Your KYC Customer Onboarding Data Template

This attribute records the name of each step or activity that occurs during the KYC process, such as 'Application Submitted', 'Risk Assessment Performed', or 'Application Approved'. It provides the sequential building blocks of the process map.

Analyzing the Activity Name allows for the visualization of the process flow, identification of frequent or rare activities, and detection of bottlenecks or rework loops. It is a cornerstone for understanding what actions are being performed and in what order, which is crucial for variant analysis and compliance checks.

It defines the steps in the process map, allowing for visualization of the process flow, identification of deviations, and analysis of activity frequency and sequence.

This information is typically found in an event log table within ACTICO, often in a field describing the event or task type. Consult ACTICO documentation.

The Customer Application is the primary case identifier that groups all events and activities related to a single customer's onboarding journey. It represents one complete instance of the KYC process, from the initial submission to the final decision of approval or rejection.

In process mining, this attribute is essential for reconstructing the end-to-end journey of each application. It allows analysts to track the sequence of activities, measure total cycle times, and compare different process paths, or variants, taken by various applications. All events sharing the same Customer Application ID are considered part of the same case.

This is the fundamental attribute for process mining, as it connects all related events into a single, cohesive process instance, enabling end-to-end analysis of each customer's onboarding experience.

This is a primary key in the main application or case management tables within ACTICO. Consult ACTICO documentation for specific table and field names.

Event Time is the precise date and time that an activity was recorded in the system. It provides the chronological order for all events within a single customer application case, forming the timeline of the onboarding journey.

This attribute is critical for all time-based analysis. It is used to calculate cycle times between activities, identify delays and waiting times, measure overall case duration, and check for compliance with Service Level Agreements (SLAs). The sequence of these timestamps for a given case allows process mining tools to reconstruct the exact process flow as it happened.

This attribute provides the chronological order of events, which is essential for calculating durations, discovering bottlenecks, and analyzing the process timeline.

Located in the event log tables of ACTICO, this is the timestamp associated with each recorded activity. Consult ACTICO documentation.

This attribute records the date and time of the most recent data pull from ACTICO. It is a metadata field that applies to the entire dataset, rather than individual events, providing context on the freshness of the analysis.

In dashboards and reports, this information is vital for users to understand how current the data is. It helps manage expectations about the timeliness of insights and is crucial for operational monitoring where near real-time data is important. Displaying this timestamp ensures transparency and trust in the data being presented.

Indicates the freshness of the data, allowing users to understand if they are analyzing up-to-date information, which is critical for operational decision-making.

This value is generated and stored during the data extraction, transformation, and loading (ETL) process. It reflects the timestamp when the ETL job was successfully completed.

This attribute identifies the source information system where the data was generated. For this process, it will consistently be 'ACTICO', but in broader analyses involving multiple systems, it helps differentiate data origins.

In a process mining context, it is crucial for data governance and validation. It ensures that data is correctly attributed to its source, which is important when merging data from different systems to create a holistic process view. It also helps in troubleshooting data quality issues by tracing them back to the originating system.

Provides essential context about data origin, ensuring data lineage and governance, which is critical when combining data from multiple sources.

This is typically a static value ('ACTICO') added during the data extraction and transformation process to label the dataset.

This attribute indicates the final disposition of a case, typically 'Approved' or 'Rejected'. It can also show the status of in-progress cases. This is a critical dimension for outcome-based analysis.

Understanding why applications are approved or rejected is a key goal of KYC process mining. This attribute allows for filtering the process map to see the typical journeys of approved versus rejected applications, helping to identify process patterns that lead to undesirable outcomes. It is also the basis for calculating the 'Application Rejection Rate' KPI.

Defines the outcome of each case, enabling comparative analysis between successful and unsuccessful process instances and the calculation of rejection rates.

This is a case-level attribute, often found in the main case or application table in ACTICO. It reflects the final status of the application.

