BIN & Issuer Outlier Analysis Dashboard flags BINs whose performance is swinging sharply period-over-period, so you can catch a sudden shift in a specific BIN's sales, transaction count, or rate before it shows up in your portfolio-level numbers.
Default view displays settled data only. Use the Funding Method filter to access conveyed data.
Pay by Bank transactions are excluded from this dashboard.
BIN data is limited to what has been provided by acquirers.
The dashboard has two parts and is built to be worked top to bottom. The chart tells you which BINs moved. The pivot table tells you what the underlying numbers were and whether the movement is specific to that BIN or shared by its peers.
Chart Section
PoP Net Sales % Change by BIN
Start here to find out which BINs are worth investigating. Each bubble is one BIN6, positioned by how much its selected Measure changed against the immediately preceding period of equal length, and sized by Net Sales.
Read the two dimensions together rather than separately. A large bubble sitting far from the 0% line is a high-volume BIN that has genuinely moved, and is usually where to start. A small bubble at an extreme percentage is more often a low-volume BIN where a handful of transactions swung the rate, which rarely justifies follow-up. Bubbles clustered near 0% are behaving normally and can be ignored.
Change the Measure control to shift what you are testing for:
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Net Sales (default) answers "did money move?"
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Transactions separates a drop in volume from a drop in average ticket. If Net Sales fell but Transactions held steady, the issue is ticket size, not customer behaviour.
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Effective Rate surfaces cost shifts rather than sales shifts, which is what you want when a BIN's volume is flat but its economics have changed.
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% of Total Sales and % of Total Transactions show whether a BIN is gaining or losing share of your portfolio. Use these when overall volume is trending and you need to know whether a BIN moved relative to everything else or simply moved with it.
Date Granularity sets what each point is compared against. At the default Weekly setting, each point compares one week to the week before it. Switch to Monthly when weekly noise is obscuring a slower trend, or to Daily when you are pinning down exactly when a shift started.
Pivot Table
The chart identifies candidates. The pivot table is where you confirm them, and it does two jobs that used to be split across separate sections.
It shows the numbers behind the percentage. The Net Sales Delta % and Transactions Delta % measures carry the same period-over-period comparison the chart plots, sitting directly alongside the Net Sales and Transactions values they were calculated from. This matters because a percentage alone can mislead. A BIN showing a 60% drop may have gone from $400,000 to $160,000, which is worth escalating, or from $500 to $200, which is not. Reading the delta next to the absolute value settles that in one glance.
It puts the BIN in context. The chart organises everything around BIN6 alone. Grouping lets you see a BIN against its issuer group and card funding source, which is what turns an observation into a diagnosis. If a BIN's sales dropped and every other BIN in the same issuer group dropped with it, you are looking at an issuer-side event and there is nothing BIN-specific to chase. If the rest of the group held steady, the problem is isolated to that BIN and worth a closer look.
Controls:
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Measure: multi-select, with all seven measures selected by default. Clear the selection and keep two or three when you want a table you can actually scan, since each grouping row expands into one line per selected measure.
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Groups: sets how many levels the table drills through, up to three, each chosen independently. The default arrangement is Card : BIN6, then BIN : Issuer Group, then BIN : Card Funding Source, which is the sequence that answers the "is this BIN alone or is the whole issuer moving?" question. Any grouping field can be swapped in.
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Date Granularity: controls the column periods, independently of the chart above. Defaults to Weekly.
Available measures:
|
Measure |
What it tells you |
|---|---|
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Net Sales |
Net sales for the group in each period. The absolute value behind any percentage movement. |
|
Net Sales Delta % |
Change in Net Sales against the prior period of equal length. This is the measure the chart plots, shown here in table form. |
|
Transactions |
Transaction count for the group in each period. |
|
Transactions Delta % |
Change in transaction count against the prior period. Read alongside Net Sales Delta % to separate a volume shift from a ticket size shift. |
|
Effective Rate |
Cost as a percentage of sales for the group. Use when a BIN's volume is steady but its economics have changed. |
|
% of Total Sales |
The group's share of total sales, so you can tell portfolio share movement from absolute movement. |
|
% of Total Transactions |
The group's share of total transaction count. |
This is also the practical place to export from when you need the underlying numbers for analysis outside Harmonize.
Filtering Tips
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Date Range for Charts and Data: defaults to the last 8 weeks including the current week, and applies to the chart and the pivot table together. Narrowing it narrows everything on the page at once, so widen it before concluding that a swing is new.
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Funding Method: the dashboard defaults to settled data only. Open Filters and adjust Funding Method to include conveyed transactions.
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BIN 6: leave this empty to see the Top N BINs by volume. Once you have identified a BIN worth tracking, enter its BIN6 value here to pin both the chart and the pivot table to that BIN, which is the fastest way to follow one BIN week to week.
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Top N: defaults to 10, ranked by Net Sales $. Hover over the Top N info icon to confirm which metric the ranking is based on. Raise it when a mid-volume BIN you are looking for is being cut off, but be aware that a high Top N crowds the chart and makes clusters harder to read.
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Date Granularity: defaults to Weekly, switchable to Yearly, Quarterly, Monthly, or Daily. The chart and the pivot table each have their own granularity control, so they can be set differently.
Use Case Example
"We noticed a big swing in sales for a specific BIN week over week and want to identify the outliers."
Review PoP Net Sales % Change by BIN to spot which BIN6 values show the largest swings, prioritising large bubbles far from the 0% line
In the Pivot Table, read Net Sales next to Net Sales Delta % for that BIN to confirm the movement is material rather than a large percentage on a small base
Read down the grouping from BIN6 to Issuer Group to Card Funding Source to establish whether the swing is isolated to that BIN or shared across its peers
Enter the BIN6 value in the BIN 6 filter to lock the page to that BIN and watch it over subsequent weeks