Your Loan Origination Data Template

This attribute records the name of each step in the loan origination lifecycle, such as 'Application Submitted', 'Credit Check Completed', or 'Loan Decision Rendered'. These activities form the nodes of the discovered process map.

Analyzing the sequence and frequency of these activities is the core of process mining. It helps to understand the actual process flow, identify deviations from the standard procedure, and pinpoint bottlenecks or areas with high rework rates. The specific activity names are crucial for calculating stage-specific KPIs like Underwriting Processing Time.

Activities define the steps of the process. Analyzing their sequence, frequency, and duration is fundamental to understanding and improving the process flow.

This information is typically generated from system event logs, status change records, or task completion entries within Finastra Fusion Mortgagebot.

The Event Timestamp, or Start Time, captures the precise moment an activity took place. This temporal data is essential for ordering events chronologically and building an accurate representation of the process flow.

In analysis, timestamps are used to calculate all duration-based metrics, including overall cycle time, processing times for individual activities, and waiting times between steps. This allows for the identification of bottlenecks, monitoring SLA adherence, and tracking performance trends over time, which are critical for dashboards like 'Overall Loan Processing Time Analysis' and 'Loan Approval Cycle Time Trend'.

Timestamps provide the chronological order of events and are essential for calculating all performance metrics, such as cycle times and wait times.

This data is usually found alongside the activity or status update record in the system's event logs or transaction tables, often labeled as 'Creation Date' or 'Status Timestamp'.

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 analysis, this ID is fundamental for constructing the end-to-end view of each case. All activities, from 'Application Submitted' to 'Funds Disbursed' or 'Loan Closed as Rejected', are linked back to this single identifier, enabling the calculation of cycle times, identification of process variants, and tracking of specific loan journeys.

This is the essential Case ID that connects all related events into a single process instance, which is the foundation for any process mining analysis.

This is the primary identifier for a loan application within Finastra Fusion Mortgagebot, typically available in all modules and tables related to loan processing.

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

In any analysis or dashboard, knowing the recency of the data is critical for making informed decisions. This timestamp helps users understand if they are looking at real-time information or a snapshot from a specific point in time, which is important for operational monitoring versus strategic analysis.

Informs users about the timeliness of the data, ensuring that analyses are based on data of a known and acceptable age.

This value is generated and stamped on the dataset during the data extraction and transformation (ETL) process.

This attribute specifies the originating system for the data, for example, 'Finastra Fusion Mortgagebot'. In environments with multiple integrated systems, this field helps distinguish data sources.

While often a static value in a single-system analysis, it becomes crucial when merging data from different platforms, like a separate CRM or document management system. It ensures data lineage is clear and helps in troubleshooting data quality issues by tracing them back to the correct source.

Provides crucial context about data origin, ensuring traceability and aiding in data governance, especially in multi-system environments.

This is typically a static value added during the data extraction and transformation (ETL) process, identifying 'Finastra Fusion Mortgagebot' as the source.

This attribute specifies the origin of the application, for example, whether it came through an online portal, a mobile app, directly via a loan officer, or through a third-party broker.

Understanding the channel is important for operational and strategic analysis. The 'Overall Loan Processing Time Analysis' dashboard uses this attribute to compare the efficiency of different channels. This can reveal if certain channels lead to longer processing times, higher rejection rates, or require more rework, providing insights for process optimization and marketing strategy.

Allows for performance comparison across different customer-facing channels, helping to optimize channel-specific processes and resource allocation.

Consult Finastra Fusion Mortgagebot documentation. This information is often captured at the time of application creation.

This attribute identifies the primary employee, the loan officer, who is handling the application. This person is often the main point of contact for the applicant and is responsible for guiding the application through the process.

This dimension is critical for performance and workload analysis. Dashboards like the 'Loan Officer Workload Distribution' directly use this attribute to visualize the number of active loans and average processing times per officer. It helps management identify top performers, those who may be overloaded, and opportunities for resource balancing or training.

Enables analysis of workload distribution, resource performance, and identification of resource-related bottlenecks or best practices.

Consult Finastra Fusion Mortgagebot documentation. This is likely stored in loan application header data or an assignment table, linked to the user management module.

This attribute specifically identifies the underwriter responsible for the critical underwriting stage of the process. The underwriter evaluates the loan's risk and makes a recommendation for approval or rejection.

For the 'Underwriting Bottleneck Deep Dive' dashboard, this attribute is essential. It allows for the segmentation of underwriting processing times and queue times by individual underwriter. This helps to understand performance variations, ensure consistency in decision-making, and manage workload within the underwriting team.

Crucial for detailed analysis of the underwriting stage, enabling performance comparisons and workload management for the underwriting team.