This attribute assigns an activity to a specific organizational unit, such as 'Compliance', 'Onboarding Team', or 'Client Relations'. It provides an organizational context to the process flow.

Department-level analysis is crucial for understanding how work is handed off between different parts of the organization. It helps identify inter-departmental bottlenecks, measure departmental efficiency, and analyze resource allocation across teams. Dashboards can be filtered by department to provide managers with a view of their team's specific performance.

Provides an organizational dimension for analysis, allowing for the identification of cross-departmental delays and the evaluation of team-level performance.

This information might be stored directly with the event data or derived by joining user data with an HR master data table that maps users to departments. Consult ACTICO documentation.

The End Time marks the completion of an activity. Paired with the Start Time (EventTime), it allows for the calculation of the precise duration of each task, known as processing time. Not all events have a distinct end time, as some can be instantaneous.

This attribute is fundamental for performance analysis, particularly for measuring how long each step takes. It enables the creation of detailed performance dashboards, helps identify which activities consume the most time, and is essential for calculating KPIs like 'Average Document Review Time'.

Enables the calculation of precise activity durations (processing time), which is crucial for identifying performance bottlenecks and analyzing resource efficiency.

Like the start time, this is typically found in the event log tables of ACTICO. Some systems store start and end times in separate columns for a single event record. Consult ACTICO documentation.

This attribute identifies the specific user or system agent responsible for executing an activity. It links process steps to the individuals or teams who performed them.

Analyzing performance by user is a common requirement. This attribute allows for the creation of dashboards that show workload distribution, individual processing times, and performance comparisons between users or teams. It helps identify high-performing individuals as well as those who may require additional training, and it is key for understanding resource allocation and utilization.

Links process activities to specific users, enabling performance analysis by individual or team and helping to identify training needs or resource imbalances.

Typically stored alongside each event in ACTICO's event log or transaction history tables. Consult ACTICO documentation.

When an application's final status is 'Rejected', this attribute provides the underlying cause. Examples include 'Incomplete Documentation', 'Failed Background Check', or 'High Risk Profile'.

This is a vital attribute for root cause analysis. By analyzing the frequency of different rejection reasons, the business can identify systemic issues in the process or with customer submissions. For example, a high number of rejections due to incomplete documentation might indicate that the application instructions are unclear. This directly supports the 'Application Rejection Rate and Reasons' dashboard.

Provides the 'why' behind application rejections, enabling root cause analysis to reduce the rejection rate and improve process efficiency.

Usually found in the main case or application table in ACTICO, often populated only when the Application Status is 'Rejected'.

This attribute represents the assessed risk category of a customer, which often determines the complexity and rigor of the subsequent KYC process. A high-risk customer may require additional checks and approvals compared to a low-risk one.

In process mining, Risk Level is a powerful dimension for comparative analysis. It allows analysts to check if the process correctly follows different paths based on risk, as intended. For example, one can verify that all high-risk customers undergo an enhanced due diligence step. It is key for the 'Risk Assessment Process Flows' dashboard.

Allows for segmentation of cases based on risk, enabling analysis of whether the process correctly adapts to different risk profiles as required by compliance policies.

This is a key data point stored at the case level in the main application table within ACTICO.

The SLA Target Date is the deadline for completing the onboarding of a customer, as defined by internal service level agreements. This date is often calculated based on the application submission date plus a standard processing time.

This attribute is essential for monitoring SLA compliance. By comparing the actual completion date of a case to its SLA Target Date, the system can determine if the case was completed on time or breached the SLA. This is the basis for the 'Onboarding SLA Performance' dashboard and the 'Onboarding SLA Adherence Rate' KPI.

Provides the benchmark for measuring on-time performance, enabling direct monitoring and reporting of SLA compliance.

This may be stored as a field in the main case table in ACTICO or could be derived based on business rules (e.g., Submission Date + 5 business days).

