quarter if you're investigating a spike rather than a trend
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Date Granularity: Switch to Week if you need to isolate exactly when a reject reason started climbing
Use Case Example
"We're seeing more authorization rejects this month — where are they coming from?"
Start at Reject Category to see whether Verification or Authorization-stage rejects are driving the increase
Expand into Reject Reason Group and Reject Reason to find the specific failure (e.g., Invalid Account Number vs. Do Not Honor)
Drill into MOP Group / MOP to see whether the increase is concentrated in a single card network or spread across all of them
Compare Reject Count against Reject Amount at each level to judge whether the increase is costing you volume or value