Your Loan Origination Data Template

The Activity Name describes a distinct step or milestone within the loan origination journey, such as 'Application Submitted' or 'Credit Check Completed'. These activities form the backbone of the process map, showing the sequence of events for each loan application.

Analyzing these activities helps visualize the process flow, identify common pathways, discover deviations, and pinpoint bottlenecks. The sequence and frequency of activities are fundamental to understanding how the process actually operates compared to how it was designed.

It defines the steps in the process, allowing for the visualization of the process map and the analysis of process flow, bottlenecks, and deviations.

This information is typically derived from event logs, status change records, or audit trail tables within Temenos. It may require mapping from technical status codes or event types to user-friendly activity names.

The Event Timestamp records the precise moment an activity took place. This chronological data is critical for ordering events correctly and for all time-based process analysis.

This attribute enables the calculation of key performance indicators like cycle times, processing times, and waiting times between activities. It is used to identify delays, measure performance against service level agreements (SLAs), and understand the temporal dynamics of the loan origination process. Without accurate timestamps, process mining is not possible.

This timestamp is essential for sequencing events correctly and calculating all duration-based metrics, such as cycle times and bottlenecks.

Typically found alongside the activity or status field in event logs or audit trail tables within Temenos. Look for fields named 'TIMESTAMP', 'EVENT_DATE', or similar.

The Loan Application ID uniquely identifies each individual loan request throughout its entire lifecycle, from submission to final decision and funding. 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 attribute is essential for constructing the case view, where each Loan Application ID represents a single end-to-end process instance. Analyzing data through this identifier allows for the calculation of case-level metrics such as total cycle time, rework loops, and final outcomes, providing a comprehensive understanding of the loan origination flow.

This is the essential case identifier that connects all related events into a single end-to-end process, making process analysis possible.

This is typically the primary key in the main loan application or deal entity within Temenos. Consult Temenos documentation for the specific table and field name, such as those related to the AA.ARRANGEMENT module.

This metadata attribute records the date and time of the most recent data extraction or update from the source system. It does not represent a business event but rather the freshness of the data being analyzed.

This information is vital for understanding the timeliness of the process mining analysis and for data governance. It helps users know if they are looking at real-time, daily, or weekly data, which sets the context for their findings and decisions.

Indicates the freshness of the data, which is critical for data governance and for users to understand the timeliness of their analysis.

This is a metadata attribute added during the data extraction, transformation, and loading (ETL) process. It is usually set to the timestamp of the ETL run.

This attribute identifies the source information system where the activity data originated. In a complex IT landscape, loan origination events might be recorded in multiple systems, such as a front-end portal, the core banking platform, and a document management system.

Specifying the source system is important for data governance, troubleshooting, and understanding the technological touchpoints in the process. It helps validate data accuracy and can reveal process fragmentation across different platforms.

Identifies the origin of the data, which is crucial for data validation, troubleshooting, and understanding process integrations.

This is a metadata attribute that is typically added during the data extraction and transformation process. It can be a static value, like 'Temenos Transact', for all records from that system.

The Application Channel indicates the submission method used by the customer. Different channels can have different data quality, customer expectations, and processing requirements, impacting the overall process performance.

Analyzing the process by channel helps identify which channels are most efficient and which may require process improvements. For example, applications from the online portal might be faster on average than those submitted in a branch. This insight is valuable for optimizing channel strategy and resource allocation.

Enables performance analysis across different customer interaction channels, helping to optimize channel-specific processes and user experiences.

This information is typically captured at the start of the process and stored on the main loan application record. Look for a 'SOURCE' or 'CHANNEL' field within Temenos.

This attribute identifies the employee or team member who executed a particular task in the loan origination process. It is often referred to as the 'resource' in process mining.

Analyzing performance by loan officer helps in understanding workload distribution, identifying top performers, and uncovering opportunities for training or process standardization. It is fundamental for dashboards related to resource performance and workload management, allowing managers to optimize team efficiency and balance assignments.

Attributes user actions to specific individuals or teams, enabling workload analysis, performance comparison, and resource optimization.

This information is commonly found in audit trail tables, often linked to a user ID. Look for fields like 'USER_ID', 'PROCESSED_BY', or 'OWNER' in event or transaction records within Temenos.

The credit score is a numerical representation of an applicant's creditworthiness, obtained from a credit bureau. It is a key factor in the underwriting and decision-making process.