Consult Finastra Fusion Mortgagebot documentation. This information is typically recorded when the case is assigned to the underwriting queue or a specific underwriter.

This attribute captures the final business outcome of the loan origination process for each application. This is a critical case-level attribute that defines the success or failure of a process instance.

This attribute is the foundation for the 'Loan Application Rejection Trends' dashboard and the 'Loan Decision Consistency by Risk' analysis. By filtering and segmenting the process map by outcome, analysts can identify common process patterns that lead to rejections versus approvals. It directly supports the calculation of the Loan Application Rejection Rate KPI.

Defines the business outcome of each case, allowing for comparative analysis between successful and unsuccessful process flows to find root causes.

This is typically the final status of a loan application case, found in the main case or application table within Finastra Fusion Mortgagebot.

While StartTime captures when an activity begins, EndTime marks its completion. For many events in loan origination, the start and end time might be identical, representing an instantaneous event. However, for activities with a measurable duration like 'Underwriting Review', this field is crucial.

Having a distinct EndTime allows for the direct calculation of activity processing time, as opposed to inferring it from the start time of the next activity. This is more accurate as it separates true processing time from idle or wait time, which is essential for the 'Underwriting Bottleneck Deep Dive' dashboard.

Enables the precise calculation of activity processing time, distinguishing it from the waiting time that follows, leading to more accurate bottleneck analysis.

Consult Finastra Fusion Mortgagebot documentation or system logs. This might be recorded as a 'Completion Date', 'End Date', or may need to be derived if not explicitly available.

This attribute identifies the specific branch or office responsible for the loan. It is a key organizational dimension for large institutions with multiple locations.

Used in the 'Loan Officer Workload Distribution' dashboard, this attribute allows for performance analysis to be aggregated at a location level. It helps compare the efficiency, workload, and outcomes between different branches, highlighting regional performance differences and informing resource allocation across the organization.

Enables performance benchmarking and workload analysis across different geographical or organizational units.

Consult Finastra Fusion Mortgagebot documentation. This is likely associated with the loan officer's user profile or selected during application intake.

This attribute contains the numerical credit score obtained from a credit bureau during the 'Credit Check Completed' activity. It is a key factor in assessing the applicant's creditworthiness.

In the 'Loan Decision Consistency by Risk' dashboard, the credit score is plotted against the final decision outcome. This analysis helps to ensure that lending decisions are fair, consistent, and aligned with the institution's risk appetite. It can highlight potential inconsistencies where applicants with similar scores receive different outcomes.

A critical data point for risk assessment, this attribute helps analyze the consistency and fairness of loan decisions.

Consult Finastra Fusion Mortgagebot documentation. This data is typically stored in a dedicated field after being retrieved from a credit reporting agency via an integration.

This boolean attribute distinguishes between tasks executed automatically by the system, like an initial credit check or generating a standard disclosure, and those performed manually by employees.

Analyzing this attribute helps in understanding the level of automation in the process. It can be used to compare the speed and consistency of automated versus manual steps. This is valuable for identifying further automation opportunities to improve efficiency and reduce the risk of human error.

Helps measure the degree of automation in the process and identify opportunities for further efficiency gains through new automation initiatives.

This is typically derived by checking the 'User' associated with an activity. If the user is a system or service account, the flag is set to true.

This boolean attribute is set to 'true' if a specific activity, like 'Supporting Documents Requested' or 'Underwriting Review Returned', occurs more than once for the same Loan Application ID. It helps to explicitly tag and quantify rework loops.

In the 'Rework and Redundant Steps Map' dashboard, this flag can be used to highlight rework loops visually or to filter for cases with high levels of rework. Calculating the Rework Activity Rate KPI becomes a simple matter of counting events where 'IsRework' is true. This helps focus improvement efforts on the root causes of repeated work, such as unclear communication or data entry errors.

Explicitly identifies and quantifies rework, making it easier to analyze the causes and impacts of process inefficiencies and loops.

This attribute is calculated in the data transformation layer by checking if the same activity name has already appeared for the given CaseId.

This boolean attribute is a result of comparing the actual duration of a stage with its target SLA. For example, it would be 'true' if the time between 'Underwriting Commenced' and 'Underwriting Completed' is greater than the 'Underwriting SLA Target'.

This flag simplifies analysis in the 'SLA Adherence and Breach Analysis' dashboard by creating a clear binary dimension for filtering and aggregation. It allows for quick calculation of the SLA Adherence Rate KPI and helps in drilling down to the specific characteristics, like Loan Type or Branch, of applications that frequently breach their service level agreements.

Provides a simple, clear indicator of SLA failure for any given case, making it easy to filter, aggregate, and analyze the root causes of breaches.

This attribute is calculated during data preparation. The logic is: (Actual Duration > UnderwritingSlaTarget) ? true : false.

