Your Loan Origination Data Template

This attribute represents a single task or milestone in the loan application lifecycle, such as 'Initial Review Performed' or 'Underwriting Completed'. These activities form the nodes in the process map, and their sequence defines the process flow.

Analyzing the sequence, frequency, and duration of these activities helps identify common process paths, deviations, and bottlenecks. It is crucial for understanding what work is being done and in what order, forming the basis of all process mining analysis.

This attribute defines the steps of the process, allowing for the visualization and analysis of the process flow, which is the core of process mining.

This is typically derived from changes in the 'Stage' or 'Status' fields on the Loan object in nCino, or from related Task or Event records.

The Loan Application ID uniquely identifies each individual loan request throughout its entire lifecycle. It serves as the central entity to group all associated activities and data, allowing for a complete trace of the origination journey for a specific loan.

In process mining, this ID acts as the Case ID. Every event, from submission to the final decision, is linked back to this identifier. This enables the reconstruction of the end-to-end process flow for each application, which is fundamental for cycle time analysis, variant analysis, and bottleneck identification.

This is the essential Case ID that connects all process steps, making it possible to analyze the end-to-end journey of each loan application.

This is typically the primary key of the Loan object in nCino, often referenced as LLC_Loan__c.Name or a similar unique identifier field.

The Start Time, or event timestamp, records the exact date and time an activity occurred. This chronological data is essential for ordering events correctly and reconstructing the process flow as it happened in reality.

This timestamp is the foundation for all time-based analysis. It is used to calculate cycle times between activities, identify waiting times and delays, and measure overall case duration. Accurate timestamps are critical for performance monitoring and bottleneck analysis.

This timestamp provides the chronological order of events, which is essential for calculating all performance metrics like cycle times and durations.

This can be sourced from system-generated timestamps like 'CreatedDate' on related records or the timestamp of a specific field change in the field history.

This attribute records the date and time of the last data extraction from the source system. It provides context for the freshness of the data being analyzed.

In dashboards and reports, this information is critical for users to understand how current the analysis is. It helps manage expectations and ensures that decisions are made based on an understood timeframe of the underlying data.

Provides crucial context on data freshness, ensuring users understand how up-to-date the process analysis is.

This timestamp is generated by the data extraction tool or ETL process at the time of the data pull.

This attribute identifies the source application where the event data originated. For this process, it will typically be 'nCino'.

While it may seem redundant if all data comes from one system, it is crucial for data governance and becomes vital when integrating data from multiple systems, such as a separate credit scoring engine or document management platform. It ensures clarity on data provenance.

Identifies the origin of the data, which is crucial for data governance, troubleshooting, and when combining data from multiple systems.

This is a static value, 'nCino', applied during the data extraction and transformation process.

This attribute identifies the submission source of the loan application. Different channels can have varying levels of data completeness and customer interaction, which may impact the subsequent process flow.

Analyzing the process by channel helps to evaluate channel efficiency. For example, one could compare the cycle times and rework rates of applications submitted online versus those submitted in a branch. These insights can inform strategic decisions about channel investment and process optimization for specific sources.

Helps compare the efficiency, speed, and quality of different submission channels, guiding strategic improvements and investments.

This is typically stored in a picklist field on the Loan object, often named 'Channel' or 'Source'.

This attribute indicates the functional group, like 'Origination', 'Underwriting', or 'Closing', that owns the activity. This may be derived from the assigned user's profile or a dedicated field on the loan record.

Analyzing the process from a departmental view is critical for understanding cross-functional collaboration. It helps measure handoff times between departments, identifies delays caused by inter-departmental transfers, and provides a high-level view of where work is being performed in the organization.

Crucial for analyzing inter-departmental handoffs, measuring team-level performance, and identifying cross-functional bottlenecks.

This information can be derived from the assigned user's Department field on their User record, or from a custom 'Team' or 'Queue' assignment field on the Loan object.

This attribute identifies the individual employee who executed a specific task or is assigned to the loan application at a given stage. This is often tracked as the record 'Owner' in nCino.

Analyzing data by user helps in understanding workload distribution, individual performance, and resource utilization. It can highlight overburdened employees or teams and identify top performers, which is valuable for resource management and training.

This enables analysis of workload distribution, team performance, and resource allocation, helping to identify bottlenecks related to specific users or teams.

Typically from the 'OwnerId' field on the Loan object or related Task objects, which can be joined with the User object to get the user's name.

This attribute captures the result of the 'Loan Decision Rendered' activity. It is a critical case-level attribute that defines the final state of a completed loan application process.

This attribute is essential for outcome-based analysis. It allows for comparing process flows that lead to different outcomes, for example, analyzing the common paths of approved versus declined applications. It is also the basis for calculating key metrics like the Loan Decline Rate.

Allows for filtering and comparing processes based on their final business outcome, which is key for identifying what process patterns lead to success or failure.

Typically found in a 'Status' or 'Stage' field on the Loan object once the process has concluded. The specific values are configuration-dependent.

The End Time marks the completion of an activity. The difference between the Start Time and End Time of an activity represents its processing time.

This attribute is essential for distinguishing between processing time (active work) and waiting time (idle time between activities). Analyzing processing times helps identify which specific steps are most time-consuming, while the gap between one activity's End Time and the next one's Start Time reveals handoff delays.

Enables the calculation of precise activity processing times, which is key to differentiating active work time from idle waiting time.

This can be sourced from system-generated timestamps like 'LastModifiedDate' on related records, or the timestamp of a subsequent field change in the field history.

This attribute represents the principal amount of the loan being applied for. The loan amount can significantly influence the complexity and duration of the origination process, with larger or more complex loans often requiring additional scrutiny or steps.

