What is a suspicious date range in audit testing?

A suspicious date range occurs when two or more date fields in a transaction do not follow a logical or policy-compliant sequence, such as payments recorded before purchase orders, invoices dated after payment, or unusually long gaps between approval and settlement.

Can I use my own transaction data with this workflow?

Yes. You can point the workflow to your own data sources by replacing the example input with your transaction files or database connections, and adjusting column mappings to match your data structure.

Which date fields can this workflow analyze?

The workflow can analyze any combination of date fields, such as purchase order date, invoice date, approval date, posting date, and payment date. You can configure which fields to include and how to calculate the intervals between them.

How do I adjust thresholds for what counts as suspicious?

Thresholds are configurable parameters in the workflow. You can set minimum and maximum acceptable intervals between dates or define business rules to flag specific patterns, then rerun the analysis to see which transactions violate those thresholds.

How can I deploy this analysis for recurring audits?

You can run the workflow as a data app for business users, schedule it to run regularly, or integrate it into a broader audit analytics solution using KNIME Business Hub to support continuous audit testing.

Suspicious Date Range Detection

Suspicious date range analysis is a method for detecting transactions that may be fraudulent or non-compliant by examining the timing of key events, such as purchase order and payment dates. This approach helps organizations spot unusual patterns, like early payments or retroactive approvals, that could indicate policy violations or control weaknesses.

AuditFinancial ServicesAutomation

How This Workflow Works

This workflow reviews transaction data to identify records where the sequence or timing of events falls outside expected norms. It validates the integrity of date fields, calculates differences between key dates, applies threshold checks, and presents findings through an interactive dashboard and static report.

Key Features:

Detects transactions with suspicious or non-compliant date ranges
Validates the accuracy and completeness of date and numeric fields
Calculates and flags records that exceed user-defined thresholds
Visualizes anomalies and summary statistics for further review

Step-by-step:

1. Validate Data Integrity:

The workflow checks the completeness and correctness of key fields, including dates and numeric values. It applies validation rules to ensure that the data is suitable for further analysis and highlights any missing or invalid entries.

2. Analyze Date Relationships and Flag Suspicious Records:

It calculates the time differences between important transaction dates, such as purchase order and payment dates. The workflow then compares these intervals against defined thresholds to flag transactions that fall outside acceptable ranges and that may be suspicious (e.g., those paid too early or with retroactive approvals).

3. Visualize and Share Insights:

The workflow summarizes findings in interactive dashboards, including tables and pie charts, and generates a static report that can be exported or shared. This helps users quickly understand the scope and nature of potential issues in their transaction data.