This attribute specifies the customer's country, which can have a significant impact on the KYC process. Different jurisdictions have different regulatory requirements, which may trigger additional or alternative process steps.

Analyzing the process by country allows for a comparison of performance across different regions. It can help identify if certain countries consistently experience longer cycle times or higher rejection rates, potentially indicating regulatory friction or specific market challenges. This geographical view is important for global organizations aiming to standardize processes while respecting local compliance needs.

Provides a geographical dimension for analysis, helping to understand process variations and performance differences across various regulatory jurisdictions.

This is a core piece of customer information stored at the case or customer level within the ACTICO system.

This attribute classifies the applicant into different categories, for example, an individual person versus a corporate entity. The KYC process often differs significantly between these types, with corporate onboarding being much more complex.

Using Customer Type as a dimension allows for a clear separation and comparison of these distinct processes within the same dataset. Analysts can filter the process map to view only 'Corporate' customers to understand their unique challenges, bottlenecks, and cycle times, without the data being skewed by the much simpler 'Individual' customer process.

Enables segmentation of the process for different customer categories, which often have vastly different process flows and complexities, leading to more accurate analysis.

This is a fundamental attribute stored at the case or customer level within ACTICO.

This boolean attribute distinguishes between tasks executed by a human user and those performed by system automation, such as an automated background check or risk scoring.

Analyzing this attribute helps in evaluating the effectiveness of automation initiatives. It allows for a comparison of processing times between automated and manual steps, identifies which parts of the process are still heavily manual, and can highlight opportunities for further automation to improve efficiency and reduce operational costs.

Distinguishes between human and system tasks, which is crucial for measuring the impact of automation and identifying opportunities for future efficiency gains.

This can be inferred from the 'InitiatingUser' attribute (e.g., if the user is 'SYSTEM') or it might be a dedicated flag in the event log. Consult ACTICO documentation.

This boolean attribute flags activities that are being performed for a second or subsequent time within the same case, such as a 'Document Review' that happens after a request for more information. It identifies instances of rework, which are typically a source of process inefficiency.

Identifying rework is a primary goal of process mining. This flag allows for the quantification of rework, such as calculating the 'Document Rework Rate' KPI. In the process map, rework loops can be highlighted to show where the process is cycling back on itself. This helps pinpoint quality issues or areas where the process is not 'right first time'.

Highlights instances of repeated work, allowing for the direct measurement of process inefficiency and the identification of activities with quality or clarity issues.

This is a calculated attribute. The logic is defined within the process mining tool or during data transformation to detect repeated activities within the same case.

Processing Time is the calculated duration from an activity's start time to its end time. This metric represents the 'touch time' or the active work time, as opposed to waiting time between activities.

This is a critical KPI for measuring operational efficiency. High processing times for certain activities are clear indicators of bottlenecks or overly complex tasks. By aggregating this metric, managers can identify which steps consume the most resources and prioritize them for process improvement or automation initiatives. It is a fundamental component of performance dashboards.

Measures the active work duration of a task, helping to pinpoint inefficient activities that are consuming the most time and resources.

This is a calculated metric, derived by subtracting the 'EventTime' (StartTime) from the 'EndTime' for each activity.

This attribute provides a categorical assessment of a case's performance against its Service Level Agreement. It is derived by comparing the case's completion time (or current time for open cases) against the 'SlaTargetDate'.

This attribute simplifies SLA reporting by converting date comparisons into an easy-to-understand status. Dashboards can use this for clear visualizations like pie charts or gauges showing the percentage of cases that are 'Met' versus 'Breached'. It is a key element for the 'Onboarding SLA Performance' dashboard and directly supports the 'Onboarding SLA Adherence Rate' KPI.

Provides a clear, categorical status of SLA compliance for each case, simplifying reporting and allowing for easy visualization of performance against targets.

This is a calculated attribute derived from business logic that compares the case completion timestamp with the 'SlaTargetDate'.