In analysis, Loan Amount is a powerful tool for segmentation. It allows for investigating whether higher-value loans take longer to process, have higher rework rates, or follow different process paths. It is also fundamental for financial reporting and understanding the value of the loan portfolio being processed.

Provides key financial context and allows for segmenting analysis to see if loan value correlates with process complexity, duration, or outcomes.

Located in a currency field on the Loan object, often named 'Amount' (LLC_Loan__c.LLC_BI__Amount__c).

This attribute categorizes the loan application based on the financial product being offered. Different loan products often have distinct process variations, cycle times, and risk profiles.

Segmenting the process analysis by Loan Product Type is crucial for a meaningful comparison. It helps to understand how different products affect process efficiency and allows for the creation of tailored dashboards, such as tracking disbursement throughput for mortgages versus auto loans. This ensures that performance is evaluated in the correct business context.

Enables process analysis to be segmented by business context, revealing how different products impact process efficiency, duration, and pathways.

This is typically stored in a 'Product' or 'Loan Type' field on the nCino Loan object (LLC_Loan__c).

This metric represents the elapsed time from an activity's Start Time to its End Time. It measures the 'touch time' or active work period for a given process step.

This calculated attribute is vital for performance analysis. It helps distinguish active work time from idle or wait time, allowing analysts to pinpoint which specific tasks are the most time-consuming, as opposed to identifying long delays between steps. It is a key input for resource capacity planning and efficiency improvements.

Measures the active work duration for each step, helping to identify the most time-consuming tasks and differentiate them from waiting time.

This is calculated by taking the difference between the 'EventEndTime' and the 'EventTime' (StartTime) for each activity.

This calculated metric measures the end-to-end cycle time for each loan application. It represents the total time a case is active in the process.

Case Duration is a primary key performance indicator for overall process efficiency. It is used in dashboards to visualize the distribution of cycle times, identify long-running cases, and track performance trends over time. Analyzing the factors that correlate with longer case durations is a key goal of many process mining projects.

Represents the overall end-to-end cycle time, a critical KPI for measuring overall process health and customer experience.

This is calculated by taking the difference between the maximum and minimum EventTime for each LoanApplicationId.

This attribute holds the numerical credit score returned from a credit bureau during the 'Credit Check Completed' activity. It is a key factor in the underwriting and decision-making process.

In process mining, the credit score can be used to analyze if there is a correlation between the score and process behavior. For instance, do applications with lower credit scores require more manual steps, take longer in underwriting, or have a higher decline rate? This can provide valuable insights into risk-based process variations.

Allows for analysis of how a key decision-making factor, the credit score, correlates with process paths, durations, and outcomes.

This data is often stored in a dedicated field on a related 'Credit Report' or 'Financials' object linked to the main Loan object.

When a loan's 'Decision Outcome' is 'Declined', this attribute provides further detail on the cause. Reasons could include poor credit score, insufficient income, or incomplete documentation.

Analyzing decline reasons is crucial for business improvement. It can reveal trends that point to issues with lending criteria, product-market fit, or problems in the application process itself. These insights can help the business refine its strategies and improve approval rates.

Provides the root cause for declined applications, enabling analysis to identify trends and improve lending criteria or application processes.

Typically stored in a picklist or text field on the Loan object that becomes mandatory when the loan is moved to a 'Declined' status.

This flag distinguishes between human-driven tasks and automated system events. For example, a credit check might be an automated activity, while an underwriting decision is typically manual.

Identifying automated steps is crucial for automation discovery and analysis. It allows for measuring the impact of automation on cycle times and efficiency, and helps identify manual activities that are prime candidates for future automation initiatives.

Distinguishes between system and human activities, which is fundamental for measuring the impact of automation and identifying new automation opportunities.

This is typically derived based on the user associated with the event. If the user is a 'System' or 'Integration' user, the flag is set to true.

This boolean attribute is set to true if an activity, like 'Supporting Documents Requested', occurs more than once for the same loan application. It identifies instances of rework, which are often a source of process inefficiency.

Analyzing rework is a core process mining use case. The 'IsRework' flag allows for easy filtering and quantification of rework instances. It helps power dashboards like the 'Loan Application Rework Analysis' and is used to calculate KPIs such as the rework rate, highlighting areas where process steps are failing on the first pass.

Highlights process inefficiencies by flagging repeated work, allowing for targeted analysis to understand and reduce costly loops.

This attribute is calculated during data transformation by using window functions to check if the same activity has already occurred for a given Case ID.

This attribute provides the outcome of an SLA check, typically for a specific stage like underwriting. It compares the actual cycle time against the predefined SLA target and assigns a status like 'Met' or 'Breached'.

This attribute is essential for performance monitoring against business commitments. It directly supports the 'Underwriting SLA Adherence' dashboard and KPI, providing a clear and immediate signal of compliance. It allows for drilling down into the characteristics of breached applications to understand the root causes of delays.

Provides a clear, categorical outcome of performance against targets, making it easy to monitor SLA compliance and analyze the causes of breaches.

This is calculated in the data transformation layer by comparing the actual duration of a process segment (e.g., underwriting cycle time) with the 'UnderwritingSlaTarget' attribute.

This attribute defines the expected Service Level Agreement (SLA) for the underwriting part of the process. It serves as a benchmark against which the actual performance is measured.

This target is used to calculate SLA adherence rates. By comparing the actual underwriting cycle time to this target, applications can be flagged as 'Met' or 'Breached'. This is essential for the 'Underwriting SLA Adherence' dashboard and related KPIs.

Provides the benchmark needed to measure performance against service level agreements, enabling SLA adherence monitoring.

This value may be stored on the Loan object or derived from business rules based on attributes like Loan Product Type or Loan Amount.