Your Payments Processing Data Template

This attribute describes the action taken or the state change that the payment has undergone at a specific point in time. Examples include the creation of the request, validation checks, authorization steps, or final settlement.

Process mining relies on this attribute to define the nodes in the process map. By analyzing the sequence of these activities, analysts can identify common variants, loops where payments are reworked, and bottlenecks where payments sit for extended periods.

Standardizing these names across different source systems is often necessary to create a coherent view of the process. For example, one system might call a step 'Auth' while another calls it 'Authorization', and these should be aligned during data transformation.

It defines the nodes in the process map and allows for the analysis of process flow and variants.

Found in audit logs, status history tables, or event tracking tables.

This attribute records the exact moment an event took place within the payment system. It acts as the chronological anchor for the process, ensuring that events are ordered correctly within the case.

In analysis, this timestamp is the basis for all time-based calculations. It is used to determine the duration between activities, the total end-to-end cycle time, and adherence to service level agreements. Accurate timestamps are vital for identifying when bottlenecks occur.

High precision is preferred, especially for high-frequency trading or automated payment systems where milliseconds matter. If only dates are available without times, the ordering of activities that happen on the same day may require secondary sorting logic.

Essential for ordering events and calculating all duration-based KPIs like Cycle Time.

Found in transaction logs, history tables, or system audit trails.

This attribute tracks the freshness of the data used in the analysis. It reflects the time when the data was loaded into the process mining tool or when the record was last modified in the source database.

Having this information is vital for data governance and trust. It allows analysts to know if they are looking at real-time data or a snapshot from the previous day. This is particularly important for monitoring active payments that may be stuck in a pending state.

While not used directly for process flow calculations, it serves as a metadata control field to ensure that dashboards are displaying the most current status of the payment operations.

Ensures data freshness and helps in debugging data pipeline latency issues.

Generated during the data extraction or ETL process.

This attribute serves as the central key for linking all activities within a single payment lifecycle. It allows process mining tools to reconstruct the end-to-end journey of a payment from initiation to final settlement or failure.

In analysis, this identifier is used to group distinct events into a single case instance. It enables the visualization of the process flow and is critical for calculating cycle times per transaction. Without a unique ID, it is impossible to distinguish between thousands of simultaneous payments moving through the system.

This field typically remains constant throughout the lifespan of the payment. However, in complex scenarios involving multiple systems, this may need to be a composite key or mapped from a unique end-to-end reference number.

It is the fundamental Case ID required to create a process model and track specific payments.

Typically found in the transaction header, payment instruction table, or main ledger log.

This attribute identifies the technical origin of the record. In end-to-end payment processes, data often flows through multiple systems, such as a front-end gateway, a fraud detection engine, and a back-end ledger.

Analyzing data by system allows users to isolate technical issues. For instance, if delays are consistently observed in the fraud engine but not the ledger, the root cause analysis can be targeted effectively. It also helps in validating data integrity when merging multiple data sources.

This field is typically added during the extraction and transformation process if it does not explicitly exist in the source data. It serves as a lineage marker for auditing and debugging purposes.

Crucial for multi-system analysis and identifying which component is causing delays or errors.

Often hardcoded during the ETL process or found in system metadata.

This attribute specifies the currency unit for the payment amount, such as USD, EUR, or GBP. It is critical for accurate financial reporting and for triggering specific cross-border workflows.

Analysis often requires filtering by currency to understand regional performance or FX processing times. Different currencies may have different cut-off times, settlement cycles, and regulatory requirements, which directly influence the process flow.

Without this attribute, the 'Payment Amount' field is ambiguous. This field enables the conversion of disparate amounts into a single reporting currency for global dashboarding.

Necessary for normalizing financial values and identifying cross-border process variations.

Found alongside the payment amount in transaction tables.

This attribute captures the technical or business reason for a process failure. It is populated when a payment is rejected, fails validation, or encounters a transmission error.

Analyzing error codes is the primary method for reducing the 'Payment Failure Rate' and 'Rework Rate'. Grouping common error codes helps identify systemic issues, such as master data inaccuracies or technical connectivity problems with external clearing houses.

In happy path scenarios, this field is usually null. Its presence often indicates a deviation from the ideal process flow and triggers exception handling sub-processes.