In process mining, the credit score is a critical contextual attribute. It helps analyze the consistency of loan decisions by correlating the score with the final outcome. It can also be used to segment applications to see if lower-scored applications take longer to process or require more manual intervention.

Provides critical context for decision-making, allowing analysis of how creditworthiness impacts process paths, durations, and outcomes.

This data is typically received from an external credit bureau service and stored in a customer or application-specific table within Temenos.

Decision Outcome captures the final status of a loan application after the underwriting and approval stages are complete. This is a critical outcome metric for the entire process.

This attribute is essential for analyzing the effectiveness of the process. It is used to calculate the approval and rejection rates, and when combined with other attributes like Credit Score or Applicant Type, it helps assess the consistency of lending decisions. Understanding why applications are rejected is key to process improvement.

Represents the final business outcome of the process, enabling analysis of approval rates, rejection reasons, and decision consistency.

Usually stored as a status field on the main loan application record in Temenos. This field is typically updated at the 'Loan Decision Rendered' activity.

The End Time marks the completion of a specific activity. While Start Time indicates when an event began, End Time is necessary to understand its duration. For instantaneous events, End Time can be the same as Start Time.

In process analysis, having both a start and end time is crucial for accurately calculating the processing time of activities. This helps differentiate between the time an application is actively being worked on (processing time) and the time it is waiting for the next step (wait time), which is key to identifying true efficiency bottlenecks.

Enables the precise calculation of activity processing time, which is essential for differentiating active work time from idle waiting time in bottleneck analysis.

This may be available in audit logs as a separate 'END_TIME' field or may need to be derived by using the Start Time of the subsequent activity in the sequence. Consult Temenos documentation for event logging details.

This attribute represents the principal amount of the loan being applied for. The loan amount can significantly influence the process path, level of scrutiny, and required approvals.

Analyzing the process based on loan amount allows for segmentation to understand if higher-value loans follow a different, more rigorous process. It can help explain variations in cycle time and underwriting effort. For instance, loans above a certain threshold may require additional approval steps, which can be visualized and validated with process mining.

Allows for value-based analysis to see how the loan amount affects process complexity, cycle time, and required approval levels.

This is a fundamental field on the loan application record in Temenos. Look for a field such as 'AMOUNT' or 'REQUESTED_AMOUNT'.

This attribute categorizes each loan application based on the financial product being requested. Different loan products often have distinct process flows, SLAs, and risk profiles.

Segmenting the process analysis by Loan Product Type is crucial for a meaningful comparison. It helps explain variations in cycle times, approval rates, and process paths. For example, a mortgage application process is inherently more complex and longer than a personal loan process. This attribute allows for apples-to-apples comparisons and targeted improvements.

Allows for process segmentation to compare performance and identify variations across different business lines, which often have unique process requirements.

This is a core attribute of the loan application, typically found in the main application table in Temenos. Look for fields related to 'PRODUCT_ID' or 'PRODUCT_CATEGORY'.

This attribute segments applicants into meaningful groups, such as 'New Customer', 'Existing Customer', or 'Business'. The process may differ based on the type of applicant; for example, processing for existing customers may be faster due to pre-existing data.

Analyzing the process by applicant type helps to understand how different customer segments experience the process. It can reveal opportunities to streamline the journey for existing customers or to provide more support for new ones. This segmentation is key for the 'Loan Decision Outcome Consistency' analysis.

Enables segmentation to analyze if the process differs for new versus existing customers, helping to tailor and improve the customer journey.

This information is typically derived by checking if the applicant has an existing customer profile or ID within Temenos at the time of application.

The Customer Region provides the geographic location of the applicant, such as 'North America', 'Europe', or a specific state. This allows for geographical segmentation of the process.

Analyzing performance by region can uncover regional variations in process efficiency, approval rates, or product popularity. This insight can be used for targeted marketing, resource allocation, and identifying regional best practices or challenges.

Enables geographical analysis to compare process performance across different regions, identify regional bottlenecks, and understand market differences.

This information is part of the customer's profile or address details stored in the Customer Information File (CIF) or equivalent customer master data within Temenos.

This attribute indicates the business unit or department, such as 'Origination', 'Underwriting', or 'Closing', that performed a given activity. It helps to understand the handoffs between different parts of the organization.