This attribute represents the principal amount of the mortgage being applied for. It is a key financial metric for each application and often influences the process itself, for example, larger 'Jumbo' loans may require additional approval steps.

In analysis, Loan Amount can be used to segment the process and investigate correlations between the loan value and processing times, approval rates, or the level of scrutiny applied. For example, one might find that loans above a certain threshold consistently take longer in the underwriting stage. It provides valuable business context to the process flow.

Allows for financial analysis of the loan portfolio and helps investigate if the loan value impacts process behavior, such as cycle time or approval rates.

This is a core field entered during the application submission process and stored in the main loan application data within Finastra Fusion Mortgagebot.

This attribute categorizes the loan application based on the specific mortgage product being requested by the applicant. Different loan types often have distinct process requirements, compliance rules, and risk profiles.

Analyzing the process by Loan Type is a fundamental practice. It can reveal that certain products have significantly longer cycle times or higher rejection rates. This segmentation is crucial for identifying product-specific bottlenecks and for ensuring that compliance checks, like those in the 'Compliance Violation Pathways' analysis, are correctly applied.

Different loan products often have different process paths and SLAs, making this attribute essential for meaningful comparative analysis and filtering.

This is a fundamental data point selected at the beginning of the application process and stored in the main loan application data in Finastra Fusion Mortgagebot.

This metric represents the actual time an activity took to complete, calculated as the difference between the activity's EndTime and StartTime. It measures active work time, distinct from the idle or wait time between activities.

Processing time is a core metric for performance analysis and bottleneck identification. In the 'Underwriting Bottleneck Deep Dive', this metric for underwriting activities reveals how long underwriters are actively working on cases. Summing the processing times of all activities in a case gives the total touch time, a key measure of process efficiency.

Measures the actual work duration for activities, helping to distinguish value-added time from non-value-added wait time for precise efficiency analysis.

This attribute is not directly in the source system but is calculated during data preparation by subtracting the EventTimestamp from the EventEndTime.

When a loan's 'Decision Outcome' is 'Rejected', this attribute provides the specific cause. Reasons can range from poor credit history and insufficient income to incomplete documentation or property appraisal issues.

This is a vital attribute for the 'Loan Application Rejection Trends' dashboard. Analyzing the most frequent rejection reasons provides actionable insights for process improvement. For instance, a high number of rejections due to incomplete documentation might indicate a need to improve the document collection process or applicant communication.

Provides the root cause for failed processes, enabling targeted improvements to increase the approval rate and reduce wasted effort.

Consult Finastra Fusion Mortgagebot documentation. This is typically stored in a reason code or notes field associated with the final decision status.

The Risk Category is a classification assigned to a loan application based on various factors, including credit score, debt-to-income ratio, and loan-to-value ratio. It simplifies risk assessment into a few understandable tiers.

This attribute is essential for the 'Loan Decision Consistency by Risk' dashboard. By grouping loans into categories, it becomes easier to visualize whether decision outcomes are consistent for applications with similar risk profiles. This analysis supports regulatory compliance and helps in refining the internal risk assessment model.

Simplifies complex risk data, enabling high-level analysis of decision consistency and alignment with corporate risk strategy.

This may be a field calculated and stored by Finastra Fusion Mortgagebot's internal decision engine or a related risk management module.

This metric measures the idle time a loan application spends waiting after the credit check is done but before underwriting actively starts. It is calculated as the duration between the timestamp of 'Credit Check Completed' and 'Underwriting Commenced'.

This KPI is crucial for the 'Underwriting Bottleneck Deep Dive' as it isolates wait time from active processing time. High queue times can indicate resource shortages, inefficient work assignment, or system delays. Reducing this queue time is often a key goal for improving overall process speed and efficiency.

Isolates the waiting time before a critical process stage, providing a clear target for reducing idle time and improving flow efficiency.

This attribute is calculated during data analysis or ETL by finding the time difference between the 'Credit Check Completed' and 'Underwriting Commenced' events for each case.

This attribute specifies the Service Level Agreement (SLA) target for the duration of the underwriting process, often expressed in business hours or days. This target can vary based on factors like loan type or risk category.

This is a critical input for the 'SLA Adherence and Breach Analysis' dashboard and the Overall SLA Adherence Rate KPI. By comparing the actual underwriting processing time against this target, each case can be flagged as 'Met' or 'Breached'. This allows for monitoring performance against promises made to customers or internal goals, and for identifying the root causes of SLA failures.

Provides the benchmark against which actual performance is measured, enabling direct monitoring of SLA compliance and identification of breaches.

This may be stored as a static business rule, or as a field on the loan application itself, potentially derived by a rules engine within Finastra Fusion Mortgagebot.