The primary attribute for Root Cause Analysis of failures and rework.

Found in error logs, rejection messages, or response payloads.

This attribute represents the financial value that is being transferred. It is the primary numeric metric for sizing the impact of process inefficiencies. For example, a delay in a million-dollar payment is often more critical than a delay in a ten-dollar payment.

In analysis, this field is used to aggregate volumes, calculate total liquidity requirements, and segment payments by value bands. High-value payments often follow different approval workflows compared to low-value ones, and this attribute helps differentiate those paths.

It is essential to pair this attribute with the currency code to ensure apples-to-apples comparisons. Aggregating amounts without currency conversion or separation can lead to misleading financial reporting.

Allows for financial impact analysis and segmentation of high-value vs low-value transactions.

Found in the transaction details or financial posting tables.

This attribute represents the target deadline for the payment. It allows the system to measure 'On-Time Payment Rate' and determining if service level agreements (SLAs) were met.

Comparing the actual completion timestamp against this due date provides a clear metric for process performance. Payments completed after this date are considered late, which may incur penalties or damage business relationships.

This field is particularly relevant for accounts payable processes or guaranteed service delivery contracts where timing is a contractual obligation.

Required for calculating SLA adherence and On-Time Payment rates.

Found in the invoice header or payment request instruction.

This attribute categorizes the payment by its execution type, such as Wire Transfer, ACH, Credit Card, or Instant Payment. Each method typically follows a distinct process path with different timing expectations and costs.

By segmenting data using this attribute, analysts can compare the performance of different payment rails. For instance, wire transfers might require more manual approval steps compared to automated ACH batches.

Understanding the mix of payment methods helps in capacity planning and identifying shifts in customer behavior, such as a migration from traditional checks to digital instant payments.

Critical for distinguishing between process variants (e.g., Instant vs Wire) which have different SLAs.

Found in the payment instruction details.

This attribute identifies who or what executed a specific step in the payment process. It can refer to a human user performing a manual review, or a system account executing an automated task.

This data is vital for analyzing resource utilization and bottlenecks. It helps distinguish between automated processing (Straight-Through Processing) and manual interventions. High rates of manual user involvement often correlate with higher costs and slower cycle times.

For compliance, this field helps in segregation of duties analysis, ensuring that the person who created a payment is not the same person who approved it.

Enables analysis of automation rates (STP) and resource productivity.

Found in audit logs or metadata columns of the transaction table.

This attribute identifies the payee. In a B2B context, this is the vendor; in a P2P context, it is the individual recipient. It provides context for who is being paid.

Analyzing payments by beneficiary can reveal patterns such as frequent payments to high-risk entities or concentration risk with specific vendors. It is also useful for fraud analysis, identifying if multiple small payments are being funneled to a single unexpected beneficiary.

Data quality issues are common here, with variations in spelling (e.g., 'Inc.' vs 'Incorporated'). Cleaning this data is often necessary for accurate aggregation.

Useful for vendor analysis, fraud detection, and risk profiling.

Found in the beneficiary details section of the payment instruction.

This attribute indicates the entry point of the payment instruction, such as Mobile App, Web Portal, API, or File Upload. It provides insight into customer behavior and channel usage.

Analyzing process performance by channel can highlight technical disparities. For example, payments initiated via API might be processed instantly, while file uploads might wait for batch processing windows. This helps in understanding the user experience across different platforms.

It is also useful for analyzing the shift from legacy channels (like manual entry or fax) to digital channels, supporting digital transformation initiatives.

Helps analyze volume trends and performance differences across entry points (e.g., Mobile vs Web).

Found in the transaction header or session metadata.

This attribute is a value generated by fraud detection engines or risk models. A higher score typically indicates a higher probability that the transaction is fraudulent or high-risk.

In process analysis, this score helps explain why certain payments go through extended review loops. Payments with high risk scores often trigger manual intervention activities, increasing the cycle time. Correlating risk scores with final outcomes (approved vs rejected) helps tune the efficiency of the risk rules.

Not all systems generate a numeric score; some may only provide a status flag. However, for modern payment gateways, this is a standard metric for decisioning.

Explains process deviations such as manual reviews and holds due to fraud checks.

Output from the fraud detection system or risk engine.