Your Accounts Receivable Data Template

This attribute defines the specific step or status change that occurred in the invoice lifecycle. Examples include Invoice Created, Payment Posted, Dispute Opened, or Credit Memo Issued.

By analyzing the sequence of these activities, process mining tools reconstruct the process map. This allows organizations to identify bottlenecks, rework loops (such as repeated dispute updates), and deviations from the standard collection procedures.

It is the mandatory Activity field required to define the process steps.

Found in transaction logs, change documents, or status history tables.

This attribute captures the exact moment an action took place within the source system. It is critical for establishing the chronological order of events within a case.

Analysts use this data to calculate throughput times between process steps, such as the time from Invoice Sent to Payment Posted. It is also the basis for determining the duration of specific activities and identifying delays in the cash collection cycle.

It is the mandatory Start Timestamp required to order events and calculate duration.

Found in system logs, transaction entry timestamps, or change history tables.

This attribute represents the fundamental case identifier for the Accounts Receivable process. It distinguishes one billing transaction from another and serves as the primary key for tracking the lifecycle of a receivable from creation to clearing.

In process mining analysis, this attribute is used to group all related events, such as invoice creation, updates, disputes, and payments, into a single case. It allows analysts to visualize the end-to-end journey of a specific bill and calculate performance metrics at the transaction level.

It is the mandatory Case ID required to reconstruct the process flow.

Typically found in the transaction header table or the main accounts receivable sub-ledger table.

This attribute records when the dataset was last updated in the process mining application. It serves as a metadata field to inform users about the freshness of the analysis.

It helps analysts understand if they are looking at real-time data or a snapshot from a previous period. This is crucial for accurate reporting and ensuring that decisions are based on the most current information available.

Provides context on data latency and reliability.

Generated by the data pipeline or ETL tool at runtime.

This attribute identifies the software application or environment (such as SAP, Oracle, or HighRadius) from which the record was extracted. In complex landscapes with multiple ERPs, this helps distinguish data sources.

It is particularly useful when comparing process performance across different regions or business units that may utilize different underlying technologies. It ensures traceability of data back to its origin.

Essential for filtering and validating data in multi-system environments.

Usually added as a static string during the ETL process.

This attribute represents the organizational entity within the enterprise that owns the receivable. In systems like SAP, this corresponds to Company Code; in NetSuite, it is the Subsidiary.

It allows for comparative analysis across different branches or divisions. Management can use this view to benchmark collection performance, standard process adherence, and DSO metrics across different parts of the organization.

Allows for benchmarking performance across different organizational divisions.

Found in the invoice header or financial accounting document header.

This attribute records when the open item was closed in the system, typically through the posting of a payment or a credit memo. It signifies the end of the collection lifecycle for that invoice.

This date is mathematically critical for calculating the actual cycle time and Days Sales Outstanding (DSO). It helps distinguish between open and closed invoices and is used to measure the efficiency of the cash application process.

Essential for calculating cycle time and actual Days Sales Outstanding.

Found in the accounting document line item or status history.

This attribute identifies the debtor associated with the invoice. It allows for the aggregation of data at the account level.

This is essential for analyzing payment behaviors of specific clients. It enables the identification of strategic customers who frequently pay late or dispute invoices, allowing for targeted relationship management and customized collection approaches.

Enables account-level analysis and identification of problematic payers.

Found in customer master data tables joined with the invoice header.

This attribute captures the classification for why a customer is refusing to pay part or all of an invoice. Common reasons include pricing errors, damaged goods, or missing documentation.

This data is vital for root cause analysis. By aggregating dispute reasons, organizations can identify systemic issues in upstream processes (like fulfillment or pricing) that are causing payment delays and administrative rework.

Key for identifying root causes of payment delays and rework.

Found in dispute management modules or specific dispute case tables.

This attribute specifies the deadline for payment as agreed upon in the terms of sale. It is the baseline for determining if an invoice is current, past due, or delinquent.

Analysts use this date to calculate early or late payment metrics. Comparing the actual payment date against the due date reveals customer compliance behavior and the effectiveness of collection strategies.

The baseline for calculating aging, delinquency, and payment compliance.

Found in the invoice header or calculated from baseline date plus payment terms.

This attribute represents the financial value of the claim against the customer. It typically reflects the gross amount including taxes and surcharges.

In analysis, this field is fundamental for calculating financial KPIs such as Days Sales Outstanding (DSO) weighted by value, identifying high-value accounts at risk, and prioritizing collection efforts. It enables the segmentation of processes based on financial impact.

Critical for financial impact analysis and prioritizing high-value collections.

Found in the invoice header or accounting document header.

This boolean attribute distinguishes between manual user actions and system-driven events. It is often derived by checking the User ID against a list of known service accounts.

This is the primary driver for calculating automation rates. It helps organizations understand the return on investment for robotic process automation (RPA) and identify opportunities to automate repetitive manual tasks like cash application or dunning.

Crucial for measuring digital transformation and process automation rates.

Derived from User ID or specific transaction flags.

This attribute contains the code or description of the payment schedule, such as Net 30, Net 60, or Immediate. It defines the contractual expectation for cash flow.

Analyzing this attribute helps identify if standard terms are being adhered to or if sales teams are granting unfavorable terms to close deals. It is also used to correlate payment terms with actual payment behavior to check for term compliance.

Provides context for the Due Date and helps analyze term compliance.

Found in the invoice header or customer master data.

This attribute identifies the specific employee or team assigned to manage the customer account and ensure payment. It links human resources to process outcomes.

This allows for the analysis of collector productivity and effectiveness. Managers can compare collection rates, promise-to-pay fulfillment, and workload across different collectors to identify training needs or rebalance portfolios.

Enables workforce productivity analysis and performance benchmarking.

Found in the customer master record or specific collection management tables.

This attribute groups customers into categories such as Key Accounts, SMB, High Risk, or Government. It is usually defined in the master data.

Segmenting process analysis by this attribute reveals how different customer types are treated. It helps verify if high-value customers are receiving prioritized service or if high-risk customers are being monitored closely enough.

Allows for strategy analysis across different customer tiers.

Found in customer master data or business partner tables.