Analyzing the process by department is crucial for identifying cross-functional inefficiencies and delays during handoffs. It can highlight communication gaps or resource constraints within specific departments, providing a clear view of organizational bottlenecks.

Helps visualize the flow of work between different teams, making it possible to analyze handoff times and identify organizational bottlenecks.

This is often not a direct field in the event log but can be derived by mapping users (the 'AssignedLoanOfficer' attribute) to their respective departments using an HR master data source.

This boolean attribute distinguishes between activities executed by a human user and those performed by an automated system, such as an automated credit check or initial validation rule.

Understanding the level of automation is key to identifying opportunities for further efficiency gains. It allows for a comparison of the speed and consistency of automated steps versus manual ones. This analysis helps in prioritizing future automation initiatives and measuring their impact.

Distinguishes between system-driven and human-driven activities, which is crucial for identifying automation opportunities and measuring the impact of existing automation.

This is often derived based on the user associated with the activity. If the user is a system or service account, the activity is flagged as automated. It could also be based on the event type itself.

This boolean flag identifies activities that represent rework, meaning a step or a sequence of steps had to be repeated. For example, if 'Supporting Documents Requested' occurs after 'Underwriting Commenced', it indicates a loop back in the process.

Detecting and flagging rework is a core strength of process mining. This attribute allows for easy quantification of the 'Application Rework Rate' KPI. Analyzing the drivers of rework, such as incomplete initial submissions, is key to improving process efficiency, reducing costs, and shortening cycle times.

Highlights activities that are part of inefficient process loops, allowing for easy quantification and root cause analysis of rework.

This attribute is not in the source system. It is calculated by the process mining engine by detecting repeated sequences of activities within a single case.

This boolean attribute is a calculated flag that indicates whether the underwriting stage for a loan application has breached its Service Level Agreement (SLA). It is determined by comparing the actual duration of the underwriting process against the 'Underwriting SLA Target'.

This flag simplifies analysis and reporting by providing a clear, binary indicator of SLA compliance. It is used directly in dashboards and KPIs to track the SLA breach rate over time, by product, or by loan officer, helping to proactively manage performance and compliance risk.

Provides a simple yes/no indicator for SLA compliance, making it easy to filter, aggregate, and analyze the frequency and root causes of SLA breaches.

This attribute is not in the source system. It is calculated by measuring the duration between 'Underwriting Commenced' and 'Underwriting Completed' activities and comparing it to the 'UnderwritingSlaTarget' attribute.

Processing Time is a calculated metric that represents the active work time for an activity, computed as the difference between the End Time and Start Time. It contrasts with waiting time, which is the time spent between activities.

This metric is fundamental for bottleneck analysis. By separating processing time from waiting time, analysts can determine if delays are caused by inefficient activities (long processing time) or by queues and handoff delays (long waiting time). This is essential for targeting improvement efforts correctly.

Measures the active work duration of an activity, helping to distinguish between process inefficiencies and waiting time in bottleneck analysis.

This attribute is not present in the source system. It is calculated during data transformation by subtracting the 'EventTimestamp' (StartTime) from the 'EndTime' for each activity.

This attribute provides context for the 'Decision Outcome'. For rejected applications, it specifies the underlying reason, such as 'Insufficient Income', 'High Debt-to-Income Ratio', or 'Poor Credit History'.

This information is invaluable for root cause analysis of rejections. By analyzing the most common rejection reasons, the organization can identify issues in the pre-qualification stage, improve customer communication, or adjust lending criteria. It directly supports efforts to reduce rework and improve the overall quality of applications entering the pipeline.

Provides critical context for rejected applications, enabling root cause analysis to improve application quality and reduce unnecessary processing.

Often stored in a related notes or reason code field linked to the final decision status in Temenos.

The Underwriting SLA Target defines the expected service level agreement for the underwriting stage, typically measured in business hours or days. This target can vary based on factors like the loan product type or amount.

This attribute serves as the benchmark against which actual performance is measured. It is used directly in the 'Underwriting SLA & Compliance Status' dashboard and is required to calculate the 'SLA Breach Rate' KPI. Analyzing breaches helps identify the causes of delays and manage operational and compliance risks.

Provides a clear benchmark for performance, enabling the measurement of SLA adherence and the identification of applications at risk of breaching their targets.

This may be stored as a static value based on business rules or could be a field on the loan application, potentially derived from product master data in Temenos.