Most organisations have a defined way their processes are supposed to work. An approval should happen before payment. A customer may need to pass specific checks before an account is activated. Procurement may require a purchase order before an invoice is processed.
But documented processes and actual processes are not always the same.
Conformance checking in process mining helps organisations identify the difference. It compares actual process execution, captured in event logs, with a predefined process model or set of rules to identify where reality deviates from expectations. Research identifies conformance checking as one of the core disciplines of process mining, alongside process discovery and process enhancement.
At its simplest, conformance checking answers:
"Is our process actually being executed the way we intended?"
Process mining uses event data from systems such as ERP, CRM, and workflow platforms to reconstruct what happened during individual process instances. Conformance checking then compares that observed behaviour with a model representing the intended process.
For example, an organisation might define its procure-to-pay process as:
Purchase Requisition → Approval → Purchase Order → Goods Receipt → Invoice → Payment
If event data shows that invoices are regularly processed before the required approval, conformance checking can identify those cases as deviations.
The result is more than a process map. It provides evidence of where actual behaviour differs from the expected process.
Depending on the model and technique being used, conformance checking can identify patterns such as:
Modern conformance-checking approaches include techniques such as token replay, rule checking and alignment-based analysis. Alignments are particularly useful because they can show how observed events correspond to activities in the intended process and where the two diverge.
This allows organisations to move beyond simply knowing that a process is "non-conforming" and investigate what actually went wrong and where.
Conformance checking can be highly relevant to compliance, but the two concepts should not be treated as identical.
A process can conform perfectly to an internal process model and still fail to meet an external regulatory requirement if that model does not accurately represent the applicable regulation.
This distinction is increasingly important in regulated environments. Recent research into business process compliance highlights conformance checking as an important mechanism for comparing process instances with defined process models and compliance rules, while also identifying limitations in current approaches.
A more reliable compliance approach is therefore:
Regulation or policy → Operational rule → Process model → Actual process data → Conformance analysis
This creates an evidence-based way to identify potential control violations that can then be investigated.
Traditional compliance and process reviews can provide valuable insight, but they are often periodic exercises.
Conformance checking can analyse large volumes of actual process execution data and identify recurring deviations.
For enterprise leaders, this can support:
The objective isn't to eliminate every deviation. Some exceptions are legitimate and necessary.
The real question is:
Which deviations matter, why are they occurring, and what risk or business impact do they create?
Conformance checking is not a magic compliance button.
Its results depend on the quality of both the reference model and the event data.
If the documented process is outdated, a high level of deviation may indicate that the model is wrong rather than that employees are failing to follow it.
Likewise, if important process activity takes place outside the systems being analysed, the event log may not provide a complete picture.
Research into conformance checking has long recognised challenges involving incomplete data, noise and non-conforming behaviour.
This is why organisations should interpret conformance results in their operational context rather than treating a single conformance score as a definitive measure of process quality.
Conformance checking becomes particularly valuable when it is connected to a broader process intelligence strategy.
The progression can be thought of as:
Data → Process → Deviation → Root Cause → Action → Continuous Monitoring
Process discovery helps establish how processes actually operate.
Conformance checking identifies where reality differs from expectations.
Process intelligence can then help organisations understand the causes, assess business impact and prioritise action.
This aligns with Verdant Data's broader view of Process Intelligence as a way to use business data for fact-based visibility and analysis, including identifying process inefficiencies and compliance challenges.
The field is also evolving. Research published in 2025 is examining how AI could support conformance checking, including opportunities to address existing analytical challenges.
When evaluating conformance-checking capabilities, organisations should look beyond a simple "compliant/non-compliant" result.
Ask whether the solution can identify:
The goal isn't perfect conformity for its own sake.
The goal is to understand where reality differs from expectation - and determine which differences actually matter.
Conformance checking turns the gap between "how we think the process works" and "how the process actually works" into something measurable.
For organisations focused on operational excellence, governance and risk, that creates a valuable feedback loop:
Define → Observe → Compare → Investigate → Improve → Monitor
Process discovery tells you what is happening.
Conformance checking tells you where reality differs from what was intended.
And process intelligence helps turn those findings into action.
For enterprise organisations, that distinction can be the difference between simply documenting a process and actually governing how it operates.
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